Dissertations / Theses on the topic 'Multi-Objective Planning'
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Dasgupta, Sumantra. "Multi-objective stochastic path planning." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2755.
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Mohamed, Radzi Nor Haizan. "Multi-objective planning using linear programming." Thesis, University of Strathclyde, 2010. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=15344.
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Oral, Tugcem. "Multi-objective Path Planning For Virtual Environments." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614643/index.pdf.
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Path planning is a crucial issue for virtual environments where autonomous agents try to navigate from a specific location to a desired one. There are several algorithms developed for path planning, but several domain requirements make engineering of these algorithms difficult. In complex environments, considering single objective for searching and finding optimal or sub-optimal paths becomes insufficient. Thus, multi objective cases are distinguished and more complicated algorithms to be employed is required. It can be seen that more realistic and robust results can be obtained with these algorithms because they expand solution perspective into more than one criteria. Today, they are used in various games and simulation applications.
On the other hand, most of these algorithms are off-line and delimitate interactive behaviours and dynamics of real world into a stationary virtuality. This situation reduces the solution quality and boundaries. Hence, the necessity of solutions where multi objectivity is considered in a dynamic environment is obvious. With this motivation, in this work, a novel multi objective incremental algorithm, MOD* Lite, is proposed. It is based on a known complete incremental search algorithm, D* Lite. Solution quality and execution time requirements of MOD* Lite are compared with existing complete multi objective off-line search algorithm, MOA*, and better results are obtained.
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Sroka, Michal. "Multi-objective planning using a metric sensitive planner." Thesis, King's College London (University of London), 2015. https://kclpure.kcl.ac.uk/portal/en/theses/multiobjective-planning-using-a-metric-sensitive-planner(8874baf3-9d09-468e-9b5b-bdf25b996f24).html.
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Automated planning addresses the problem of generating a sequence of actions to satisfy given goal conditions for a constructed model of the world. In recent planning approaches heuristic guidance is used to lead the search towards the goal. The focus of this work is on domains where plan quality is assessed with plan metrics. A discussion of the impact of a popular relaxed planning graph heuristic on the quality of plans in such domains is presented. The relaxed planning graph heuristic bias towards shorter plans, irrespective of quality, is described. A novel approach to constructing the relaxed planning graph based on metric cost is presented to overcome this bias and to generate good quality plans. A notion of metric sensitivity as the ability of a planner to respond to the change of the plan metric, is introduced and methods to determine metric sensitivity are presented. Current state-of-the-art planners are evaluated in terms of their metric sensitivity. This research also tackles the problem of planning in multiobjective domains, where quality of a plan is evaluated using multiple plan metrics. For multiobjective domains the solution is no longer a single plan but a set of plans. A set of non dominated solutions is called a pareto frontier. This thesis contains a discussion on the desired properties of such sets of plans and methods of generating them. Metric sensitivity is a required property for a planner to effectively reason with user defined metrics and generate desired set of plans. The main significant contributions of the work described in the thesis are: 1. A definition and exploration of metric sensitivity in planning. 2. A context-dependent, cost-based relaxed planning graph and heuristic. 3. A compilation method from cost to temporal domains. 4. Examination of the impact of planners’ properties on the quality of plans and APFs.
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Tuyiragize, Richard. "Multi-objective optimization techniques in electricity generation planning." Doctoral thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/10720.
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The objective of this research is to develop a framework of multi-objective optimization (MOO) models that are better capable of providing decision support on future long-term electricity generation planning (EGP), in the context of insufficient electricity capacity and to apply it to the electricity system for a developing country. The problem that motivated this study was a lack of EGP models in developing countries to keep pace with the countries' socio-economic and demographic dynamics. This research focused on two approaches: mathematical programming (MP) and system dynamics (SD). Detailed model descriptions, formulations, and implementation results are presented in the thesis along with the observations and insights obtained during the course of this research.
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Limaye, Ameya Shankar. "Multi-objective process planning method for Mask Projection Stereolithography." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19717.
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Mask Projection Stereolithography (MPSLA) is a high resolution manufacturing process that builds parts layer by layer in a photopolymer. In this research, a process planning method to fabricate MPSLA parts with constraints on dimensions, surface finish and build time is formulated.
As a part of this dissertation, a MPSLA system is designed and assembled. The irradiance incident on the resin surface when a given bitmap is imaged onto it is modeled as the Irradiance model . This model is used to formulate the Bitmap generation method which generates the bitmap to be imaged onto the resin in order to cure the required layer.
Print-through errors occur in multi-layered builds because of radiation penetrating beyond the intended thickness of a layer, causing unwanted curing. In this research, the print through errors are modeled in terms of the process parameters used to build a multi layered part. To this effect, the Transient layer cure model is formulated, that models the curing of a layer as a transient phenomenon, in which, the rate of radiation attenuation changes continuously during exposure. In addition, the effect of diffusion of radicals and oxygen on the cure depth when discrete exposure doses, as opposed to a single continuous exposure dose, are used to cure layers is quantified. The print through model is used to formulate a process planning method to cure multi-layered parts with accurate vertical dimensions. This method is demonstrated by building a test part on the MPSLA system realized as a part of this research.
A method to improve the surface finish of down facing surfaces by modulating the exposure supplied at the edges of layers cured is formulated and demonstrated on a test part.
The models formulated and validated in this dissertation are used to formulate a process planning method to build MPSLA parts with constraints on dimensions, surface finish and build time. The process planning method is demonstrated by means of a case study.
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Nordström, Peter. "Multi-objective optimization and Pareto navigation for voyage planning." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-220338.
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The shipping industry is very large and ships require a substantial amount of fuel. However, fuel consumption is not the only concern. Time of arrival, safety concerns, distance travelled etc. are also of importance and these objectives might be inherently conflicting. This thesis aims to demonstrate multi-objective optimization and Pareto navigation for application in voyage planning. In order to perform this optimization, models of weather, ocean conditions, ship dynamics and propulsion system are needed. Statistical methods for estimation of resistance experienced in calm and rough sea are used. An earlier developed framework is adopted to perform the optimization and Pareto navigation. The results show that it is a suitable approach in voyage planning. A strength of the interactive Pareto navigation is the overview of the solution space presented to the decision maker and the control of the spread of the objective space. Another benefit is the possibilities of assigning specific values on objectives and setting thresholds in order to narrow down the solution space. The numerical results reinforces the trend of slow steaming to decrease fuel consumption.
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Matthews, Keith B. "Applying genetic algorithms to multi-objective land-use planning." Thesis, Robert Gordon University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.344018.
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El-Sayed, Jacqueline Johnson. "Multi-objective optimization of manufacturing processes design /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841282.
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Menon, U. "A multi-objective production planning framework for automated manufacturing systems." Thesis, University of Nottingham, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356077.
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Yadav, Jagdish Prasad. "Participatory multi-objective planning for the management of natural resources." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/11630.
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The research study provides a participatory methodology appropriate for the management of natural resources. The major natural resources, i.e., agriculture, forest and community lands are considered. A large proportion of these resources is degraded or is in various stages of degradation. The past and the present management practices have been inadequate to maintain these resources in proper state for sustainable use. In this study, a planning process for the management of these natural resources is demonstrated by selecting a typical site which consists of six villages, with their resources, at Sohna in Haryana State of India. It involves different participants, namely the local people, Village Panchayats (village level elected administrative bodies) and the government agencies which are responsible singly or jointly for the management. In the planning, the natural resources and the goods and services derived from them along with people, livestock and their activities are viewed as an interactive and inter-dependent, 'whole system'. A systems approach has been used, beginning with detailed analysis of socio-economic and bio-physical major sub-systems of the selected site, which is followed by integration of different components of the sub-systems to achieve the specified objectives (environmental amelioration and social welfare) and goals (demand of food, fodder, timber and other minor products, employment opportunities and maximisation of income) through the use of mathematical programming especially goal and linear programming. Two alternative management scenarios - village level and community level - are presented and discussed. The salient features of this study are integration of agriculture resource with the management of common and forest lands, hitherto all of them managed singly, holistic view of management and the participation of stakeholders in the management process.
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Rögnvaldsson, Kristján Óttar. "Multi-Objective Mixed-Integer Linear Optimisation of Aircraft Load Planning." Thesis, KTH, Optimeringslära och systemteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253363.
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A general multi-objective optimisation model is developed for the load planning decision process of a bulk loaded commercial aircraft, using the Airbus A321 fitted with additional fuel tanks as a baseline platform. The model’s input is a specific set of load items, with associated quantities, mass and volume. The output is a load plan, stating where each item should be loaded and in what quantity. The load plans should be optimal with respect to a target centre of gravity range and handling efficiency. Furthermore, the solutions should be robust with respect to perturbations in the input data. Three objective functions and a set of constraints are defined to achieve this task. A constraint that ensures the ground stability of the aircraft is developed and analysed. A lexicographic approach is used solve the multi-objective problem, by sequentially solving a set of mixed-integer linear programs. The sequence is determined from a priority ranking of the objectives. Testing is carried out with data from an operator of the A321, with four different test cases. Test results indicate that the model is capable of solving the load planning problem for the baseline aircraft. The centre of gravity values are within the optimal range, and the load distributions are efficient. Additional margins on aircraft limits assist with maintaining feasibility in case of input perturbation. The model is also robust with respect to the highly variable test data. The main causes of infeasibility are mixing constraints and additional balance envelope margins. The ground stability constraint does not cause any significant amount of infeasibilities, and primarily increases the safety level of the load plans. A strength of the model is its relatively simple handling of the multiple objectives, and the small number of tunable parameters also makes the model controllable. A trained agent in the industry is able to understand and control the model without an extensive technical background. The test process used differs slightly from the actual industry load planning process. As a result, testing only allows for evaluation of the model’s ability to solve the load planning problem, and gives no justification for implementation in real-world operations. Such an evaluation requires a prototype to be tested in an operational environment using the actual process. As testing was only done for the baseline aircraft, with one set of test data and model parameters, a justifiable conclusion cannot be reached on the model’s applicability to other bulk loaded aircraft. Therefore, it is recommended to carry out further testing on different aircraft as the next step in them model’s evaluation. ivEn allmänn flermåls optimeringsmodell utvecklas för besluttsprocessen relaterat till lastning av ett kommersiellt flygplan, som använder Airbus A321 utrustad med ytterligare bränslestankar som en bas. Modellens indata är ett specifikt set av artiklar som ska lastas tillsammans med information om mängd, tyngd och volym. Utvärdet är en beskrivande plan som visar var varje artikel ska lastas och i vilken mängd. Planen ska vara optimal med hänsyn till ett specifikt tyngdpunkts intervall och vara effectiv för lastningsoperationer. Dessutom ska den vara robust med hänsyn till störningar i indatan. Tre målfunktioner tillsammans med ett set av begränsningar används för att lösa problemet. En specifik begränsning som säkrar flygplanets stabilitet på grunden utvecklas och dess känslighet analyseras. En lexikografisk metod används för att lösa flermåls problemet, där lösar en sekvens av blandade heltalsprogrammer. Sekvensen är definierad ut från en prioritetsordning av de olika målfunktioner. Testning av modellen är utförd med indata från en operatör av A321 basflygplanet med fyra olika testfall. Testresultaten visar att modellen kan användas för att lösa lastningsproblemet för basflygplanet. Tyngdpunktsvärden är inom det optimal intervall och fördelningen av artiklar är effektiv. Extra marginaler på flygplansbegränsningar hjälper med att säkra lösningen under störningar på indatan. Modellen är också robust med hänsyn till högvarierad indata. Huvudorsaker till omöjliga testfall, de utan lösningar, är begränsningar på blandning av artiklar samt extra marginaler på flygplansbegränsningar. Begränsningen för grund stabilitet är inte en orsak till omöjlighet, och ökar primärt säkerhetsnivån på lösningen. En styrka till modellen är dess enkel hantering av de olika målfunktioner och de få parametrar gör modellen kontrollbar. En utbildad agent från industrin kan förstå och kontrollera modellen, utan att ha en teknisk bakgrund. Testprocessen som används representerar inte exakt industriprocessen. Testprocessen kan därför bara användas till att utvärdera modellens förmåga till att lösa lastningsproblemet, och ger ingen motivering på bruk i verkliga operationer. En utvärdering på den förmåga krävs en utveckling av en prototyp i verkliga världen. Testning av bara en typ av basflygplan, tillsammans med ett set av indata och modellparametrar, ger inte en grund till en konklusion på modellens tillämplighet för andra flygplan. Därför rekommenderas det att utföra ytterligare testning på andra flygplan som nästa steg i modellens utvärdering.
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Wu, Paul Pao-Yen. "Multi-objective mission flight planning in civil unmanned aerial systems." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/31322/1/Paul_Wu_Thesis.pdf.
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Unmanned Aerial Vehicles (UAVs) are emerging as an ideal platform for a wide range of civil applications such as disaster monitoring, atmospheric observation and outback delivery. However, the operation of UAVs is currently restricted to specially segregated regions of airspace outside of the National Airspace System (NAS). Mission Flight Planning (MFP) is an integral part of UAV operation that addresses some of the requirements (such as safety and the rules of the air) of integrating UAVs in the NAS. Automated MFP is a key enabler for a number of UAV operating scenarios as it aids in increasing the level of onboard autonomy. For example, onboard MFP is required to ensure continued conformance with the NAS integration requirements when there is an outage in the communications link. MFP is a motion planning task concerned with finding a path between a designated start waypoint and goal waypoint. This path is described with a sequence of 4 Dimensional (4D) waypoints (three spatial and one time dimension) or equivalently with a sequence of trajectory segments (or tracks). It is necessary to consider the time dimension as the UAV operates in a dynamic environment. Existing methods for generic motion planning, UAV motion planning and general vehicle motion planning cannot adequately address the requirements of MFP. The flight plan needs to optimise for multiple decision objectives including mission safety objectives, the rules of the air and mission efficiency objectives. Online (in-flight) replanning capability is needed as the UAV operates in a large, dynamic and uncertain outdoor environment. This thesis derives a multi-objective 4D search algorithm entitled Multi- Step A* (MSA*) based on the seminal A* search algorithm. MSA* is proven to find the optimal (least cost) path given a variable successor operator (which enables arbitrary track angle and track velocity resolution). Furthermore, it is shown to be of comparable complexity to multi-objective, vector neighbourhood based A* (Vector A*, an extension of A*). A variable successor operator enables the imposition of a multi-resolution lattice structure on the search space (which results in fewer search nodes). Unlike cell decomposition based methods, soundness is guaranteed with multi-resolution MSA*. MSA* is demonstrated through Monte Carlo simulations to be computationally efficient. It is shown that multi-resolution, lattice based MSA* finds paths of equivalent cost (less than 0.5% difference) to Vector A* (the benchmark) in a third of the computation time (on average). This is the first contribution of the research. The second contribution is the discovery of the additive consistency property for planning with multiple decision objectives. Additive consistency ensures that the planner is not biased (which results in a suboptimal path) by ensuring that the cost of traversing a track using one step equals that of traversing the same track using multiple steps. MSA* mitigates uncertainty through online replanning, Multi-Criteria Decision Making (MCDM) and tolerance. Each trajectory segment is modeled with a cell sequence that completely encloses the trajectory segment. The tolerance, measured as the minimum distance between the track and cell boundaries, is the third major contribution. Even though MSA* is demonstrated for UAV MFP, it is extensible to other 4D vehicle motion planning applications. Finally, the research proposes a self-scheduling replanning architecture for MFP. This architecture replicates the decision strategies of human experts to meet the time constraints of online replanning. Based on a feedback loop, the proposed architecture switches between fast, near-optimal planning and optimal planning to minimise the need for hold manoeuvres. The derived MFP framework is original and shown, through extensive verification and validation, to satisfy the requirements of UAV MFP. As MFP is an enabling factor for operation of UAVs in the NAS, the presented work is both original and significant.
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Raisanen, Larry. "Multi-objective site selection and analysis for GSM cellular network planning." Thesis, Cardiff University, 2005. http://orca.cf.ac.uk/56041/.
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Although considerable effort has been placed on developing techniques and algo rithms to create feasible cell plans, much less effort has been placed on understanding the relationship between variables and objectives. The purpose of this thesis is to improve the body of knowledge aimed at understanding the trade-offs and tensions in the selection of transmission sites and in the configuration of macro-cells for GSM and related FDMA wireless systems. The work begins by using an abstract 2-dimensional (2D) model for area coverage. A multiple objective optimisation framework is de veloped to optimise the sequential placement and configuration of downlink wireless cells. This is deployed using a range of evolutionary algorithms whose performance is compared. The framework is further tuned via a decoding mechanisms using the best performing evolutionary algorithm. The relationship between primary variables in the 2D model is analysed in detail. To improve realism, the thesis additionally addresses complexities relating to planning in 3-dimensional (3D) environments. A detailed open source static model is developed and the optimisation framework is extended to accommodate the additional model complexities and choices in algorithm design are compared. Finally, sensitivity analysis is performed to determine the relationship between objectives in the 3D model and benchmark solutions are provided.
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Fejes, Niklas. "High Performance Multi-Objective Voyage Planning Using Local Gradient-Free Methods." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-305582.
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A number of parallel gradient-free local optimization methods are investigated in application to problems of voyage planning for maritime ships. Two optimization algorithms are investigated, a parallel version of the Nelder-Mead Simplex method and the Subplex method with Nelder-Mead Simplex as its inner solver. Additionally, two new formulations of the optimization problem are suggested which together with an improved implementation of the objective function increases the overall performance of the model. Numerical results show the efficiency of these methods in comparison with the earlier introduced Grid search method and solvers from an open-source optimization library.
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Muteba, Kande Joel. "Multi-Objective Heterogeneous Multi-Asset Collection Scheduling Optimization with High-Level Information Fusion." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42557.
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Surveillance of areas of interest through image acquisition is becoming increasingly essential for intelligence services. Several types of platforms equipped with sensors are used to collect good quality images of the areas to be monitored. The evolution of this field has different levels: some studies are only based on improving the quality of the images acquired through sensors, others on the efficiency of platforms such as satellites, aircraft and vessels which will navigate the areas of interest and yet others are based on the optimization of the trajectory of these platforms. Apart from these, intelligence organizations demonstrate an interest in carrying out such missions by sharing their resources. This thesis presents a framework whose main objective is to allow intelligence organizations to carry out their observation missions by pooling their platforms with other organizations having similar or geographically close targets. This framework will use Multi-Objective Optimization algorithms based on genetic algorithms to optimize such mission planning. Research on sensor fusion will be a key point to this thesis, researchers have proven that an image resulting from the fusion of two images from different sensors can provide more information compared to the original images. Given that the main goal for observation missions is to collect quality imagery, this work will also use High-Level Information Fusion to optimize mission planning based on image quality and fusion. The results of the experiments not only demonstrate the added value of this framework but also highlight its strengths (through performance metrics) as compared to other similar frameworks.
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Haidine, Abdelfatteh. "Multi-objective combinatorial optimization in topology planning of wireline broadband access networks." Köln WiKu-Verl, 2008. http://d-nb.info/99229200X/04.
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Gunna, Vivek Reddy. "ADAPTIVE MULTI-OBJECTIVE OPERATING ROOM PLANNING WITH STOCHASTIC DEMAND AND CASE TIMES." UKnowledge, 2017. https://uknowledge.uky.edu/me_etds/108.
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The operating room (OR) is accountable for most hospital admissions and is one of the most cost and work intensive areas in the hospital. From recent trends, we discover an unexpected parallel increase in expenditure and waiting time. Therefore, improving OR planning has become obligatory, particularly regarding utilization, and service level. Significant challenges in OR planning are the high variations in demand, processing times of surgical specialties, the trade-off between the objectives, and control of OR performance in long-term. Our model provides OR configurations at a strategical level of OR planning to minimize the tradeoff between the utilization and service level accounting for variation in both demand and processing times of surgical specialties. An adaptive control scheme is proposed to aid OR managers to maintain the OR performance within the prescribed controllable limits. Our model is validated using a simulation of demand and processing time data of surgical services at University of Kentucky Health Care.
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Bonnet, Jonathan. "Multi-criteria and multi-objective dynamic planning by self-adaptive multi-agent system, application to earth observation satellite constellations." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30058/document.
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Etablir le meilleur plan pour l'usinage d'un produit, le meilleur ordonnancement des activités de construction d'un bâtiment ou la meilleure tournée de véhicules pour la livraison des commandes, en prenant en compte diverses contraintes économiques, temporelles, humaines, ou même météorologiques : dans cette diversité d'applications, optimiser la planification est une tâche complexe par le grand nombre d'entités hétérogènes en interaction, la forte dynamique, les objectifs contradictoires à atteindre, etc. La planification de missions pour des constellations de satellites en est un exemple majeur : beaucoup de paramètres et de contraintes, souvent antagonistes, doivent être pris en compte, entraînant une importante combinatoire. Actuellement, en Europe, les plans de missions sont élaborés au sol, juste avant que le satellite ne soit visible par la station d'émission. Les requêtes arrivant durant la planification ne peuvent être traitées, et sont mises en attente. De plus, la complexité de ce problème croit drastiquement : le nombre de constellations et les satellites les composant augmentent, ainsi que le nombre de requêtes journalières. Les approches actuelles montrent leurs limites. Pour pallier à ces inconvénients, de nouveaux systèmes basés sur la décentralisation et la distribution inhérentes à ce genre de problèmes, sont nécessaires. La théorie des systèmes multi-agents adaptatifs (AMAS) et notamment le modèle AMAS4Opt (AMAS for Optimisation) ont montré leur adéquation pour la résolution de problèmes d'optimisation complexes sous contraintes. Le comportement local et coopératif des agents AMAS permet au système de s'auto-adapter à la forte dynamique et de fournir des solutions adéquates rapidement. Dans cette thèse, nous adressons la résolution de la planification des missions de satellites par AMAS. Pour cela, nous avons complété et enrichi les modèles d'agents proposés par AMAS4Opt. Nous avons ainsi développé le système de planification dynamique de missions ATLAS. Pour valider ATLAS sur divers critères, nous avons utilisé un grand nombre de données hétérogènes. Enfin, ce travail a été comparé à un système " opérationnel' " standard sur des scénarios réels, mettant en valeur les apports de notre systèmeBuilding the best plan in product treatment, the best schedule to a building construction or the best route for a salesman in order to visit a maximum of cities in the time allowed while taking into account different constraints (economic, temporal, humans or meteorological ): in all of those variety of applications, optimizing the planning is a complex task including a huge number of heterogeneous entities in interaction, the strong dynamics, multiple contradictory objectives, etc. Mission planning for constellations of satellites is a major example: a lot of parameters and constraints, often antagonists must be integrated, leading to an important combinatorial search space. Currently, in Europe, plans are built on ground, just before the satellite is visible by the ground stations. Any request coming during the planning process must wait for the next period. Moreover, the complexity of this problem grows drastically: the number of constellations and satellites increases, as the number of daily requests. Current approaches have shown their limits. To overcome those drawbacks, new systems based on decentralization and distribution inherent to this problem, are needed. The adaptive multi-agent systems (AMAS) theory and especially the AMAS4Opt (AMAS For Optimization) model have shown their adequacy in complex optimization problems solving. The local and cooperative behavior of agents allows the system to self-adapt to highly dynamic environments and to quickly deliver adequate solutions. In this thesis, we focus on solving mission planning for satellite constellations using AMAS. Thus, we propose several enhancement for the agent models proposed by AMAS4Opt. Then, we design the ATLAS dynamic mission planning system. To validate ATLAS on several criteria, we rely on huge sets of heterogeneous data. Finally, this work is compared to an operational and standard system on real scenarios, highlighting the value of our system
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Alarcón-Rodríguez, Arturo D. "A multi-objective planning framework for analysing the integration of distributed energy resources." Thesis, University of Strathclyde, 2009. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21779.
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The electricity industry faces the challenge of adapting to new circumstances where environmental concerns and the optimal use of resources are crucial. In this scenario, Distributed Energy Resources (DER) are recognised as one of the possible solutions for sustainable economic development. The optimal integration of DER in the distribution networks is essential to maximise DER benefits and minimise the cost of DER integration. An adequate DER planning method is required to obtain valuable information for the best deployment of these resources. The integration of DER has several drivers, such as the minimisation of cost, the reduction of carbon emission and the reduction of energy losses, among others. At the same time, several stakeholders are involved in DER research, development and management. Consequently, a flexible and multi-objective planning method that considers technical, environmental and economic impacts of DER integration can provide a deep insight into the advantages and drawbacks of DER, and can reflect the different perspectives on the problem. Most renewable DER have a variable output. Hence, the planning of DER integration must consider the stochastic nature of DER Likewise, the active management of DER and the network has been recognised recently as one of the new paradigms for the integration of larger penetrations of DER As a result, an appropriate planning technique for DER integration must consider the simultaneous interaction of controllable and stochastic DER to provide an adequate evaluation of DER impacts and benefits. Novel multi-objective optimisation techniques, known as Multi-objective Evolutionary Algorithms (MOEA), have been developed recently. MOEA are able to analyse complex objective functions and offer a "true" multi-objective approach. Consequently, MOEA are able to handle complex multi-objective problems such as DER planning effectively. This thesis proposes to use multi-objective planning to analyse the optima This thesis proposes to use multi-objective planning to analyse the optimal integration of stochastic and controllable DER It presents the design, development and demonstration of a planning framework based on a state-of-the-art MOEA. Results from two relevant case studies show that the multi-objective planning method proposed is a novel and valuable tool for the analysis of DER integration. The framework proposed is generic and can be applied to other energy planning problems.
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Shaikh, Meher Talat. "Multi-objective Intent-based Path Planning for Robots for Static and Dynamic Environments." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8510.
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This dissertation models human intent for a robot navigation task, managed by a human and undertaken by a robot in a dynamic, multi-objective environment. Intent is expressed by a human through a user interface and then translated into a robot trajectory that satisfies a set of human-specified objectives and constraints. For a goal-based robot navigation task in a dynamic environment, intent includes expectations about a path in terms of objectives and constraints to be met. If the planned path drifts from the human's intent as the environment changes, a new path needs to be planned. The intent framework has four elements: (a) a mathematical representation of human intent within a multi-objective optimization problem; (b) design of an interactive graphical user interface that enables a human to communicate intent to the robot and then to subsequently monitor intent execution; (c) integration and adoption of a fast online path-planning algorithms that generate solutions/trajectories conforming to the given intent; and (d) design of metric-based triggers that provide a human the opportunity to correct or adapt a planned path to keep it aligned with intent as the environment changes. Key contributions of the dissertation are: (i) design and evaluation of different user interfaces to express intent, (ii) use of two different metrics, cosine similarity and intent threshold margin, that help quantify intent, and (iii) application of the metrics in path (re)planning to detect intent mismatches for a robot navigating in a dynamic environment. A set of user studies including both controlled laboratory experiments and Amazon Mechanical Turk studies were conducted to evaluate each of these dissertation components.
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Tangpattanakul, Panwadee. "Multi-objective optimization of earth observing satellite missions." Thesis, Toulouse, INSA, 2013. http://www.theses.fr/2013ISAT0023/document.
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Cette thèse considère le problème de sélection et d’ordonnancement des prises de vue d’un satellite agile d’observation de la Terre. La mission d’un satellite d’observation est d’obtenir des photographies de la surface de la Terre afin de satisfaire des requêtes d’utilisateurs. Les demandes, émanant de différents utilisateurs, doivent faire l’objet d’un traitement avant transmission d’un ordre vers le satellite, correspondant à une séquence d’acquisitions sélectionnées. Cette séquence doit optimiser deux objectifs sous contraintes d’exploitation. Le premier objectif est de maximiser le profit global des acquisitions sélectionnées. Le second est d’assurer l’équité du partage des ressources en minimisant la différence maximale de profit entre les utilisateurs. Deux métaheuristiques, composées d’un algorithme génétique à clé aléatoire biaisées (biased random key genetic algorithm - BRKGA) et d’une recherche locale multi-objectif basée sur des indicateurs (indicator based multi-objective local search - IBMOLS), sont proposées pour résoudre le problème. Pour BRKGA, trois méthodes de sélection, empruntées à NSGA-II, SMS-EMOA, et IBEA, sont proposées pour choisir un ensemble de chromosomes préférés comme ensemble élite. Trois stratégies de décodage, parmi lesquelles deux sont des décodages uniques et la dernière un décodage hybride, sont appliquées pour décoder les chromosomes afin d’obtenir des solutions. Pour IBMOLS, plusieurs méthodes pour générer la population initiale sont testées et une structure de voisinage est également proposée. Des expériences sont menées sur des cas réalistes, issus d’instances modifiées du challenge ROADEF 2003. On obtient ainsi les fronts de Pareto approximés de BRKGA et IBMOLS dont on calcule les hypervolumes. Les résultats de ces deux algorithmes sont comparésThis thesis considers the selection and scheduling problem of observations for agile Earth observing satellites. The mission of Earth observing satellites is to obtain photographs of the Earth surface to satisfy user requirements. Requests from several users have to be managed before transmitting an order, which is a sequence of selected acquisitions, to the satellite. The obtained sequence must optimize two objectives under operation constraints. The first objective is to maximize the total profit of the selected acquisitions. The second one is to ensure the fairness of resource sharing by minimizing the maximum profit difference between users. Two metaheuristic algorithms, consisting of a biased random key genetic algorithm (BRKGA) and an indicator-based multi-objective local search (IBMOLS), are proposed to solve the problem. For BRKGA, three selection methods, borrowed from NSGA-II, SMS-EMOA, and IBEA, are proposed to select a set of preferred chromosomes to be the elite set. Three decoding strategies, which are two single decoding and a hybrid decoding, are applied to decode chromosomes to become solutions. For IBMOLS, several methods for generating the initial population are tested and the neighborhood structure according to the problem is also proposed. Experiments are conducted on realistic instances based on ROADEF 2003 challenge instances. Hypervolumes of the approximate Pareto fronts are computed and the results from the two algorithms are compared
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Mohammadi, Mehrdad. "A multi-objective optimization framework for an inspection planning problem under uncertainty and breakdown." Thesis, Paris, ENSAM, 2015. http://www.theses.fr/2015ENAM0055/document.
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Dans les systèmes manufacturiers de plus en plus complexes, les variations du processus de fabrication et de ses paramètres opératoires ainsi que leurs effets sur l’ensemble du système doivent être maîtrisés, mesurés et contrôlés. Cette thèse propose un cadre d’optimisation pour l’élaboration d’un plan d’inspection optimal qui permet une prise de décision opérationnelle afin d’assurer la satisfaction des objectifs stratégiques (réduction des coûts, amélioration de la qualité, augmentation de la productivité, …). La prise de décision se divise en trois questions : Quoi contrôler ? Comment contrôler ? Quand contrôler ? Le manque d'informations fiables sur les processus de production et plusieurs facteurs environnementaux est devenu un problème important qui impose la prise en compte de certaines incertitudes lors de la planification des inspections. Cette thèse propose plusieurs formulations du problème d’optimisation de la planification du processus d'inspection, dans lesquelles, les paramètres sont incertains et les machines de production sont sujettes aux défaillances. Ce problème est formulé par des modèles de programmation mathématique avec les objectifs : minimiser le coût total de fabrication, maximiser la satisfaction du client, et minimiser le temps de la production totale. En outre, les méthodes Taguchi et Monte Carlo sont appliquées pour faire face aux incertitudes. En raison de la complexité des modèles proposés, les algorithmes de méta-heuristiques sont utilisés pour trouver les solutions optimalesQuality inspection in multistage production systems (MPSs) has become an issue and this is because the MPS presents various possibilities for inspection. The problem of finding the best inspection plan is an “inspection planning problem”. The main simultaneous decisions in an inspection planning problem in a MPS are: 1) which quality characteristics need to be inspected, 2) what type of inspection should be performed for the selected quality characteristics, 3) where these inspections should be performed, and 4) how the inspections should be performed. In addition, lack of information about production processes and several environmental factors has become an important issue that imposes a degree of uncertainty to the inspection planning problem. This research provides an optimization framework to plan an inspection process in a MPS, wherein, input parameters are uncertain and inspection tools and production machines are subject to breakdown. This problem is formulated through several mixed-integer mathematical programming models with the objectives of minimizing total manufacturing cost, maximizing customer satisfaction, and minimizing total production time. Furthermore, Taguchi and Monte Carlo methods are applied to cope with the uncertainties. Due to the complexity of the proposed models, meta-heuristic algorithms are employed to find optimal or near-optimal solutions. Finally, this research implements the findings and methods of the inspection planning problem in another application as hub location problem. General and detail concluding remarks are provided for both inspection and hub location problems
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Castro, Amulfo de. "A technique for multi-attribute utility expansion planning under uncertainty : with focus on incorporating environmental factors into the planning process /." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06062008-162223/.
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Ramadan, Mohamed Wahba Mohamed Hassan. "Multi-objective approach for university staff planning (an empirical study of an Egyptian private university)." Thesis, University of the West of England, Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429691.
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Anagnostakis, Ioannis. "A multi-objective, decomposition-based algorithm design methodology and its application to runaway operations planning." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28913.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.Includes bibliographical references (p. 283-296).
(cont.) to the design of a heuristic decomposed algorithm for solving the ROP problem. This decomposition methodology offers an original paradigm potentially applicable to the design of solution algorithms for a class of problems with functions and parameters that, similar to those of the ROP problem, can be parsed in subsets. The potential merit in decomposing the ROP problem in two stages and the resulting utility of the two-stage solution algorithm are evaluated by performing benefits analysis across specific dimensions related to airport efficiency, as well as stability and robustness analysis of the algorithm output.
Significant delays and resulting environmental impacts are commonly observed during departure operations at major US and European airports. One approach for mitigating airport congestion and delays is to exercise tactical operations planning and control with an objective to improve the efficiency of surface and terminal area operations. As a subtask of planning airport surface operations, this thesis presents a thorough study of the structure and properties of the Runway Operations Planning (ROP) problem. Runway Operations Planning is a workload-intensive task for controllers because airport operations involve many parameters, such as departure demand level and timing that are typically characterized by a highly dynamic behavior. This research work provides insight to the nature of this task, by analyzing the different parameters involved in it and illuminating how they interact with each other and how they affect the main functions in the problem of planning operations at the runway, such as departure runway throughput and runway queuing delays. Analysis of the Runway Operations Planning problem revealed that there is a parameter of the problem, namely the demand "weight class mix", which: a) is more "dominant" on the problem performance functions that other parameters, b) changes value much slower than other parameters and c) its value is available earlier and with more certainty than the value of other parameters. These observations enabled the parsing of the set of functions and the set of parameters in subsets, so that the problem can be addressed sequentially in more than one stage where different parameter subsets are treated in different stages. Thus, a decomposition-based algorithm design technique was introduced and applied
by Ioannis D. Anagnostakis.
Ph.D.
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Abedrabboh, Walid Yousef. "Multi-objective decision making applied for watershed development planning of Zarqa River Basin in Jordan." Diss., The University of Arizona, 1988. http://hdl.handle.net/10150/191142.
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In developing natural resources, decision makers are seeking to achieve different objectives, which cannot be reduced to a single objective such as economic efficiency, this covers only part of the problem. Tradeoffs between multiple objective of unequal importance is unavoidable in the process of selection or ranking of alternative developmental projects or plans. Multiobjective technique has the ability to deal with qualitative and quantitative objectives, also it enhances the planning process by involving broader segments of the society in the process of decision making. Compromise programming (CP) and utility worth analysis (UWA), two multiobjective methods were applied on Zarqa River Basin Project (ZRBP) in Jordan. Their appropriateness and suitability as decision aiding tools was examined in this study. For the purpose of the study, five criteria were developed to serve as a basis for the evaluation and 61 farmers and 15 technicians, planners and decision makers were interviewed. High consistency was observed among the results of ranking the six alternatives when both methods were applied, at the same time the ranking of the alternatives according to benefit/cost ratio and the internal rates of return as economic efficiency measures showed no agreement with the multiobjective ranking.
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Champion, Heather. "Beam angle and fluence map optimization for PARETO multi-objective intensity modulated radiation therapy treatment planning." Medical Physics, 2011. http://hdl.handle.net/1993/8910.
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In this work we introduce PARETO, a multiobjective optimization tool that simultaneously optimizes beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning using a powerful genetic algorithm. We also investigate various objective functions and compare several parameterizations for modeling beam fluence in terms of fluence map complexity, solution quality, and run efficiency. We have found that the combination of a conformity-based Planning Target Volume (PTV) objective function and a dose-volume histogram or equivalent uniform dose -based objective function for Organs-At-Risk (OARs) produced relatively uniform and conformal PTV doses, with well-spaced beams. For two patient data sets, the linear gradient and beam group fluence parameterizations produced superior solution quality using a moderate and high degree of modulation, respectively, and had comparable run times. PARETO promises to improve the accuracy and efficiency of treatment planning by fully automating the optimization and producing a database of non-dominated solutions for each patient.
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Alfaify, Abdullah Yahia M. "Integrated Modelling for Supply Chain Planning and Multi-Echelon Safety Stock Optimization in Manufacturing Systems." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/30691.
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Optimizing supply chain is the most successful key for manufacturing systems to be competitive. Supply chain (SC) has gotten intensive research works at all levels: strategic, tactical, and operational levels. These levels, in some researches, have integrated with each other or integrated with other planning issues such as inventory. Optimizing inventory location and level of safety stock at all supply chain partners is essential in high competitive markets to manage uncertain demand and service level. Many works have been developed to optimize the location of safety stock along supply chain, which is important for fast response to fluctuation in demand. However, most of these studies focus on the design stage of a supply chain. Because demand at different horizon times may vary according to different reasons such as the entry of different competitors on market or seasonal demand, safety stock should be optimized accordingly. At the planning (tactical) level, safety stock can be controlled according to each planning horizon to satisfy customer demand at lower cost instead of being fixed by a decision taken at the strategic level. On the other hand, most studies that consider safety stock optimization are tied to a specific system structure such as serial, assembly, or distribution structure.
This research focuses on formulating two different models. First, a multi- echelon safety stock optimization (MESSO) model for general supply chain topology is formulated. Then, it is converted into a robust form (RMESSO) which considers all possible fluctuation in demand and gives a solution that is valid under any circumstances. Second, the safety stock optimization model is integrated with tactical supply chain planning (SCP) for manufacturing systems. The integrated model is a multi-objective mixed integer non-linear programming (MINLP) model. This model aims to minimize the total cost and total time. A case study for each model is provided and the numerical results are analyzed.
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Ab, Rashid Mohd Fadzil Faisae. "Integrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation." Thesis, Cranfield University, 2013. http://dspace.lib.cranfield.ac.uk/handle/1826/8257.
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In assembly optimisation, Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) optimisations currently performed in serial, present an opportunity for integration, allowing benefits such as larger search space leading to better solution quality, reduced error rate in planning and fast time-to-market for a product. The literature survey highlights the research gaps, where the existing integrated ASP and ALB optimisation is limited to a Genetic Algorithm (GA) based approach, while Particle Swarm Optimisation (PSO) demonstrates better performance in individual ASP and ALB optimisation compared to GA. In addition, the existing works are limited to simple assembly line problems which run a homogeneous model on an assembly line. The aim of this research is to establish a methodology and algorithm for integrating ASP and ALB optimisation using Particle Swarm Optimisation. This research extends the problem type to integrated mixed-model ASP and ALB in order to generalise the problem. This research proposes Multi-Objective Discrete Particle Swarm Optimisation (MODPSO), to optimise integrated ASP and ALB. The MODPSO uses the Pareto-based approach to deal with the multi-objective problem and adopts a discrete procedure instead of standard mathematical operators to update its position and velocity. The MODPSO algorithm is tested with a wide range of problem difficulties for integrated single-model and mixed-model ASP and ALB problems. In order to supply sufficient test problems that cover a range of problem difficulties, a tuneable test problem generator is developed. Statistical tests on the algorithms’ performance indicates that the proposed MODPSO algorithm presents significant improvement in terms of larger non-dominated solution numbers in Pareto optimal, compared to comparable algorithms including GA based algorithms in both single-model and mixed-model ASP and ALB problems. The performance of the MODPSO algorithm is finally validated using artificial problems from the literature and real-world problems from assembly products.
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Trifković, Aleksandar. "Multi-objective and risk-based modelling methodology for planning, design and operation of water supply systems." [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-32516.
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Barnacle, Malcolm. "A multi-objective transmission reinforcement planning approach for analysing future energy scenarios in the GB network." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=28340.
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Due to increasing worldwide environmental concern, the United Kingdom (UK) government, under the Climate Change Act (2008), has set a target of at least an 80% reduction in the net UK carbon account, from baseline 1990 levels, by 2050. Recently there has been a rise in the number of low-carbon policy related studies, creating a growing number of national energy scenarios, some of which achieve the emission targets for 2050. A key aspect of evaluating the technical and economic impact of these energy scenarios is in assessing the associated effect on the electrical transmission network. As a result of a new scenario-related generation background, network limitations are likely to occur on the system. By creating a transmission reinforcement plan to alleviate these network issues, a conclusion can be made as to the economic impact of a future scenario to the electrical transmission network; thereby aiding the overall assessment of the scenario. However, by its nature the transmission planning problem is multi-objective with multiple economic conflicts. For a reinforcement designed for the main interconnected transmission system to gain economic approval from the network regulator, the reinforcement needs to alleviate annual network congestion such that the cost savings associated are greater than the capital expenditure and maintenance costs of the project. Further, this reinforcement will need to be established with minimal outages to existing network assets. This thesis proposes a flexible framework to evaluate the thermal and economic effect of applying a future energy scenario to the GB network. This is achieved through locating an optimal set of transmission reinforcement plans for the multi-criteria problem outlined above. The framework utilises a novel systematic algorithm to generate individual reinforcements and overall reinforcement plans for a large-scale multi-voltage network. The systematic algorithm can alter the associated reinforcements should they exacerbate thermal constraints. Specific reinforcements are therefore created for the scenario, and the framework can therefore be used to evaluate a wide range of future scenarios. The framework is designed to cater for three variations in reinforcement characteristic; location, configuration (line upgrading, single-circuit and double-circuit addition) and thermal capacity. The new framework carries out a thorough exploration of each characteristic and uses a proven multi-objective meta-heuristic technique to perform the optimisation, which can handle complex multi-criteria problems such as transmission network planning effectively. The reinforcement plans generated are assessed against a stochastic, seasonal evaluation of annual network congestion, which reflects the uncertainty of annual generation output and the impact of planned network outages on annual system constraints. Although meta-heuristic techniques have been successfully applied to solve a variant of the multi-objective transmission planning problem proposed in this thesis, these approaches often simplified the reinforcement characteristics considered and the impact of these reinforcements on the objectives involved, and were often tested against small-scale simplified network backgrounds. From the frameworks output, a verdict on the economic impact of a future scenario to the electrical transmission network can be made which considers the different perspectives and complexities of the transmission planning problem. By comparing verdicts, a scenario can be located that is the best route forward, from the perspective of the electrical transmission network, to economically meet governmental emission targets. Hence the approach proposed can be used to improve current understanding on the economic impact of a wide range of penetrations in renewable and conventional generation to the network, to guide governmental energy policy and transmission network owner investment. Results from several scenario studies show that the framework is valuable for use in the evaluation of a UK energy scenario which envisions the continuation of a centralised power system.
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Ahern, Zeke Alexander. "Exact and approximate optimisation for strategic bus network planning." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/206458/1/Zeke_Ahern_Thesis.pdf.
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This thesis contributes to the area of transportation network design at the strategic level, considering objectives for the passenger and operator. The main goal of the research is to improve the existing methods by developing new and more rigorous approaches to integrating route choice, service frequency and adequately accounting for passenger waiting time. An exact model was developed: providing a concise non-ambiguous description to the problem. Case study problem instances found that exact methods implemented by commercial solvers are not scalable for practical problems. Therefore, meta-heuristics were presented to find near-optimal solutions efficiently and demonstrate the practicality of the model in the real-world.
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Gettman, Douglas Mark 1971. "A multi-objective integrated large-scale optimized ramp metering control system for freeway/surface-street traffic management." Diss., The University of Arizona, 1998. http://hdl.handle.net/10150/282797.
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This research, denoted MILOS (Multi-objective Integrated Large-scale Optimized ramp metering System) is a hierarchical structure for solution of the large-scale freeway management problem to address the key features of this problem (dynamic state changes, stochasticity, multi-dimensionality, unpredictability, partial-observability, and existence of multiple objectives). MILOS decomposes the freeway control problem into subproblems along temporal/spatial boundaries and is composed of three primary components: SPC-based anomaly detection and optimization scheduling, area-wide coordination layer, and predictive-cooperative real-time (PC-RT) optimization layer. The area-wide coordination component of the hierarchical control system considers the impact of queue growth on the adjacent interchanges in a quadratic programming optimization model with a multi-criterion objective function. The formulation of the area-wide optimization problem is augmented with overflow variables to guarantee a feasible solution. The nominal solution of the areawide coordination problem is then modified in real-time by the locally traffic-reactive, PC-RT algorithm based on a linear-program using a linearized dynamic difference equation implementation of the macroscopic FREFLO model. The PC-RT formulation pro-actively plans to utilize opportunities to disperse queues or hold back additional vehicles when freeway and ramp demand conditions are appropriate. The cost coefficients of this optimization problem is linked to the solution of the area-wide coordination problem by using information on the dual of the solution to the area-wide coordination problem. The optimization runs of the area-wide coordination problem and the PC-RT optimization problems at each ramp are scheduled by a demand/flow monitoring system based on statistical process control. A simulation experiment is executed to evaluate the MILOS hierarchical system against "no control", ADOT's current ramp metering policy, and an area-wide LP optimization problem resolved in 5-minute intervals on a small freeway network in the metropolitan Phoenix, AZ area. Three test cases are presented for a short "burst" of heavy-volume flows to all ramps, a 3-hour commuting peak, and a 3-hour commuting peak with a 30-minute incident occurring in the middle of the network. The performance results indicate that MILOS is able to reduce freeway travel time, increase freeway average speed, and improve recovery performance of the system when flow conditions become congested.
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Trifković, Aleksandar [Verfasser]. "Multi-objective and risk-based modelling methodology for planning, design and operation of water supply systems / von Aleksandar Trifković." Stuttgart : Inst. für Wasserbau, 2007. http://d-nb.info/996789073/34.
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Martinez, Oscar. "MULTIOBJECTIVE COORDINATION MODELS FOR MAINTENANCE AND SERVICE PARTS INVENTORY PLANNING AND CONTROL." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3985.
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In many equipment-intensive organizations in the manufacturing, service and particularly the defense sectors, service parts inventories constitute a significant source of tactical and operational costs and consume a significant portion of capital investment. For instance, the Defense Logistics Agency manages about 4 million consumable service parts and provides about 93% of all consumable service parts used by the military services. These items required about US$1.9 billion over the fiscal years 1999-2002. During the same time, the US General Accountability Office discovered that, in the United States Navy, there were about 3.7 billion ship and submarine parts that were not needed. The Federal Aviation Administration says that 26 million aircraft parts are changed each year. In 2002, the holding cost of service parts for the aviation industry was estimated to be US$50 billion. The US Army Institute of Land Warfare reports that, at the beginning of the 2003 fiscal year, prior to Operation Iraqi Freedom the aviation service parts alone was in excess of US$1 billion. This situation makes the management of these items a very critical tactical and strategic issue that is worthy of further study. The key challenge is to maintain high equipment availability with low service cost (e.g., holding, warehousing, transportation, technicians, overhead, etc.). For instance, despite reporting US$10.5 billion in appropriations spent on purchasing service parts in 2000, the United States Air Force (USAF) continues to report shortages of service parts. The USAF estimates that, if the investment on service parts decreases to about US$5.3 billion, weapons systems availability would range from 73 to 100 percent. Thus, better management of service parts inventories should create opportunities for cost savings caused by the efficient management of these inventories. Unfortunately, service parts belong to a class of inventory that continually makes them difficult to manage. Moreover, it can be said that the general function of service parts inventories is to support maintenance actions; therefore, service parts inventory policies are highly related to the resident maintenance policies. However, the interrelationship between service parts inventory management and maintenance policies is often overlooked, both in practice and in the academic literature, when it comes to optimizing maintenance and service parts inventory policies. Hence, there exists a great divide between maintenance and service parts inventory theory and practice. This research investigation specifically considers the aspect of joint maintenance and service part inventory optimization. We decompose the joint maintenance and service part inventory optimization problem into the supplier s problem and the customer s problem. Long-run expected cost functions for each problem that include the most common maintenance cost parameters and service parts inventory cost parameters are presented. Computational experiments are conducted for a single-supplier two-echelon service parts supply chain configuration varying the number of customers in the network. Lateral transshipments (LTs) of service parts between customers are not allowed. For this configuration, we optimize the cost functions using a traditional, or decoupled, approach, where each supply chain entity optimizes its cost individually, and a joint approach, where the cost objectives of both the supplier and customers are optimized simultaneously. We show that the multiple objective optimization approach outperforms the traditional decoupled optimization approach by generating lower system-wide supply chain network costs. The model formulations are extended by relaxing the assumption of no LTs between customers in the supply chain network. Similar to those for the no LTs configuration, the results for the LTs configuration show that the multiobjective optimization outperforms the decoupled optimization in terms of system-wide cost. Hence, it is economically beneficial to jointly consider all parties within the supply network. Further, we compare the model configurations LTs versus no LTs, and we show that using LTs improves the overall savings of the system. It is observed that the improvement is mostly derived from reduced shortage costs since the equipment downtime is reduced due to the proximity of the supply. The models and results of this research have significant practical implications as they can be used to assist decision-makers to determine when and where to pre-position parts inventories to maximize equipment availability. Furthermore, these models can assist in the preparation of the terms of long-term service agreements and maintenance contracts between original equipment manufacturers and their customers (i.e., equipment owners and/or operators), including determining the equitable allocation of all system-wide cost savings under the agreement.Ph.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering PhD
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Rodríguez, Molins Mario. "Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals." Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/48545.
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Despite the continuous evolution in computers and information technology, real-world
combinatorial optimization problems are NP-problems, in particular in the domain of
planning and scheduling. Thus, although exact techniques from the Operations Research
(OR) field, such as Linear Programming, could be applied to solve optimization problems,
they are difficult to apply in real-world scenarios since they usually require too much computational
time, i.e: an optimized solution is required at an affordable computational time.
Furthermore, decision makers often face different and typically opposing goals, then resulting
multi-objective optimization problems. Therefore, approximate techniques from
the Artificial Intelligence (AI) field are commonly used to solve the real world problems.
The AI techniques provide richer and more flexible representations of real-world (Gomes
2000), and they are widely used to solve these type of problems. AI heuristic techniques
do not guarantee the optimal solution, but they provide near-optimal solutions in a reasonable
time. These techniques are divided into two broad classes of algorithms: constructive
and local search methods (Aarts and Lenstra 2003). They can guide their search processes
by means of heuristics or metaheuristics depending on how they escape from local optima
(Blum and Roli 2003). Regarding multi-objective optimization problems, the use of AI
techniques becomes paramount due to their complexity (Coello Coello 2006).
Nowadays, the point of view for planning and scheduling tasks has changed. Due to
the fact that real world is uncertain, imprecise and non-deterministic, there might be unknown
information, breakdowns, incidences or changes, which become the initial plans
or schedules invalid. Thus, there is a new trend to cope these aspects in the optimization
techniques, and to seek robust solutions (schedules) (Lambrechts, Demeulemeester, and
Herroelen 2008).
In this way, these optimization problems become harder since a new objective function
(robustness measure) must be taken into account during the solution search. Therefore,
the robustness concept is being studied and a general robustness measure has been developed
for any scheduling problem (such as Job Shop Problem, Open Shop Problem,
Railway Scheduling or Vehicle Routing Problem). To this end, in this thesis, some techniques
have been developed to improve the search of optimized and robust solutions in
planning and scheduling problems. These techniques offer assistance to decision makers
to help in planning and scheduling tasks, determine the consequences of changes, provide
support in the resolution of incidents, provide alternative plans, etc.
As a case study to evaluate the behaviour of the techniques developed, this thesis focuses
on problems related to container terminals. Container terminals generally serve
as a transshipment zone between ships and land vehicles (trains or trucks). In (Henesey
2006a), it is shown how this transshipment market has grown rapidly. Container terminals
are open systems with three distinguishable areas: the berth area, the storage yard,
and the terminal receipt and delivery gate area. Each one presents different planning and
scheduling problems to be optimized (Stahlbock and Voß 2008). For example, berth allocation,
quay crane assignment, stowage planning, and quay crane scheduling must be
managed in the berthing area; the container stacking problem, yard crane scheduling, and
horizontal transport operations must be carried out in the yard area; and the hinterland
operations must be solved in the landside area.
Furthermore, dynamism is also present in container terminals. The tasks of the container
terminals take place in an environment susceptible of breakdowns or incidences. For
instance, a Quay Crane engine stopped working and needs to be revised, delaying this
task one or two hours. Thereby, the robustness concept can be included in the scheduling
techniques to take into consideration some incidences and return a set of robust schedules.
In this thesis, we have developed a new domain-dependent planner to obtain more effi-
cient solutions in the generic problem of reshuffles of containers. Planning heuristics and
optimization criteria developed have been evaluated on realistic problems and they are
applicable to the general problem of reshuffling in blocks world scenarios.
Additionally, we have developed a scheduling model, using constructive metaheuristic
techniques on a complex problem that combines sequences of scenarios with different
types of resources (Berth Allocation, Quay Crane Assignment, and Container Stacking
problems). These problems are usually solved separately and their integration allows
more optimized solutions.
Moreover, in order to address the impact and changes that arise in dynamic real-world
environments, a robustness model has been developed for scheduling tasks. This model
has been applied to metaheuristic schemes, which are based on genetic algorithms. The
extension of such schemes, incorporating the robustness model developed, allows us to
evaluate and obtain more robust solutions. This approach, combined with the classical
optimality criterion in scheduling problems, allows us to obtain, in an efficient in way,
optimized solution able to withstand a greater degree of incidents that occur in dynamic
scenarios. Thus, a proactive approach is applied to the problem that arises with the presence
of incidences and changes that occur in typical scheduling problems of a dynamic real world.Rodríguez Molins, M. (2015). Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48545
TESIS
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Kamali, Aslan. "Developing a Decision Making Approach for District Cooling Systems Design using Multi-objective Optimization." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-208228.
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Energy consumption rates have been dramatically increasing on a global scale within the last few decades. A significant role in this increase is subjected by the recent high temperature levels especially at summer time which caused a rapid increase in the air conditioning demands. Such phenomena can be clearly observed in developing countries, especially those in hot climate regions, where people depend mainly on conventional air conditioning systems. These systems often show poor performance and thus negatively impact the environment which in turn contributes to global warming phenomena. In recent years, the demand for urban or district cooling technologies and networks has been increasing significantly as an alternative to conventional systems due to their higher efficiency and improved ecological impact. However, to obtain an efficient design for district cooling systems is a complex task that requires considering a wide range of cooling technologies, various network layout configuration possibilities, and several energy resources to be integrated. Thus, critical decisions have to be made regarding a variety of opportunities, options and technologies. The main objective of this thesis is to develop a tool to obtain preliminary design configurations and operation patterns for district cooling energy systems by performing roughly detailed optimizations and further, to introduce a decision-making approach to help decision makers in evaluating the economic aspects and environmental performance of urban cooling systems at an early design stage. Different aspects of the subject have been investigated in the literature by several researchers. A brief survey of the state of the art was carried out and revealed that mathematical programming models were the most common and successful technique for configuring and designing cooling systems for urban areas. As an outcome of the survey, multi objective optimization models were decided to be utilized to support the decision-making process. Hence, a multi objective optimization model has been developed to address the complicated issue of decision-making when designing a cooling system for an urban area or district. The model aims to optimize several elements of a cooling system such as: cooling network, cooling technologies, capacity and location of system equipment. In addition, various energy resources have been taken into consideration as well as different solar technologies such as: trough solar concentrators, vacuum solar collectors and PV panels. The model was developed based on the mixed integer linear programming method (MILP) and implemented using GAMS language. Two case studies were investigated using the developed model. The first case study consists of seven buildings representing a residential district while the second case study was a university campus district dominated by non-residential buildings. The study was carried out for several groups of scenarios investigating certain design parameters and operation conditions such as: Available area, production plant location, cold storage location constraints, piping prices, investment cost, constant and variable electricity tariffs, solar energy integration policy, waste heat availability, load shifting strategies, and the effect of outdoor temperature in hot regions on the district cooling system performance. The investigation consisted of three stages, with total annual cost and CO2 emissions being the first and second single objective optimization stages. The third stage was a multi objective optimization combining the earlier two single objectives. Later on, non-dominated solutions, i.e. Pareto solutions, were generated by obtaining several multi objective optimization scenarios based on the decision-makers’ preferences. Eventually, a decision-making approach was developed to help decision-makers in selecting a specific solution that best fits the designers’ or decision makers’ desires, based on the difference between the Utopia and Nadir values, i.e. total annual cost and CO2 emissions obtained at the single optimization stagesDie Energieverbrauchsraten haben in den letzten Jahrzehnten auf globaler Ebene dramatisch zugenommen. Diese Erhöhung ist zu einem großen Teil in den jüngst hohen Temperaturniveaus, vor allem in der Sommerzeit, begründet, die einen starken Anstieg der Nachfrage nach Klimaanlagen verursachen. Solche Ereignisse sind deutlich in Entwicklungsländern zu beobachten, vor allem in heißen Klimaregionen, wo Menschen vor allem konventionelle Klimaanlagensysteme benutzen. Diese Systeme verfügen meist über eine ineffiziente Leistungsfähigkeit und wirken sich somit negativ auf die Umwelt aus, was wiederum zur globalen Erwärmung beiträgt. In den letzten Jahren ist die Nachfrage nach Stadt- oder Fernkältetechnologien und -Netzwerken als Alternative zu konventionellen Systemen aufgrund ihrer höheren Effizienz und besseren ökologischen Verträglichkeit satrk gestiegen. Ein effizientes Design für Fernkühlsysteme zu erhalten, ist allerdings eine komplexe Aufgabe, die die Integration einer breite Palette von Kühltechnologien, verschiedener Konfigurationsmöglichkeiten von Netzwerk-Layouts und unterschiedlicher Energiequellen erfordert. Hierfür ist das Treffen kritischer Entscheidungen hinsichtlich einer Vielzahl von Möglichkeiten, Optionen und Technologien unabdingbar. Das Hauptziel dieser Arbeit ist es, ein Werkzeug zu entwickeln, das vorläufige Design-Konfigurationen und Betriebsmuster für Fernkälteenergiesysteme liefert, indem aureichend detaillierte Optimierungen durchgeführt werden. Zudem soll auch ein Ansatz zur Entscheidungsfindung vorgestellt werden, der Entscheidungsträger in einem frühen Planungsstadium bei der Bewertung städtischer Kühlungssysteme hinsichtlich der wirtschaftlichen Aspekte und Umweltleistung unterstützen soll. Unterschiedliche Aspekte dieser Problemstellung wurden in der Literatur von verschiedenen Forschern untersucht. Eine kurze Analyse des derzeitigen Stands der Technik ergab, dass mathematische Programmiermodelle die am weitesten verbreitete und erfolgreichste Methode für die Konfiguration und Gestaltung von Kühlsystemen für städtische Gebiete sind. Ein weiteres Ergebnis der Analyse war die Festlegung von Mehrzieloptimierungs-Modelles für die Unterstützung des Entscheidungsprozesses. Darauf basierend wurde im Rahmen der vorliegenden Arbeit ein Mehrzieloptimierungs-Modell für die Lösung des komplexen Entscheidungsfindungsprozesses bei der Gestaltung eines Kühlsystems für ein Stadtgebiet oder einen Bezirk entwickelt. Das Modell zielt darauf ab, mehrere Elemente des Kühlsystems zu optimieren, wie beispielsweise Kühlnetzwerke, Kühltechnologien sowie Kapazität und Lage der Systemtechnik. Zusätzlich werden verschiedene Energiequellen, auch solare wie Solarkonzentratoren, Vakuum-Solarkollektoren und PV-Module, berücksichtigt. Das Modell wurde auf Basis der gemischt-ganzzahlig linearen Optimierung (MILP) entwickelt und in GAMS Sprache implementiert. Zwei Fallstudien wurden mit dem entwickelten Modell untersucht. Die erste Fallstudie besteht aus sieben Gebäuden, die ein Wohnviertel darstellen, während die zweite Fallstudie einen Universitätscampus dominiert von Nichtwohngebäuden repräsentiert. Die Untersuchung wurde für mehrere Gruppen von Szenarien durchgeführt, wobei bestimmte Designparameter und Betriebsbedingungen überprüft werden, wie zum Beispiel die zur Verfügung stehende Fläche, Lage der Kühlanlage, örtliche Restriktionen der Kältespeicherung, Rohrpreise, Investitionskosten, konstante und variable Stromtarife, Strategie zur Einbindung der Solarenergie, Verfügbarkeit von Abwärme, Strategien der Lastenverschiebung, und die Wirkung der Außentemperatur in heißen Regionen auf die Leistung des Kühlsystems. Die Untersuchung bestand aus drei Stufen, wobei die jährlichen Gesamtkosten und die CO2-Emissionen die erste und zweite Einzelzieloptimierungsstufe darstellen. Die dritte Stufe war ein Pareto-Optimierung, die die beiden ersten Ziele kombiniert. Im Anschluss wurden nicht-dominante Lösungen, also Pareto-Lösungen, erzeugt, indem mehrere Pareto-Optimierungs-Szenarien basierend auf den Präferenzen der Entscheidungsträger abgebildet wurden. Schließlich wurde ein Ansatz zur Entscheidungsfindung entwickelt, um Entscheidungsträger bei der Auswahl einer bestimmten Lösung zu unterstützen, die am besten den Präferenzen des Planers oder des Entscheidungsträgers enstpricht, basierend auf der Differenz der Utopia und Nadir Werte, d.h. der jährlichen Gesamtkosten und CO2-Emissionen, die Ergebnis der einzelnen Optimierungsstufen sind
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Esmaeelnezhad, Ali. "Stochastic long-term transmission expansion planning with HVDC links." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Find full textAbstract:
The ever-increasing load demand all across the world has led to increasing the demand for energy. The major part of this demand is supplied through conventional power plants with fossil fuels. Recently, the concern on environmental emissions has increased, which in turn put pressure on power systems as one of the main sectors in contributing to environmental emissions. Accordingly, different countries have started installing renewable energies to alleviate these concerns and mitigate environmental emissions. These green technologies have been being installed either as centralized plants, for example, wind farms, or as distributed energy resources (DERs) in distribution networks. If such huge wind farms connect to the transmission system, they may bring severe challenges to the system, such as transmission network congestion. On the other hand, in some cases, these wind sites are located far from the main power system. Thus, it is required to expand the existing power system by constructing new corridors to connect them to the transmission system. High voltage DC technologies have also been introduced as efficient systems with numerous merits. In this regard, this dissertation seeks to address the long-term AC/DC transmission expansion planning to connect distant wind farms to the power system. The problem has been modeled both deterministically and stochastically within a multi-objective mixed-integer quadratically constrained programming framework, aimed at minimizing the total cost and transmission line loading. The total cost is comprised of the total investment cost and the total operating cost. Then, the problem is solved by using the normal boundary intersection method as an efficient mathematical multi-objective optimization technique, and the most preferred solution has been selected by utilizing the VIKOR decision maker. Different case studies have also been evaluated by simulating the problem using the Garver test system and IEEE reliability test system.
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Xiao, Lijian. "The Course Scheduling Problem with Room Considerations." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright162186801109714.
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Zmijewski, Nicholas. "Effects of Watershed Dynamics on Water Reservoir Operation Planning : Considering the Dynamic Effects of Streamflow in Hydropower Operation." Doctoral thesis, KTH, Vattendragsteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-201612.
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Water reservoirs are used to regulate river discharge for a variety of reasons, such as flood mitigation, water availability for irrigation, municipal consumption and power production purposes. Recent efforts to increase the amount of renewable power production have seen an increase in intermittent climate-variable power production due to wind and solar power production. The additional variable energy production has increased the need for regulating the capacity of the electrical system, to which hydropower production is a significant contributor. The hydraulic impact on the time lags of flows between production stations have often largely been ignored in optimization planning models in favor of computational efficiency and simplicity. In this thesis, the hydrodynamics in the stream network connecting managed reservoirs were described using the kinematic-diffusive wave (KD) equation, which was implemented in optimization schemes to illustrate the effects of wave diffusion in flow stretches on the resulting production schedule. The effect of wave diffusion within a watershed on the variance of the discharge hydrograph within a river network was also analyzed using a spectral approach, illustrating that wave diffusion increases the variance of the hydrograph while the regulation of reservoirs generally increases the variance of the hydrograph over primarily short periods. Although stream hydrodynamics can increase the potential regulation capacity, the total capacity for power regulation in the Swedish reservoir system also depends significantly on the variability in climatic variables. Alternative formulations of the environmental objectives, which are often imposed as hard constraints on discharge, were further examined. The trade-off between the objectives of hydropower production and improvement of water quality in downstream areas was examined to potentially improve the ecological and aquatic environments and the regulation capacity of the network of reservoirs.QC 20170210
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Kok, Jonathan. "Design methodologies and architectures of hardware-based evolutionary algorithms for aerospace optimisation applications on FPGAS." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/72904/5/Jonathan_Kok_Thesis.pdf.
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This thesis is a study of new design methods for allowing evolutionary algorithms to be more effectively utilised in aerospace optimisation applications where computation needs are high and computation platform space may be restrictive. It examines the applicability of special hardware computational platforms known as field programmable gate arrays and shows that with the right implementation methods they can offer significant benefits. This research is a step forward towards the advancement of efficient and highly automated aircraft systems for meeting compact physical constraints in aerospace platforms and providing effective performance speedups over traditional methods.
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"Multi-objective Operating Room Planning and Scheduling." Doctoral diss., 2010. http://hdl.handle.net/2286/R.I.8777.
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abstract: Surgery is one of the most important functions in a hospital with respect to operational cost, patient flow, and resource utilization. Planning and scheduling the Operating Room (OR) is important for hospitals to improve efficiency and achieve high quality of service. At the same time, it is a complex task due to the conflicting objectives and the uncertain nature of surgeries. In this dissertation, three different methodologies are developed to address OR planning and scheduling problem. First, a simulation-based framework is constructed to analyze the factors that affect the utilization of a catheterization lab and provide decision support for improving the efficiency of operations in a hospital with different priorities of patients. Both operational costs and patient satisfaction metrics are considered. Detailed parametric analysis is performed to provide generic recommendations. Overall it is found the 75th percentile of process duration is always on the efficient frontier and is a good compromise of both objectives. Next, the general OR planning and scheduling problem is formulated with a mixed integer program. The objectives include reducing staff overtime, OR idle time and patient waiting time, as well as satisfying surgeon preferences and regulating patient flow from OR to the Post Anesthesia Care Unit (PACU). Exact solutions are obtained using real data. Heuristics and a random keys genetic algorithm (RKGA) are used in the scheduling phase and compared with the optimal solutions. Interacting effects between planning and scheduling are also investigated. Lastly, a multi-objective simulation optimization approach is developed, which relaxes the deterministic assumption in the second study by integrating an optimization module of a RKGA implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to search for Pareto optimal solutions, and a simulation module to evaluate the performance of a given schedule. It is experimentally shown to be an effective technique for finding Pareto optimal solutions.Dissertation/Thesis
Ph.D. Industrial Engineering 2010
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SU, SHI-CHANG, and 蘇世昌. "Multi-objective optimal VAR planning in power system." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/90194350881163233072.
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Hsieh, Chih-Yi, and 謝志毅. "Applying Compromise Model to Multi-objective Aggregate Production Planning." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/54927116421458812251.
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碩士萬能科技大學
經營管理研究所
94
Aggregate production planning (APP) involves meeting the demand of ever changing customer orders in the medium term, which are 6 to 18 months prior to the production. In this research, we show a compromise model for solving the multi-objective production aggregate production planning in a certain environment. The proposed model can provide a compromise index which is adjustable by the decision maker according to reveal the way of change on the degree of satisfaction for each objective. Keyword：Aggregate production planning ;compromise model
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Chen, Lin-Kung, and 陳凌焜. "A research on multi-objective supply chain production planning." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/23204700718931387532.
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碩士國防管理學院
資源管理研究所
92
Though there are multiple mutual conflict objective functions exist in practical supply chain production planning problems, there are few literature dedicated to such multiple objective supply chain production planning. This research proposed a multiple objective integer programming formulation with two objective functions (maximum profit and maximum fill rates). In order to find out the non-inferior solutions for the formulation, this research then solved by weighting method and weighting method with conversion the weights to standardized weights. By comparison the non-inferior solutions between these two kinds of method, this research find out a fast way to obtain the ideal solution as well.
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Lin, Yu-Fan, and 林祐帆. "Applying Fuzzy Multi-Objective Programming to Recoverable Remanufacturing Planning." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/04568616570151462660.
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碩士國立屏東科技大學
工業管理系所
100
The product life cycle is characterized by specific stages, including research, development, introduction, maturity, decline, and obsolescence. Each stage is often linked with changes in the flows of raw materials, manufacturing, and distribution to markets, environmental issues related to upstream suppliers for the material to downstream manufacturers for the product should use recoverable components and reducing waste of limited resources purposes. However, the demand of customization is gradually increasing through recoverable components can not satisfy customer demand for new products. To fulfill a particular customer demand, lots of new materials and recoverable components may need to be released to minimize total expected costs. This study develops a fuzzy multi-objective linear programming (FMOLP) model with piecewise linear membership function to solve integrated multi-component and multi-supplier procurement/production planning decisions problems with fuzzy objectives. The initial multi-objective linear programming developed in this study model attempts to simultaneously minimize total costs and total lead time in relation to supplier capacity, lot-size release, machine yield, and customer demand. The proposed FMOLP model provides a systematic framework that facilitates fuzzy decision-making process, enabling the decision maker to interactively adjust the search direction during the solution procedure to obtain a decision maker’s preferred satisfactory solution. To test the models adequacy, an implementation designed as several scenarios was conducted in reality with remanufacturing production system. Analytical results presented in this study can help decision managers better understand the systematic analysis and potentials for the cost-effectiveness of production planning.
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CHANG, RUI-YING, and 張睿穎. "Applying Multi-objective Genetic Algorithm for Logistics Planning Problem." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/m5n3a7.
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碩士國立臺北科技大學
工業工程與管理系碩士班
105
In recent years, with the growth of the online shopping market, the rapid development of logistic demand makes it an important task to establish a complete and convenient logistics and distribution system. How to improve the distribution flexibility under the conditions of efficiency and quality to meet customer needs, and effectively reduce the distribution costs, to maximize corporate profits, promote the growth and competitiveness of the logistics industry. This study will focus on the internal and external operating network of the freight industry. In order to address the serious carbon emissions caused by the transport industry, this study aims to minimize the carbon emissions generated by total driving. In recent years, the Government has increased the minimum wage, overtime pay. therefore, the study also consider the work of the truck balance to minimize the difference between each truck’s delivery time as the goal to multi- Window constrained vehicle routing problem to construct a mathematic model to solve the path of delivery of goods by multi-objective genetic algorithm planning. In addition, the study will build an instant, flexible, real-time delivery application for cargo delivery through the provision of an information system, combined with personal mobile device (APP) technology, to deliver information to carriers and other customers via instant delivery of the multi-demand.
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"Multi-objective land use optimization using genetic algorithm." Thesis, 2010. http://library.cuhk.edu.hk/record=b6074924.
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Land use optimization is a multifaceted process that entails complex decision-making which involves the selection of activities, the percentages to allocate, and where to allocate. It will also add a whole extra class of variables to the problem when combined with the inevitable consideration of spatial optimization. The related applications by linear programming (LP), "Pareto Front Optimal" based methods, heuristics methods and integration of GIS etc. for spatial multi-objective land use optimization are reviewed and analyzed on their advantages and disadvantages in this thesis. Accordingly, due to the nonlinearity and the complexity caused by the multiple objectives and increasing variables during the optimization process, the efficiency and effect would be the issues to be considered. The need for effective and efficient models for land use optimization is evident from the above discussion as the core content. In order to comprehensively fulfill all the requirements, the understanding of the sustainability of land use is translated into eight objectives to form the Multi-objective Optimization of Land Use (MOLU) model. Furthermore, an efficient model named Boundary based Fast Genetic Algorithm (BFGA) using goal programming is employed in the multi-objective optimization in Tongzhou Newtown. This algorithm is especially efficient for land use optimization problems derived from its special boundary based operators. Furthermore, considering the characteristics of planning support process and these two models mentioned above, the interactive spatial land use optimization prototype with a friendly interface and a simplified 3D visualization module could be established, thus yielding good effects and potential to support the planning process in the study area. Finally, in light of the study results and limitations, some directions are also provided for future research.Land use optimization, a kind of resource allocation, can be defined as the process of allocating different land use categories (e.g., residential, commercial, and industrial, etc.) to specific units of area within a region. As one of the most popular words nowadays, sustainable development can be viewed as a process of change in which the exploitation of resources, the direction of investment, the orientation of technological development and institutional change are all harmonized. Sustainability is, hence, an important and imminent societal goal for land use planning. Land use optimization involves the active planning of land for future use by people to provide for their needs. In this thesis, the central goal is to develop a sustainable land use optimization prototype to enrich the field of planning support with regard to sustainability.
Cao, Kai.
Source: Dissertation Abstracts International, Volume: 72-04, Section: A, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2010.
Includes bibliographical references (leaves 132-139).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
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CHEN, CHUNG-YANG, and 陳仲揚. "Multi-objective Stochastic Capacity Planning in Multi-Stage Production Network under Risk-Aversion." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/25543819138530165578.
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