Thèses : « Planning Optimization »

1

Morey, Christopher. « Optimization models for synchronization planning ». Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26674.

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MacLeod, Colin W. « Optimization of sewer infrastructure rehabilitation planning ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0008/MQ60152.pdf.

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Li, Qishan. « Accelerated degradation test planning and optimization ». Diss., The University of Arizona, 2002. http://hdl.handle.net/10150/289807.

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With increasing emphasis on reliability in industry, products are now made more robust, and few failures are observed in a short development period. In this circumstance, assessing product reliability based on degradation data at high stress levels becomes necessary. These tests are called accelerated degradation tests. There is a need to scientifically design these test plans. A good test plan can save time and expense, and provide more accurate estimates of reliability for the same number of test units and test time. To address this problem, a four-step-approach for the optimum planning of accelerated degradation tests is proposed in this study. First, a cost model for accelerated degradation tests is given. New analytical methods for obtaining the optimal allocations of the test units to selected stress levels are developed next. The stress considered here is temperature. Then, measurement plans are discussed. Nonlinear mixed effects models are applied and further developed and extended to allow for acceleration in the analysis of accelerated degradation data and to obtain the degradation model's parameters. A simulation method is used to evaluate the test plans' properties. In the simulation step, the mean square error is used as a criterion for comparing the accuracies that can be obtained from the test plans. A linear degradation case is used to illustrate the given approach. An LED example is also given to illustrate this approach. Test plans for obtaining accurate estimates of reliability information within cost budgets are important. The proposed approach enables reliability and test engineers to get the most efficient use of their test resources.

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Wang, Shentao. « Supply chain planning using network flow optimization ». online access from Digital Dissertation Consortium access full-text, 2003. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?MQ89164.

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Holm, Åsa. « Manpower Planning in Airlines : Modeling and Optimization ». Thesis, Linköping University, Department of Mathematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-14757.

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Crew costs are one of the largest expenses for airlines and effective manpower planning is therefore important to maximize profit. The focus of research in the field of manpower planning for airlines has mainly been on the scheduling of crew, while other areas, surprisingly, have received very little attention. This thesis provides an overview of some of the other problems facing manpower planners, such as designing a career ladder, planning transitions and making course schedules.

Mathematical models are presented for some of theses problems, and for the problem of allocating training and vacation in time the mathematical model has been tested on data from SAS Scandinavian Airlines. When allocating training and vacation there are many aspects to consider, such as avoiding crew shortage, access to resources needed for training, and vacation laws. Comparisons between solutions obtained with the model and SAS Scandinavian Airlines manual plan show encouraging results with savings around 10%.

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Gozbasi, Halil Ozan. « Optimization approaches for planning external beam radiotherapy ». Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34726.

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External beam radiotherapy is delivered from outside the body aimed at cancer cells to damage their DNA making them unable to divide and reproduce. The beams travel through the body and may damage nearby healthy tissues unless carefully planned. Therefore, the goal of treatment plan optimization is to find the best system configuration to deliver sufficient dose to target structures while avoiding damage to healthy tissues. This thesis investigates optimization approaches for two external beam radiation therapy techniques: Intensity-Modulated Radiation Therapy (IMRT) and Volumetric-Modulated Arc Therapy (VMAT). We develop an automated treatment planning technology for IMRT which generates several high-quality treatment plans satisfying the provided requirements in a single invocation and without human guidance. Our approach is based on an existing linear programming-based fluence map optimization model that approximates dose-volume requirements using conditional value-at-risk (C-VaR) constraints. We show how the parameters of the C-VaR constraints can be used to control various metrics of treatment plan quality. A novel bi-criteria scoring based beam selection algorithm is developed which finds the best beam configuration at least ten times faster for real-life brain, prostate, and head and neck cases as compared to an exact mixed integer programming model. Patient anatomy changes due to breathing during the treatment of lung cancer need to be considered in treatment planning. To date, a single phase of the breathing cycle is typically selected for treatment and radiation is shut-off in other phases. We investigate optimization technology that finds optimal fluence maps for each phase of the breathing cycle by considering the overall dose delivered to a patient using image registration algorithms to track target structures and organs at risk. Because the optimization exploits the opportunities provided in each phase, better treatment plans are obtained. The improvements are shown on a real-life lung case. VMAT is a recent radiation treatment technology which has the potential to provide treatments in less time compared to other delivery techniques. This enhances patient comfort and allows for the treatment of more patients. We build a large-scale mixed-integer programming model for VMAT treatment plan optimization. The solution of this model is computationally prohibitive. Therefore, we develop an iterative MIP-based heuristic algorithm which solves the model multiple times on a reduced set of decision variables. We introduce valid inequalities that decrease solution times, and, more importantly, that identify higher quality integer solutions within specified time limits. Computational studies on a spinal tumor and a prostate tumor case produce clinically acceptable results.

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Vistica, Marko. « Value of stochasticity in hydropower planning optimization ». Thesis, KTH, Elektriska energisystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103184.

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With respect to market liberalization, efficient use of resources is becoming more important for players in the market. In order to achieve that different optimization techniques were developed which enable better operational efficiency. These techniques can be segmented in to two different categories, depending on their time horizon: • Yearly time horizon – mid-term hydropower scheduling • Daily time horizon – short-term hydropower scheduling These two time horizons account for two case studies presented in this thesis. In the first case study (mid-term planning), the focus is on determining power plant’s optimal operating strategy, while taking into account the uncertainty in inflows and prices. Stochastic dynamic programming has been chosen as mid-term optimization technique. Since stochastic dynamic programming calls for a discretization of control and state variables, it may fall under the curse of dimensionality and therefore, the modeling of stochastic variables is important. By implementing a randomized search heuristic, a genetic algorithm, into the existing stochastic dynamic programming schema, the optimal way of using the stochasticity tries tobe found. Two price models are compared based on the economic quality of the result. The results give support to the idea of using search heuristics to determine the optimal stochasticity setup, however, some deviations from the expected results occur. Second case study deals with short-term hydropower planning, with a focus on satisfying the predefined demand schedule while obtaining maximum profit. With short-term hydropower planning being a nonlinear and nonconvex problem, the main focus is on the linearization ofunit performance curves, as well as satisfying technical constraints from the power plan tperspective. This optimization techniques also includes the water value in the solution. The problem has been solved by means of mixed integer linear programming. The results from the second case study are fully in line with the expectations and it is shown that mixed integer linear programming approach gives good results with good computational time. Suggested improvements to the model and potential for future work can be found in the final chapter of this thesis.

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Marko, Vistica. « Value of Stochasticity in Hydropower Planning Optimization ». Thesis, KTH, Elektriska energisystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-101702.

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With respect to market liberalization, efficient use of resources is becoming more important for players in the market. In order to achieve that different optimization techniques were developed which enable better operational efficiency. These techniques can be segmented into two different categories, depending on their time horizon: • Yearly time horizon – mid-term hydropower scheduling • Daily time horizon – short-term hydropower scheduling These two time horizons account for two case studies presented in this thesis. In the first case study (mid-term planning), the focus is on determining power plant’s optimal operating strategy, while taking into account the uncertainty in inflows and prices. Stochastic dynamic programming has been chosen as mid-term optimization technique. Since stochastic dynamic programming calls for a discretization of control and state variables, it may fall under the curse of dimensionality and therefore, the modeling of stochastic variables is important. By implementing a randomized search heuristic, a genetic algorithm, into the existing stochastic dynamic programming schema, the optimal way of using the stochasticity tries to be found. Two price models are compared based on the economic quality of the result. The results give support to the idea of using search heuristics to determine the optimal stochasticity setup, however, some deviations from the expected results occur. Second case study deals with short-term hydropower planning, with a focus on satisfying the predefined demand schedule while obtaining maximum profit. With short-term hydropower planning being a nonlinear and nonconvex problem, the main focus is on the linearization of unit performance curves, as well as satisfying technical constraints from the power plant perspective. This optimization techniques also includes the water value in the solution. The problem has been solved by means of mixed integer linear programming. The results from the second case study are fully in line with the expectations and it is shown that mixed integer linear programming approach gives good results with good computational time. Suggested improvements to the model and potential for future work can be found in the final chapter of this thesis.

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Krenzke, Tom (Tom Paul). « Ant colony optimization for agile motion planning ». Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35292.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 67-69).
With the need for greater autonomy in unmanned vehicles growing, design of algorithms for mission-level planning becomes essential. The general field of motion planning for unmanned vehicles falls into this category. Of particular interest is the case of operating in hostile environments with unknown threat locations. When a threat appears, a replan must be quickly formulated and executed. The use of terrain masking to hide from the threat is a vital tactic, which a good algorithm should exploit. In addition, the algorithm should be able to accommodate large search spaces and non-linear objective functions. This thesis investigates the suitability of the Ant Colony Optimization (ACO) heuristic for the agile vehicle motion planning problem. An ACO implementation tailored to the motion planning problem was designed and tested against an existing genetic algorithm solution method for validation. Results show that ACO is indeed a viable option for real-time trajectory generation. ACO' ability to incorporate heuristic information, and its method of solution construction, make it better suited to motion planning problems than existing methods.
by Tom Krenzke.
S.M.

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Ni, Yang M. Eng Massachusetts Institute of Technology. « Optimization of service parts planning for InFocus ». Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38289.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.
Includes bibliographical references (leaf 66).
A common problem faced by many industry companies, including InFocus, is that they are holding excess inventories and incurring unnecessary inventory holding costs yet are not able to achieve their expected customer service levels. This problem is critical to be addressed for companies who are striving to lower their supply chain costs and improve their management efficiencies. One solution to this problem is to use better forecasting methods and employ the appropriate inventory management policies. The objective of this project is to review the software package, Servigistics software tool, used by InFocus, to evaluate if it is suitable for InFocus' operations and to identify any improper practices when using Servigistics. The first step to address the problem is to review and evaluate Servigistics software package qualitatively. We found that Servigistics is a sound software tool with appropriate inventory and forecasting formulas. But the information fed into Servigistics is not accurate and up-to-date; this results in inaccurate results output by the Servigistics software. The second step is to evaluate Servigistics' output and InFocus' inventory management policy quantitatively.
(cont.) We found that InFocus has not been managing their inventory scientifically and for certain service parts, they procure excessive amounts in one shot. Approximately USD 3.8 million can be saved if InFocus manages its inventory scientifically using the current forecasting method. The major improvement comes from more discipline in their inventory management.
by Yang Ni.
M.Eng.

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Jauffred, M. Francisco Javier. « Stochastic optimization for robust planning in transportation ». Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10028.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1998.
Includes bibliographical references (leaves 116-118).
by Francisco J. Jauffred.
Ph.D.

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12

Brown, Matthew Lasché. « Intelligent robot grinding : planning, optimization, and control ». Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/14552.

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Marehalli, Jayavardhan N. « Assembly Sequence Optimization and Assembly Path Planning ». Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/44837.

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This thesis addresses two important aspects of automatic assembly viz., assembly sequence planning and assembly path planning. These issues are addressed separately starting with sequence planning followed by assembly path planning. For efficient assembly without feedback systems (or, passive assembly), an assembler should know the ideal orientation of each component and the order in which to put the parts together (or, assembly sequence). A heuristic is presented to find the optimal assembly sequence and prescribe the orientation of the components for a minimum set of grippers = ideally one. The heuristic utilizes an index of difficulty (ID) that quantifies assembly. The ID for each task in the assembly process is computed on the basis of a number of geometrical and operational properties. The objective of the optimization problem here is to minimize the assembly ID and categorize parts/subassemblies based on their preferred direction of assembly while allowing re-orientation of the base part. It is assumed that the preferred direction of assembly is vertically downward, consistent with manual as well as most automatic assembly protocols. Our attempt is to minimize the number of degrees of freedom required in a re-orienting fixture and derive the requirements for such a fixture. The assembly of a small engine is used as an example in this study due to the variety of ideally rigid parts involved. In high precision assembly tasks, contact motion is common and often desirable. This entails a careful study of contact states of the parts being assembled. Recognition of contact states is crucial in planning and executing contact motion plans due to inevitable uncertainties. Dr. Jing Xiao of UNCC introduced the concept of principal contacts (PC) and contact formation (CF) for contact state recognition. The concept of using CFs (as sets of PCs) has the inherent advantage that a change of CF is often coincident with a discontinuity of the general contact force (force and torque). Previous work in contact motion planning has shown that contact information at the level of PCs along with the sensed location and force information is often sufficient for planning high precision assembly operations. In this thesis, we present results from experiments involving planned contact motions to validate the notion of PCs and CFs -- an abrupt change in general contact force often accompanies a change between CFs. We are only concerned with solving the 2D peg-in-corner problem.
Master of Science

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Ahlberg, Max. « Optimization based trajectory planning for autonomous racing ». Thesis, KTH, Fordonsdynamik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265602.

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Autonomous driving is one of the three new technologies that are disrupting the classical vehicle industry together with electriﬁcation and connectivity. All three are pieces in the puzzle to drastically reduce the number of fatalities and injuries from traﬃc accidents but also to reduce the total amount of cars, reduce the polluting greenhouse gases, reduce noise pollution and completely eliminate unwanted driving. For example would most people rather rest, read or do anything else instead of driving in congested traﬃc. It is not small steps to take and it will have to be done incrementally as many other things. Within the vehicle industry racing has always been the natural place to push the boundaries of what is possible. Here new technologies can be tested under controlled circumstances in order to faster ﬁnd the best solution to a problem.Autonomous driving is no exception, the international student competition ”Formula Student” has introduced a driverless racing class and Formula E are slowly implementing Robo Race. The fact that race cars aim to drive at the limits of what is possible enable engineers to develop algorithms that can handle these conditions even in the every day life. Because even though the situations when normal passenger cars need to perform at the limits are rare, it is at these times it can save peoples lives. When an unforeseen event occurs and a fast manoeuvre has to be done in order to avoid the accident, that is when the normal car is driving at the limits. But the other thing to take into considerations when taking new technology into the consumer market is that the cars cannot cost as much as a race car. This means simpler computers has to be used and this in turn puts a constraint on the algorithms in the car. They can not be too computationally heavy.In this thesis a controller is designed to drive as fast as possible around the track. But in contrast to existing research it is not about how much the limit of speed can be pushed but of how simple a controller can be. The controller was designed with a Model Predictive Controller (MPC) that is based on a point mass model, that resembles the Center of Gravity (CoG) of the car. A g-g diagram that describes the limits of the modeled car is used as the constraints and the cost function is to maximize the distance progressed along the track in a ﬁx time step. Together with constraints on the track boundaries an optimization problem is giving the best possible trajectory with respect to the derived model. This trajectory is then sent to a low level controller, based on a Pure Pursuit and P controller, that is following the predicted race trajectory. Everything is done online such that implementation is possible. This controller is then compared and evaluated to a similar successful controller from the literature but which has a more complicated model and MPC formulation. The comparison is made and some notable diﬀerences are that the point mass model is behaving similar to the more complex model from the literature. Though is the hypothesis not correct since the beneﬁts of the simpliﬁcation of the model, from bicycle to point mass model, is replaced when more complex constraints has to be set up, resulting in similar performance even in computational times.A combination of the two models would probably yield the best result with acceptable computational times, this is left as future work to research.
Autonom körning är tillsammans med elektriﬁering och uppkopplande av fordon en av tre teknologier som håller på att förändra den klassiska fordonsindustrin. Alla tre är delar i ett pussel för att drastiskt reducera antalet döda och skadade i traﬁken men också för att reducera det totala antalet bilar, minska växthusgasutsläppen, mindre störande ljud och potentiellt helt kunna ta bort de timmar som man måste sitta framför en ratt utan att vilja. Det ¨ar inga små˚ förändringar och detta kommer implementeras stegvis. Inom for-donsindustrin har racing alltid varit den naturliga arenan för att föra utvecklingen framåt. Här kan nya teknologier bli testade under kontrollerade former för att snabbare kunna hitta optimala lösningar. När det kommer till autonom körning ser det ut som att detta är fallet här också, den internationella student tävlingen Formula Student har precis infört en klass för självkörande bilar och Formula E inför långsamt det som kallas Robo Race. De faktum att racebilar kör på gränsen av vad som är möjligt gör det möjligt för ingenjörer att utveckla algoritmer som kan hantera liknande situationer i vardagen. Även om de situationer där en vanlig bil behöver kunna köra på gränsen av vad som är möjligt är få, så är det vid dessa tillfällen som det ﬁnns möjlighet att rädda människoliv. När en oförutsädd händelse inträﬀar och en snabb manöver behöver utföras för att undvika en olycka, det är precis då som en vanlig bil behöver kunna köra på gränsen av vad som är möjligt. Men en annan sak att räkna in är att bilar som säljs till konsumenter inte kan kosta lika mycket som en racerbil. Detta betyder att enklare elektronik och hårdvara måste användas vilket i sin tur betyder att algoritmerna som används inte får vara för beräknings tunga. I detta examensarbete designas en regulator som skall vara så snabb som möjligt runt en bana, men istället för att, som i mycket annan forskning, fokusera helt på att minimera varvtiden, har här valts att se hur enkel och därmed beräkningssnabb man kan göra en regulator som ändå ger acceptabla resultat. Regulatorn var designad som en Modell Prediktiv Regulator (MPC) som är baserad på en punkt-mass modell som motsvarar mass-centrum av bilen. Ett g-g diagram används för att beskriva bilens gränser på vad den kan klara och kostnadsfunktionen designas på ett sätt så att regulatorn vill ta sig så långt som möjligt på banan under en ﬁx tid. Tillsammans med restriktioner på vart bilen får köra ger detta optimeringsproblem den optimala trajektorien. Den beräknade trajektorien är sedan skickad till lågnivåregulatorer, en Pure Pursuit för lateral styrning och en P regulator för longitudinel styrning. Allting är gjort så att det skall kunna implementeras på en riktig racerbil utan att beräkningar görs i förhand. Den designade regulatorn är sedan jämförd med en mer komplicerad regulator som är publicerad och som har visat sig fungera. Vid jämförelsen visade det sig att båda beter sig likt i många situationer men vissa skillnader kunde observeras, som att den föreslagna hypotesen inte visade sig vara riktig. Även fast den designade regulatorn var enklare i sin modell, blev restriktionsbetingelserna svårare att hantera och därmed beräkningstiden ungefär den samma. Att sammanfoga de två modellerna är av intresse för att hitta en bättre mer race-optimal lösning och kommer tas an i framtida arbete.

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Donahue, Scott F. « An optimization model for Army planning and programming ». Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from the National Technical Information Service, 1992. http://edocs.nps.edu/npspubs/scholarly/theses/1992/Sep/92Sep%5FDonahue.pdf.

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Vardanyan, Yelena. « On stochastic optimization for short-term hydropower planning ». Licentiate thesis, KTH, Elektriska energisystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104638.

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Renewable generation is the fastest growing energy resources in the past decade. Renewable energy sources, particularly wind power, provide clean and environmentally friendly energy to meet the system demand, meanwhile introducing huge levels of uncertainty in the system. On the one hand the deregulated electric power industry and on the other hand the intermittent nature of renewable energy sources cause highly volatile and uncertain electricity prices in different market places. This will create challenges for economical operation and planning of the flexible energy sources, particularly hydropower, which being a flexible energy source is the best option to balance wind power variation. The main purpose of this work is to develop optimal short-term planning models for price taker hydropower producer working in the existing environment. Those models have to deal with the huge level of uncertainties the wind power introduces into the power system. An optimization tool known as stochastic optimization is used to plan hydropower production under uncertainties. The first model, which is used to make sensitivity analysis, is a twostage stochastic linear programming problem. The uncertainties are handled by generating scenarios based on historical data. Profound sensitivity analysis is provided, in terms of volatility in day-ahead market prices and water inflow level as well as in terms of water opportunity cost and initial volume of the reservoir. Based on the comparison of the stochastic and corresponding deterministic problems, the result aims to show the impact of modeling the uncertainties explicitly. The results show that for the short-term hydropower planning problems the effect of considering price uncertainty in the stochastic model is higher compared with considering inflow level uncertainty. The second model used in this work is a two-stage stochastic linear programming problem. The model generates optimal bids to day-ahead market considering real-time market price uncertainties. While simultaneously bidding to both markets, the results for most of the hours suggest two actions; either to bid the available amount of energy to upward regulation market or to bid the maximum capacity to day-ahead market and bring back the whole amount making down regulation. To make the bidding strategies more flexible and robust different approaches are modeled and assessed. Finally one of the approaches is suggested as the most applicable one.

QC 20121109

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Setlhare, Keamogetse. « Optimization and estimation study of manpower planning models ». Thesis, Pretoria : [s.n.], 2007. http://upetd.up.ac.za/thesis/available/etd-06182007-161805.

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Felker, Paul W. « Using Optimization to Improve NASA Extravehicular Activity Planning ». Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17364.

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Extravehicular Activity (EVA) is a specialized function performed during spaceflights in which two or more astronauts don spacesuits to perform tasks on the exterior of their spacecraft. An extensive and iterative planning process is required to prepare for each highly choreographed EVA operation. The current planning process relies heavily upon time-consuming heuristic approaches by subject matter experts to essentially "hand-build" each EVA plan. This research develops the EVA Planning Model (EPM), a linear, mixed-integer program intended as a proof-of- concept demonstration for employing formal mathematical optimization techniques to EVA planning. The EPM is thoroughly tested to verify that it functions as intended and is evaluated by expert EVA planners using actual task information. We find that the EPM proves the concept that formal mathematical optimization can be used to aid in subject matter experts in EVA development and planning. It is particularly useful in allowing the evaluation of alternative planning inputs and thorough assessment of EVA plan impacts resulting from external changes.

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VIEIRA, ROBSON DOMINGOS. « FREQUENCY PLANNING OPTIMIZATION USING ISOLATION AND COMPATIBILITY MATRICES ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2001. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=1965@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Um algoritmo de alocação de freqüência eficiente deve considerar aspectos de propagação e detalhes de equipamento de modo a minimizar a relação a sinal interferência, e conseqüentemente, aumentar a eficiência do espectro. Essa dissertação tem o objetivo de propor uma melhor solução para alocação de freqüência nas rede TDMA e GSM, levando em consideração os aspectos citados. A abordagem utilizada é diferente das técnicas de alocação de freqüências usuais, que são baseadas na distância de reuso. O algoritmo proposto utiliza uma matriz que representa os aspectos de propagação (matriz de isolação) e uma matriz que representa os detalhes do equipamento (matriz de compatibilidade). A matriz de isolação mostra a relação sinal interferência que cada célula tem com as outras. Um estudo de como coletar os dados (seleção da rota no interior do setor) e de como trabalhar com os dados coletados foram realizados, objetivando-se ter matriz que melhor caracterizasse a rede utilizada. A matriz de compatibilidade mostra a distância em freqüência no qual se devem alocar os canais em uma mesma célula, células adjacentes e células co-sites. Os valores de espaçamento entre freqüências, sugeridos nesta dissertação, são valores práticos e dependem das técnicas utilizadas no planejamento e do equipamento utilizado em questão. O problema de alocação foi modelado com um problema inteiro. Uma heurística (divide and conquer) foi utilizada para particionar a rede em menores partes, em que o algoritmo branch and bound foi implementado. O algoritmo foi testado em duas redes com sessenta e trezentos e quarenta e oito setores respectivamente. As redes foram construída levando em consideração os dados topográficos e morfológicos da cidade de Brasília.
An efficient frequency assignment algorithm should consider both propagation conditions and equipment details, in order to minimize the carrier-to-interference ratio (C/I) and, as a result, to increase spectrum efficiency. This dissertation proposes a way to obtain a better solution for frequency assignment in TDMA and GSM networks, taking into account propagation conditions and equipment details. The proposed approach is different from usual frequency assignment techniques based on reuse distance. A frequency assignment algorithm proposed uses one matrix that represents the propagation features (isolation matrix) and another which represents the equipment details (compatibility matrix). The isolation matrix shows the signal to interference ratio between any two sectors in the whole network. A survey to define how to collect the data ( selection of the route inside the sector) and how to work with data collected was developed, aiming to obtain the best matrix representation of the network. The compatibility matrix shows the frequency distance between channels in the same sector, adjacent sector and co-site sectors. The frequency space values used in this matrix are practical values, which depend upon the technique used on the planning and on the utilized hardware. The frequency assignment was formulated as an integer problem. An heuristic (divide and conquer) was used to split the network into smaller parts where an exact algorithm branch and bound was implemented. The algorithm was tested on two networks with 60 and 348 sectors respectively. The networks were built having the topographical and morphological data from Brasilia city.
Un algoritmo eficiente de asignación de frecuencia debe considerar aspectos de propagación y detalles del equipo de modo tal que se minimize la relación señal-interferencia, y onsecuentemente, aumente la eficiencia del espectro. Esta disertación tiene como objetivo proponer una solución para la asignación de frecuencia en las redes TDMA y GSM, considerando los aspectos anteriormente citados. El enfoque utilizado difiere de las técnicas de asignación de frecuencias usuales, baseadas en la distancia de reuso. EL algoritmo propuesto utiliza una matriz que representa los aspectos de propagación (matriz de aislamiento) y una matriz que representa los detalles del equipo (matriz de compatibilidade). La matriz de aislamiento muestra la relación señal-interferencia que cada célula tiene con las otras. Se realiza un estudio de como recolectar los datos (selección de la ruta en el interior del sector) y de como trabajar con los datos recolectados, con el objetivo de obtener la matriz que mejor caracteriza la red utilizada. La matriz de compatibilidad muestra la distancia en frecuencia en la cual se deben alocar los canales en una misma célula, células adyacentes y células co-sites. Los valores de espacio entre frecuencias, sugeridos en esta disertación, son valores prácticos y dependen de las técnicas utilizadas en el planificación y del equipo utilizado. El problema de asignación fue modelado como un problema entero. Fue utilizada una heurística (divide and conquer) para particionar la red en menores partes, donde se implementó el algoritmo branch and bound. EL algoritmo fue probado en dos redes con sesenta y trezentos y quarenta y oito sectores respectivamente. Las redes fueron construidas considerando los datos topográficos y morfológicos de la ciudad de Brasilia.

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GOMES, ULISSES ROCHA. « COLD-ROLLING PROCESS OPTIMIZATION BY MEANS EXPERIMENT PLANNING ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10090@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Esta dissertação teve como objetivo desenvolver um modelo através da técnica de planejamento de experimentos aplicado ao processo de laminação a frio para fabricação de aços especiais utilizados no segmento de mercado de bebidas carbonatadas. O processo consiste na transformação através da redução da espessura de uma bobina de aço laminada a quente e depois decapada para remoção do óxido, em uma bobina com espessura final desejada pelo cliente. Este processo envolve variáveis controláveis e não controláveis, que interagem umas com as outras afetando a variável resposta que é a espessura final do produto. As questões fundamentais são entender como as variáveis controláveis afetam a variável resposta? Quais são as mais influentes? Existem interações entre estas variáveis? É possível elaborar um modelo adequado para o problema? A técnica do planejamento fatorial fracionado não só torna possível encontrar tais respostas para as questões levantadas, mas também, abre novas perspectivas de aplicação desta poderosa ferramenta nos processos de produção, onde é bastante comum encontrar situações semelhantes ao estudo proposto por este trabalho. A realização deste projeto compreendeu, uma descrição do problema, uma revisão bibliográfica com as etapas necessárias para execução de um planejamento de experimentos, escolha das variáveis controláveis que integraram o experimento, a execução do experimento, a análise dos resultados, uma abordagem dos Modelos Lineares Generalizados e validação do modelo através de ajuste dos parâmetros do processo conforme indicado através da análise dos resultados. A aplicação desta metodologia e a implementação das alterações propostas proporcionaram robustez ao processo de forma que mesmo quando ocorrem as perturbações das variáveis não controláveis, ainda assim a espessura permanece dentro das tolerâncias especificadas. Proporcionou ainda uma redução na variação de ± 1.5% para ± 1.0% , quando era de se esperar a necessidade de investimentos em tecnologia o que tornaria o produto mais caro e menos competitivo frente aos sucedâneos. Os resultados alcançados possibilitam a defesa de mercado através de uma maior competitividade obtida pelo menor custo dos produtos em aço.
This dissertation aims to develop a model by using experiment planning techniques applied to cold-rolling process to manufacture special steels utilized in the carbonated beverage market segment. The process consists of downgauging hot-rolled coil, which is further pickled for oxide removal, into a coil with the final thickness required by customer. It involves controlled and uncontrolled variables, which interact with one another, thus affecting the response variable, i..e, product´s final thickness. Some of the fundamental questions to be asked include: How do controlled variables affect response variable? What are the most influential ones? Are there interactions among such variables? Is it possible to work out an appropriate model to address this problem? The fragmented factor planning technique provides answers to the questions raised, by breaking new grounds in terms of using this powerful tool in manufacturing processes , where situations similar to those suggested by this study are fairly common. It includes a description of the problem, a bibliography review with the required steps to conduct the experiment planning, choice of controlled variables which make up the experiment as well as its execution, result analysis, an approach to Generalized Linear Models, and validation of model by adjusting process analysis as shown in the result analysis. Applying this methodology and implementing the proposed changes bolstered the process in such a way that even when disturbances of uncontrolled variables occur the thickness remains within the specified tolerances. Additionally, a further reduction in variation ranging from ± 1.5% to ± 1.0% was achieved, where technology investment would be required, thereby making steel products more expensive and less competitive against other competing products, thus ensuring competitiveness.

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Dai, Siyu S. M. Massachusetts Institute of Technology. « Probabilistic motion planning and optimization incorporating chance constraints ». Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120230.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 201-208).
For high-dimensional robots, motion planning is still a challenging problem, especially for manipulators mounted to underwater vehicles or human support robots where uncertainties and risks of plan failure can have severe impact. However, existing risk-aware planners mostly focus on low-dimensional planning tasks, meanwhile planners that can account for uncertainties and react fast in high degree-of-freedom (DOF) robot planning tasks are lacking. In this thesis, a risk-aware motion planning and execution system called Probabilistic Chekov (p-Chekov) is introduced, which includes a deterministic stage and a risk-aware stage. A systematic set of experiments on existing motion planners as well as p-Chekov is also presented. The deterministic stage of p-Chekov leverages the recent advances in obstacle-aware trajectory optimization to improve the original tube-based-roadmap Chekov planner. Through experiments in 4 common application scenarios with 5000 test cases each, we show that using sampling-based planners alone on high DOF robots can not achieve a high enough reaction speed, whereas the popular trajectory optimizer TrajOpt with naive straight-line seed trajectories has very high collision rate despite its high planning speed. To the best of our knowledge, this is the first work that presents such a systematic and comprehensive evaluation of state-of-the-art motion planners, which are based on a significant amount of experiments. We then combine different stand-alone planners with trajectory optimization. The results show that the deterministic planning part of p-Chekov, which combines a roadmap approach that caches the all pair shortest paths solutions and an online obstacle-aware trajectory optimizer, provides superior performance over other standard sampling-based planners' combinations. Simulation results show that, in typical real-life applications, this "roadmap + TrajOpt" approach takes about 1 s to plan and the failure rate of its solutions is under 1%. The risk-aware stage of p-Chekov accounts for chance constraints through state probability distribution and collision probability estimation. Based on the deterministic Chekov planner, p-Chekov incorporates a linear-quadratic Gaussian motion planning (LQG-MP) approach into robot state probability distribution estimation, applies quadrature-sampling theories to collision risk estimation, and adapts risk allocation approaches for chance constraint satisfaction. It overcomes existing risk-aware planners' limitation in real-time motion planning tasks with high-DOF robots in 3- dimensional non-convex environments. The experimental results in this thesis show that this new risk-aware motion planning and execution system can effectively reduce collision risk and satisfy chance constraints in typical real-world planning scenarios for high-DOF robots. This thesis makes the following three main contributions: (1) a systematic evaluation of several state-of-the-art motion planners in realistic planning scenarios, including popular sampling-based motion planners and trajectory optimization type motion planners, (2) the establishment of a "roadmap + TrajOpt" deterministic motion planning system that shows superior performance in many practical planning tasks in terms of solution feasibility, optimality and reaction time, and (3) the development of a risk-aware motion planning and execution system that can handle high-DOF robotic planning tasks in 3-dimensional non-convex environments.
by Siyu Dai.
S.M.

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Kang, Sheng. « Optimization for recipe-based, diet-planning inventory management ». Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61895.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 40-41).
This thesis presents a new modeling framework and research methodology for the study of recipe-based, diet-planning inventory management. The thesis begins with an exploration on the classic optimization problem - the diet problem based upon mixed-integer linear programming. Then, considering the fact that real diet-planning is sophisticated as it would be planning recipes rather than possible raw materials for the meals. Hence, the thesis develops the modeling framework under the assumption that given the recipes and the different purchasing options for raw materials listed in the recipes, examine the nutrition facts and calculate the purchasing decisions and the yearly optimal minimum cost for food consumption. This thesis further discusses the scenarios for different groups of raw materials in terms of shelf-timing difference. To model this inventory management, the modeling implementation includes preprocess part and the optimization part: the formal part involves with conversion of customized selection to quantitative relation with stored recipes and measurement on nutrition factors; the latter part solves the cost optimization problem.
by Sheng Kang.
S.M.

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Oines, Asa (Asa J. ). « Data visualization and optimization techniques for urban planning ». Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100646.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 69-71).
In this thesis we describe a number of data visualization and optimization techniques for urban planning. Many of these techniques originate from contributions to the Social Computing Group's "You Are Here" project, which publishes maps intended to be viewed as a blend between art and urban planning tools. In particular these maps and this thesis focus on the topics of education and transportation. Eventually we hope to evolve these maps into social technologies that make it easier for communities to create the change they seek.
by Asa Oines.
M. Eng.

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Sukthong, Jarturun. « Improvement potential for fuel planning optimization using BoFiT ». Thesis, KTH, Kraft- och värmeteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292755.

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In 2018, Stockholm Exergi, the largest district heating producer in Stockholm, used around 36% of imported fuel from other countries. With a large amount of imported fuel, fuel management is taking a vital part for Stockholm Exergi to ensure district heating supply stability. To manage fuel planning, Stockholm Exergi will calculate fuel demand forecasting for the next three years by using an optimization tool called “BoFiT”. But high optimization time is the main issue for the current midterm model. More than six hours are spent on regular model optimization. Therefore, midterm model optimization is typically run overnight. If there is an error occurring while the model is running, more time might be lost. From a preliminary study on the midterm model optimization time, the time spent in the calculation process is accounted for 80% of the total optimization time. In BoFiT, the midterm visual model is transformed into the mathematical model to solve for optimum results. The idea of decreasing the midterm model optimization time is the activation of LP relaxation in BoFiT. With LP relaxation activation, the computation time in the calculation process will be minimized. However, the usage of LP relaxation is giving some of the consequences to the optimization results. Based on the study of the midterm model optimization time, the usage of LP relaxation can decrease the time spent in the calculation process for 67.72%. When considering the overall optimization time, 48.60% of the optimization time was reduced by LP relaxation activation. The statistical analysis in 2018 BoFiT optimization showed that results from relaxed optimization have slightly lower accuracy than the results from normal optimization in heat and electricity production. However, the relaxed optimization results in fuel consumption are considered comparable for most of the fuel except by fossil oils. For midterm optimization, heat and electricity production forecasting from normal optimization and relaxed optimization are comparable. For fuel demand forecasting, fossil oils give the most significant different results in terms of forecasting analysis. However, the wood chip has the most considerable difference in the demand for midterm fuel planning. The result from relaxed optimization showed that it requires 305.89 GWh more than that from normal optimization. With a vast amount of wood chip consumption, it could have high effects on the wood chip preparation and storage for Stockholm Exergi.

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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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Singh, Daljeet. « Path Planning and Evolutionary Optimization of Wheeled Robots ». Cleveland State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=csu1375744528.

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Aghighi, Meysam. « Computational Complexity of some Optimization Problems in Planning ». Doctoral thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136280.

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Automated planning is known to be computationally hard in the general case. Propositional planning is PSPACE-complete and first-order planning is undecidable. One method for analyzing the computational complexity of planning is to study restricted subsets of planning instances, with the aim of differentiating instances with varying complexity. We use this methodology for studying the computational complexity of planning. Finding new tractable (i.e. polynomial-time solvable) problems has been a particularly important goal for researchers in the area. The reason behind this is not only to differentiate between easy and hard planning instances, but also to use polynomial-time solvable instances in order to construct better heuristic functions and improve planners. We identify a new class of tractable cost-optimal planning instances by restricting the causal graph. We study the computational complexity of oversubscription planning (such as the net-benefit problem) under various restrictions and reveal strong connections with classical planning. Inspired by this, we present a method for compiling oversubscription planning problems into the ordinary plan existence problem. We further study the parameterized complexity of cost-optimal and net-benefit planning under the same restrictions and show that the choice of numeric domain for the action costs has a great impact on the parameterized complexity. We finally consider the parameterized complexity of certain problems related to partial-order planning. In some applications, less restricted plans than total-order plans are needed. Therefore, a partial-order plan is being used instead. When dealing with partial-order plans, one important question is how to achieve optimal partial order plans, i.e. having the highest degree of freedom according to some notion of flexibility. We study several optimization problems for partial-order plans, such as finding a minimum deordering or reordering, and finding the minimum parallel execution length.

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Sjölund, Jens. « MRI based radiotherapy planning and pulse sequence optimization ». Licentiate thesis, Linköpings universitet, Medicinsk informatik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-115796.

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Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential. Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning algorithm to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs. Cancerous tissue has a dierent structure from normal tissue. This affects molecular diusion, which can be measured using MRI. The prototypical diusion encoding sequence has recently been challenged with the introduction of more general waveforms. To take full advantage of their capabilities it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints.

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Lee, Moon-bor Bob, et 李滿坡. « Optimization of highway networks ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31952677.

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Wheeler, Michael G. « Navy officer manpower optimization revisited ». Thesis, Monterey, California : Naval Postgraduate School, 2010. http://edocs.nps.edu/npspubs/scholarly/theses/2010/Mar/10Mar%5FWheeler.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, March 2010.
Thesis Advisor(s): Salmeron, Javier. Second Reader: Burton, Douglas. "March 2010." Description based on title screen as viewed on April 26, 2010. Author(s) subject terms: Navy Officer Manpower Planning, Manpower Optimization, Non-linear Optimization, Manpower Modeling, Program Objective Memorandum, POM, Program Review, PR. Includes bibliographical references (p. 75-77). Also available in print.

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Johnson, Anne Elizabeth Catlin. « Integrated Airline Planning Models ». Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1089%5F1%5Fm.pdf&type=application/pdf.

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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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Yuan, Wei. « Reliable Power System Planning and Operations through Robust Optimization ». Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5807.

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In this dissertation, we introduce and study robust optimization models and decomposition algorithms in order to deal with the uncertainties such as terrorist attacks, natural disasters, and uncertain demand that are becoming more and more signicant in power systems operation and planning. An optimal power grid hardening problem is presented as a defender-attacker-defender (DAD) sequential game and solved by an exact decomposition algorithm. Network topology control, which is an eective corrective measure in power systems, is then incorporated into the defender-attacker-defender model as a recourse operation for the power system operator after a terrorist attack. Computational results validate the cost-eectiveness of the novel model. In addition, a resilient distribution network planning problem (RDNP) is proposed in order to coordinate the hardening and distributed generation resource placement with the objective of minimizing the distribution system damage under uncertain natural disaster events. A multi-stage and multi-zone based uncertainty set is designed to capture the spatial and temporal dynamics of a natural disaster as an extension to the N-K worst-case network interdiction approach. Finally, a power market day-ahead generation scheduling problem, i.e., robust unit commitment (RUC) problem, that takes account of uncertain demand is analyzed. Improvements have been made in achieving a fast

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Muhandiramge, Ranga. « Maritime manoeuvring optimization : path planning in minefield threat environments ». University of Western Australia. School of Mathematics and Statistics, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0015.

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The aim of the research project that is the subject of this thesis is to apply mathematical techniques, especially those in the area of operations research, to the problem of maritime minefield transit. We develop several minefield models applicable to different aspects of the minefield problem. These include optimal mine clearance, shortest time traversal and time constrained traversal. We hope the suite of models and tools developed will help make mine field clearance and traversal both safer and more efficient and that exposition of the models will bring a clearer understanding of the mine problem from a mathematical perspective. In developing the solutions to mine field models, extensive use is made of network path planning algorithms, particularly the Weight Constrained Shortest Path Problem (WCSPP) for which the current state-of-the-art algorithm is extended. This is done by closer integration of Lagrangean relaxation and preprocessing to reduce the size of the network. This is then integrated with gap-closing algorithms based on enumeration to provide optimal or near optimal solutions to the path planning problem. We provide extensive computational evidence on the performance of our algorithm and compare it to other algorithms found in the literature. This tool then became fundamental in solving various separate minefield models. Our models can be broadly separated into obstacle models in which mine affected regions are treated as obstacles to be avoided and continuous threat in which each point of space has an associated risk. In the later case, we wish to find a path that minimizes the integral of the risk along the path while constraining the length of the path. We call this the Continuous Euclidean Length Constrained Minimum Cost Path Problem (C-LCMCPP), for which we present a novel network approach to solving this continuous problem. This approach results in being able to calculate a global lower bound on a non-convex optimization problem.

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Kinscherff, Anika [Verfasser]. « Robust Optimization and Evacuation Planning with Bridges / Anika Kinscherff ». München : Verlag Dr. Hut, 2018. http://d-nb.info/117442673X/34.

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Al-Agha, A. A. H. « The use of optimization techniques in national economic planning ». Thesis, Swansea University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635652.

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Risberg, Daniel. « Robust Optimization in Seasonal Planning of Hydro Power Plants ». Thesis, KTH, Optimeringslära och systemteori, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-173478.

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Hydro power producers are faced with the task of releasing water from the reservoirs in the right time. To do this there are tools using stochastic optimization that aims at maximizing the income of that producer. The existing methods have a high computing time and grow exponentially with the size of the problem. A new approach that uses linear decision rules is investigated in this thesis to see if it is possible to maintain the same quality of the solutions and in the same time decrease run times. With this method the hydro power producer receives policies as an affine function of the realization of the uncertainty variables in inflow and price. This thesis presents a deterministic model and then converts it into an linear decision rules, LDR, model. It also presents a way to model the uncertainty in both inflow to the reservoir and the spot price. The result is that the LDR approach generates reasonable policies with low run times but loses a lot of optimality compared to solutions that are used today. Therefore it is concluded that this approach needs further development before commercial use. The work described in this thesis has been done in cooperation with three master students at NTNU. The approach of using linear decision rules are the same in the two projects but the differences are the models evaluated.

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Hong, Zhaofu. « Optimization of production planning and emission-reduction policy-making ». Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-00978457.

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This research focuses on carbon emission-reduction issues in an area where the government imposes emission-reduction policies on local manufacturers. Policymaking problems for the government and production planning problems for the manufacturers are investigated with Operations Research/Management Science (OR/MS) approaches. Two types of emission-reduction policies, including emission-cap regulation policy and emission cap-and-trade scheme, are addressed. We first discuss manufacturers' long-term strategic decision problem under the government-imposed emission-cap regulation policy. With the objective of maximizing the manufacturers' profits, Stackelberg game model is formulated to optimize their decisions on carbon footprint, wholesale price and retailer selection. The problem is proven to be NP hard and a hybrid algorithm is developed to solve the model. We then investigate manufacturers' medium-term production planning to minimize the total production and inventory holding cost, by considering emission-reduction constraints through technology selection, some of the technologies being green. The problems are shown to be polynomially solvable. Based on these results, we study the government's policymaking problems to maximize the social welfare of the area. Stackelberg game models are formulated to optimize the emission-reduction policies by anticipating manufacturers' operational decisions in response to the governmental policies. Hybrid algorithms are developed to solve the problems. For each studied problem, numerical analyses are conducted to evaluate the algorithms. The computation results show that the algorithms developed in this research are effective. Some interesting and valuable managerial insights are drawn from computational results and sensitivity analyses.

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Gopalakrishnan, Srimathy. « Optimization models for production planning in metal sheet manufacturing ». Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/36427.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
Includes bibliographical references (leaves 84-85).
by Srimathy Gopalakrishnan.
M.S.

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Mogensen, Matthew D. (Matthew David). « Service network design optimization for Army Aviation lift planning ». Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90066.

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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 145-146).
The need for optimized aviation lift planning is becoming increasingly important as the United States and her allies participate in the Global War on Terror (GWOT). As part of a comprehensive effort, our nation's fighting forces find themselves conducting operations around the globe, with this trend likely to increase, even as budget constraints limit the number of personnel and amount of equipment that is deployed. While much attention has been given to airline schedule optimization and fleet planning. the challenge of Army Aviation lift planning is unique in that it must be able to adapt to changing requirements and missions on a daily basis. In this thesis, we model Army Aviation lift planning as a service network design problem, and propose two heuristic algorithms, which compare favorably to current human planning systems. Furthermore, we apply these heuristic algorithms to long term asset planning and capacity requirement estimation for future military scenarios, and analyze how passenger flexibility affects the need for capacity.
by Matthew D. Mogensen.
S.M. in Transportation

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Hadfield-Menell, Dylan. « Execution cost optimization for hierarchical planning in the now ». Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85421.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 73-75).
For robots to effectively interact with the real world, they will need to perform complex tasks over long time horizons. This is a daunting challenge, but human ability to routinely solve these problems leads us to believe that there is underlying structure we can leverage to find solutions. Recent advances using hierarchical planning [19] have been able to solve these problems by breaking a single long-horizon problem into several short-horizon problems. While this approach is able to effectively solve real world robotics planning problems, it makes no effort to account for the execution cost of an abstract plan and often arrives at poor quality plans. In this thesis, we analyze situations that lead to execution cost inefficiencies in hierarchical planners. We argue that standard optimization techniques from flat planning or search are likely to be ineffective in addressing these issues. We outline an algorithm, RCHPN, that improves a hierarchical plan by considering peephole optimizations during execution. We frame the underlying question as one of evaluating the resource needs of an abstract operator and propose a general way to approach estimating them. We introduce the marsupial logistics domain to study the effectiveness of this approach. We present experiments in large problem instances from marsupial logistics and observed up to 30% reduction in execution cost when compared with a standard hierarchical planner.
by Dylan Hadfield-Menell.
M. Eng.

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Jennings, Mark. « Optimization models for resource planning in urban energy systems ». Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24842.

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Resource planning at urban scale has received significant scrutiny in recent years. What has been paid less attention is whether existing tools such as optimization models can play a role in helping with this planning. The reason for the historical lack of interest is argued as due to both insufficient computing capability and practical modelling difficulties. The proposition put forward here is that optimization software can work with large distributed systems associated with urban resources. Resources considered include inter alia capital, employees, and technologies. To help support the contention, a mixed integer linear program and a nonlinear program have been formulated and tested. RESCOM and LAPM are optimization models developed in this research to make decisions on urban scale at the concept stage of design. The focus is on residential energy retrofits and workforce allocation for technology deployment respectively. Solutions are described to the problems encountered. Obstacles include use of available data, modelling interventions in a computationally efficient manner, and modelling thermal energy requirements as dependent on both temperature and thermal transmittance. Real world case studies in the London Borough of Haringey demonstrate the functionalities of the models. It is proposed that it is the structure rather than the objective function's value which should be analysed. Three methods are employed to test the resilience of solution structures as follows: i) scenario analysis, ii) generation of near-optimal solution alternatives, and iii) uncertainty analysis. The detailed analysis of the results is an important - but often omitted - step yielding interesting findings. Yet the insights must be considered with respect to the scope and assumptions within the models. Notwithstanding, RESCOM and LAPM carefully combine modern developments in the literature with practical real-world constraints to provide novel contributions to existing knowledge. The hypothesis put forward as a result of the research described in this work is that optimization software can be successfully employed for modelling resources planning at the large scale.

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Peters, Jorrit. « Dynamic Programming Heuristics for the Optimization of Hydropower Planning ». Thesis, KTH, Optimeringslära och systemteori, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188686.

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In short-term planning of a hydropower system one optimizes the choice of release of water and which generators to use. This problem is prone to Bellman's curse of dimensionality, since the amount of states increases exponentially with the amount of hydropower stations in the system. In this thesis, two different dynamic programming heuristics to this problem are derived, described and compared with a heuristic developed by Powel AS. A difficulty that extends the problem, is the addition of a power reserve on each time period. A third dynamic programming heuristic is developed to deal with a constraint on the power reserve and the parallel with knapsack problem is described. The new dynamic programming approaches perform similar to the heuristic by Powel AS. The third dynamic programming heuristic that deals with the reserve constraints, provides a better way of dealing with and fulfilling the constraint than currently in place by Powel AS.
För kortsiktig planering av vattenkraftsystem optimerar man mängden vatten som släpps på och valet av generatorer att använda. Det här problemet drabbas av Bellman's dimensionalitetens förbannelse då antalet tillstånd ökar exponentiellt med antalet vattenkraftverk i systemet. I detta examensarbete härleds två dynamiska programmeringsheuristiker som jämförs med en heuristik utvecklad av Powel AS. En aspekt som försvårar uppgiften är tillägget av en effektreserv för varje tidsperiod. En tredje dynamisk programmeringsheuristik utvecklas för att hantera restriktioner på effektreserven och parallellen till knapsack problemet beskrivs. De nya dynamisk programmeringstillvägagångsätten presterar likvärdigt med Powel AS heuristik. Den tredje dynamiska programmeringsheuristiken som hanterar reservbegräsningar erbjuder en bättre lösning för att hantera och uppfylla bivillkoren jämförelse med de som i nuläget används av Powel AS.

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Kamkin, Y. V. « MAINTENANCE INTERVALS PLANNING OPTIMIZATION OF NEW GENERATION AIRCRAFTS AVIONICS ». Thesis, Національний авіаційний університет, 2015. http://er.nau.edu.ua/handle/NAU/17208.

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One of the major reasons of aviation accident is unsuitable maintenance program. Nowadays maintenance steering group-3, the “Operator/Manufacturer Scheduled Maintenance Development Document” is used.

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Hellander, Anja. « Multi-Hypothesis Motion Planning under Uncertainty Using Local Optimization ». Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166582.

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Motion planning is defined as the problem of computing a feasible trajectory for an agent to follow. It is a well-studied problem with applications in fields such as robotics, control theory and artificial intelligence. In the last decade there has been an increased interest in algorithms for motion planning under uncertainty where the agent does not know the state of the environment due to, e.g. motion and sensing uncertainties. One approach is to generate an initial feasible trajectory using for example an algorithm such as RRT* and then improve that initial trajectory using local optimization. This thesis proposes a new modification of the RRT* algorithm that can be used to generate initial paths from which initial trajectories for the local optimization step can be generated. Unlike standard RRT*, the modified RRT* generates multiple paths at the same time, all belonging to different families of solutions (homotopy classes). Algorithms for motion planning under uncertainty that rely on local optimization of trajectories can use trajectories generated from these paths as initial solutions. The modified RRT* is implemented and its performance with respect to computation time and number of paths found is evaluated on simple scenarios. The evaluations show that the modified RRT* successfully computes solutions in multiple homotopy classes. Two methods for motion planning under uncertainty, Trajectory-optimized LQG (T-LQG), and a belief space variant of iterative LQG (iLQG) are implemented and combined with the modified RRT*. The performance with respect to cost function improvement, computation time and success rate when following the optimized trajectories for the two methods are evaluated in a simulation study. The results from the simulation studies show that it is advantageous to generate multiple initial trajectories. Some initial trajectories, due to for example passing through narrow passages or through areas with high uncertainties, can only be slightly improved by trajectory optimization or results in trajectories that are hard to follow or with a high collision risk. If multiple initial trajectories are generated the probability is higher that at least one of them will result in an optimized trajectory that is easy to follow, with lower uncertainty and lower collision risk than the initial trajectory. The results also show that iLQG is much more computationally expensive than T-LQG, but that it is better at computing control policies to follow the optimized trajectories.

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Balabanska, Nadya L. « Motion planning with dynamic constraints through pose graph optimization ». Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129129.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 20-21).
This contribution is an optimization-based method for robotic path-planning that is able to recover vehicle controls in addition to discovering an optimized, feasible trajectory from start to goal for vehicles with arbitrary dynamics. The motion planner extends the application of factor-graph optimization commonly used in simultaneous localization and mapping tasks to the path-planning task, specifically the "timed elastic band" trajectory optimization approach [1] for control input extraction functionality. This is achieved by the introduction of control input-dependent vertices into the factor-graph along with a way to systematically design dynamics violation costs without relying on hand-picked geometric parameters. An implementation of the planner successfully recovers vehicle control inputs and produces feasible trajectories in simulation testing.
by Nadya L. Balabanska.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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Lao, Beyer Lukas C. « Multi-modal motion planning using composite pose graph optimization ». Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130697.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 30-31).
This work presents a motion planning framework for multi-modal vehicle dynamics. An approach for transcribing cost function, vehicle dynamics, and state and control constraints into a sparse factor graph is introduced. By formulating the motion planning problem in pose graph form, the motion planning problem can be addressed using efficient optimization techniques, similar to those already widely applied in dual estimation problems, e.g., pose graph optimization for simultaneous localization and mapping (SLAM). Optimization of trajectories for vehicles under various dynamics models is demonstrated. The motion planner is able to optimize the location of mode transitions, and is guided by the pose graph optimization process to eliminate unnecessary mode transitions, enabling efficient discovery of optimized mode sequences from rough initial guesses. This functionality is demonstrated by using our planner to optimize multi-modal trajectories for vehicles such as an airplane which can both taxi on the ground or fly. Extensive experiments validate the use of the proposed motion planning framework in both simulation and real-life flight experiments using a vertical take-off and landing (VTOL) fixed-wing aircraft that can transition between hover and horizontal flight modes.
by Lukas C. Lao Beyer.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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Chono, Shunsuke. « Optimization of Crop Planning for Nonpoint Source Pollution Management ». Kyoto University, 2012. http://hdl.handle.net/2433/157691.

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Kyoto University (京都大学)
0048
新制・課程博士
博士(農学)
甲第16900号
農博第1916号
新制||農||997(附属図書館)
学位論文||H24||N4661(農学部図書室)
29575
京都大学大学院農学研究科地域環境科学専攻
(主査)教授河地利彦, 教授村上章, 教授川島茂人
学位規則第4条第1項該当

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Thomas, Adam J. « Tri-level optimization for anti-submarine warfare mission planning ». Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://edocs.nps.edu/npspubs/scholarly/theses/2008/Sept/08Sep%5FThomas.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2008.
Thesis Advisor(s): Brown, Gerald G. ; Wood, R. Kevin. "September 2008." Description based on title screen as viewed on November 5, 2008. Includes bibliographical references (p. 53-54). Also available in print.

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Turke, Ulrich. « Efficient methods for WCDMA radio network planning and optimization ». Wiesbaden : Dt. Univ.-Verl, 2007. https://www.lib.umn.edu/slog.phtml?url=http://www.myilibrary.com?id=134460.

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Thèses sur le sujet « Planning Optimization »

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