Relevant bibliographies by topics / Planning optimisation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Planning optimisation'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Planning optimisation"

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Gerth, Hans-Peter. "Optimisation of Assembly Planning." ATZproduktion worldwide eMagazine 4, no. 3 (September 1, 2011): 10–15. http://dx.doi.org/10.1365/s38312-011-0019-7.

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Altobelli, Emma, Maurizio Amichetti, Alessio Langiu, Francesca Marzi, Filippo Mignosi, Pietro Pisciotta, Giuseppe Placidi, Fabrizio Rossi, Giorgio Russo, and Marco Schwarz. "Combinatorial optimisation in radiotherapy treatment planning." AIMS Medical Science 5, no. 3 (2018): 204–23. http://dx.doi.org/10.3934/medsci.2018.3.204.

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Bushuev, Maxim. "Convex optimisation for aggregate production planning." International Journal of Production Research 52, no. 4 (August 28, 2013): 1050–58. http://dx.doi.org/10.1080/00207543.2013.831998.

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McGowan, S. E., N. G. Burnet, and A. J. Lomax. "Treatment planning optimisation in proton therapy." British Journal of Radiology 86, no. 1021 (January 2013): 20120288. http://dx.doi.org/10.1259/bjr.20120288.

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Bland, J. A. "Space-planning by ant colony optimisation." International Journal of Computer Applications in Technology 12, no. 6 (1999): 320. http://dx.doi.org/10.1504/ijcat.1999.000215.

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Noonan, John, and Michael Wallace. "Improved optimisation through advanced relationship planning." Supply Chain Management: An International Journal 11, no. 6 (November 2006): 483–90. http://dx.doi.org/10.1108/13598540610703873.

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Napalkova, Liana, and Galina Merkuryeva. "MULTI-OBJECTIVE STOCHASTIC SIMULATION-BASED OPTIMISATION APPLIED TO SUPPLY CHAIN PLANNING." Technological and Economic Development of Economy 18, no. 1 (April 10, 2012): 132–48. http://dx.doi.org/10.3846/20294913.2012.661190.

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The paper discusses the optimisation of complex management processes, which allows the reduction of investment costs by setting the optimal balance between product demand and supply. The systematisation of existing methods and algorithms that are used to optimise complex processes by linking stochastic discrete-event simulation and multi-objective optimisation is given. The two-phase optimisation method is developed based on hybrid combination of compromise programming, evolutionary computation and response surface-based methods. Approbation of the proposed method is performed on the multi-echelon supply chain planning problem that is widely distributed in industry and its solution plays a vital role in increasing the competitiveness of a company. Three scenarios are implemented to optimise supply chain tactical planning processes at the chemical manufacturing company based on using different optimisation methods and software. The numerical results prove the competitive advantages of the developed two-phase optimisation method.
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Yu, Dexin, Xuesong Zhang, Guangdong Tian, Zhigang Jiang, Zhiming Liu, Tiangang Qiang, and Changshu Zhan. "Disassembly Sequence Planning for Green Remanufacturing Using an Improved Whale Optimisation Algorithm." Processes 10, no. 10 (October 3, 2022): 1998. http://dx.doi.org/10.3390/pr10101998.

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Currently, practical optimisation models and intelligent solution algorithms for solving disassembly sequence planning are attracting more and more attention. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this paper proposes a new optimisation model for the energy-efficient disassembly sequence planning. The minimum energy consumption is used as the evaluation criterion for disassembly efficiency, so as to minimise the energy consumption during the dismantling process. As the proposed model is a complex optimization problem, called NP-hard, this study develops a new extension of the whale optimisation algorithm to allow it to solve discrete problems. The whale optimisation algorithm is a recently developed and successful meta-heuristic algorithm inspired by the behaviour of whales rounding up their prey. We have improved the whale optimisation algorithm for predation behaviour and added a local search strategy to improve its performance. The proposed algorithm is validated with a worm reducer example and compared with other state-of-the-art and recent metaheuristics. Finally, the results confirm the high solution quality and efficiency of the proposed improved whale algorithm.
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Ab Rashid, Mohd Fadzil Faisae, Windo Hutabarat, and Ashutosh Tiwari. "Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232, no. 8 (October 24, 2016): 1444–59. http://dx.doi.org/10.1177/0954405416673095.

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In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set.
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Hueber, Christian, Gudrun Fischer, Nikolaus Schwingshandl, and Ralf Schledjewski. "Production planning optimisation for composite aerospace manufacturing." International Journal of Production Research 57, no. 18 (December 20, 2018): 5857–73. http://dx.doi.org/10.1080/00207543.2018.1554918.

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Dissertations / Theses on the topic "Planning optimisation"

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Dos, Santos De Oliveira Rafael. "Bayesian Optimisation for Planning under Uncertainty." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20762.

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Under an increasing demand for data to understand critical processes in our world, robots have become powerful tools to automatically gather data and interact with their environments. In this context, this thesis addresses planning problems where limited prior information leads to uncertainty about the outcomes of a robot's decisions. The methods are based on Bayesian optimisation (BO), which provides a framework to solve planning problems under uncertainty by means of probabilistic modelling. As a first contribution, the thesis provides a method to find energy-efficient paths over unknown terrains. The method applies a Gaussian process (GP) model to learn online how a robot's power consumption varies as a function of its configuration while moving over the terrain. BO is applied to optimise trajectories over the GP model being learnt so that they are informative and energetically efficient. The method was tested in experiments on simulated and physical environments. A second contribution addresses the problem of policy search in high-dimensional parameter spaces. To deal with high dimensionality the method combines BO with a coordinate-descent scheme that greatly improves BO's performance when compared to conventional approaches. The method was applied to optimise a control policy for a race car in a simulated environment and shown to outperform other optimisation approaches. Finally, the thesis provides two methods to address planning problems involving uncertainty in the inputs space. The first method is applied to actively learn terrain roughness models via proprioceptive sensing with a mobile robot under localisation uncertainty. Experiments demonstrate the method's performance in both simulations and a physical environment. The second method is derived for more general optimisation problems. In particular, this method is provided with theoretical guarantees and empirical performance comparisons against other approaches in simulated environments.
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Morere, Philippe. "Bayesian Optimisation for Planning And Reinforcement Learning." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21230.

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This thesis addresses the problem of achieving efficient non-myopic decision making by explicitly balancing exploration and exploitation. Decision making, both in planning and reinforcement learning (RL), enables agents or robots to complete tasks by acting on their environments. Complexity arises when completing objectives requires sacrificing short-term performance in order to achieve better long-term performance. Decision making algorithms with this characteristic are known as non-myopic, and require long sequences of actions to be evaluated, thereby greatly increasing the search space size. Optimal behaviours need balance two key quantities: exploration and exploitation. Exploitation takes advantage of previously acquired information or high performing solutions, whereas exploration focuses on acquiring more informative data. The balance between these quantities is crucial in both RL and planning. This thesis brings the following contributions: Firstly, a reward function trading off exploration and exploitation of gradients for sequential planning is proposed. It is based on Bayesian optimisation (BO) and is combined to a non-myopic planner to achieve efficient spatial monitoring. Secondly, the algorithm is extended to continuous actions spaces, called continuous belief tree search (CBTS), and uses BO to dynamically sample actions within a tree search, balancing high-performing actions and novelty. Finally, the framework is extended to RL, for which a multi-objective methodology for explicit exploration and exploitation balance is proposed. The two objectives are modelled explicitly and balanced at a policy level, as in BO. This allows for online exploration strategies, as well as a data-efficient model-free RL algorithm achieving exploration by minimising the uncertainty of Q-values (EMU-Q). The proposed algorithms are evaluated on different simulated and real-world robotics problems, displaying superior performance in terms of sample efficiency and exploration.
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Marchant, Matus Roman. "Bayesian Optimisation for Planning in Dynamic Environments." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14497.

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This thesis addresses the problem of trajectory planning for monitoring extreme values of an environmental phenomenon that changes in space and time. The most relevant case study corresponds to environmental monitoring using an autonomous mobile robot for air, water and land pollution monitoring. Since the dynamics of the phenomenon are initially unknown, the planning algorithm needs to satisfy two objectives simultaneously: 1) Learn and predict spatial-temporal patterns and, 2) find areas of interest (e.g. high pollution), addressing the exploration-exploitation trade-off. Consequently, the thesis brings the following contributions: Firstly, it applies and formulates Bayesian Optimisation (BO) to planning in robotics. By maintaining a Gaussian Process (GP) model of the environmental phenomenon the planning algorithms are able to learn the spatial and temporal patterns. A new family of acquisition functions which consider the position of the robot is proposed, allowing an efficient trajectory planning. Secondly, BO is generalised for optimisation over continuous paths, not only determining where and when to sample, but also how to get there. Under these new circumstances, the optimisation of the acquisition function for each iteration of the BO algorithm becomes costly, thus a second layer of BO is included in order to effectively reduce the number of iterations. Finally, this thesis presents Sequential Bayesian Optimisation (SBO), which is a generalisation of the plain BO algorithm with the goal of achieving non-myopic trajectory planning. SBO is formulated under a Partially Observable Markov Decision Process (POMDP) framework, which can find the optimal decision for a sequence of actions with their respective outcomes. An online solution of the POMDP based on Monte Carlo Tree Search (MCTS) allows an efficient search of the optimal action for multistep lookahead. The proposed planning algorithms are evaluated under different scenarios. Experiments on large scale ozone pollution monitoring and indoor light intensity monitoring are conducted for simulated and real robots. The results show the advantages of planning over continuous paths and also demonstrate the benefit of deeper search strategies using SBO.
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Dowie, Tracy Ann. "A disassembly planning and optimisation methodology for design." Thesis, Manchester Metropolitan University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283082.

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Gregory, Christine. "Robust optimisation and its application to portfolio planning." Thesis, Brunel University, 2009. http://bura.brunel.ac.uk/handle/2438/3638.

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Decision making under uncertainty presents major challenges from both modelling and solution methods perspectives. The need for stochastic optimisation methods is widely recognised; however, compromises typically have to be made in order to develop computationally tractable models. Robust optimisation is a practical alternative to stochastic optimisation approaches, particularly suited for problems in which parameter values are unknown and variable. In this thesis, we review robust optimisation, in which parameter uncertainty is defined by budgeted polyhedral uncertainty sets as opposed to ellipsoidal sets, and consider its application to portfolio selection. The modelling of parameter uncertainty within a robust optimisation framework, in terms of structure and scale, and the use of uncertainty sets is examined in detail. We investigate the effect of different definitions of the bounds on the uncertainty sets. An interpretation of the robust counterpart from a min-max perspective, as applied to portfolio selection, is given. We propose an extension of the robust portfolio selection model, which includes a buy-in threshold and an upper limit on cardinality. We investigate the application of robust optimisation to portfolio selection through an extensive empirical investigation of cost, robustness and performance with respect to risk-adjusted return measures and worst case portfolio returns. We present new insights into modelling uncertainty and the properties of robust optimal decisions and model parameters. Our experimental results, in the application of portfolio selection, show that robust solutions come at a cost, but in exchange for a guaranteed probability of optimality on the objective function value, significantly greater achieved robustness, and generally better realisations under worst case scenarios.
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Dimopoulos, Christos. "Genetic programming for manufacturing optimisation." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327668.

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Rowbottom, Carl Graham. "Optimisation of beam-orientations in conformal radiotherapy treatment planning." Thesis, Institute of Cancer Research (University Of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314088.

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Haas, O. C. L. "Optimisation and control systems modelling in radiotherapy treatment planning." Thesis, Coventry University, 1997. http://curve.coventry.ac.uk/open/items/fc7aad2f-a43a-4045-adb0-9afa7e9033b3/1.

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Khwandah, Sinan. "System optimisation and radio planning for future LTE-advanced." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/13822.

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This work is related to wireless communication. In this Thesis three main issues are addressed for future cellular networks: power consumption, interference and mobility. These issues continue to be a burden on the system’s performance as long as technology keeps evolving. In the presented chapters, the focus was to introduce greater intelligence to the LTE system algorithms and bring to them a dynamic and self-organizing approach. The first approach concerns power consumption in wireless terminals. The currently applied solution to save energy is the DRX mechanism. It organizes the time when the terminal wakes up and starts receiving data, and when it goes into sleep mode in order to save its battery power. The current DRX is described as static or fixed which makes its parameters unsuitable for the nature of the bursty traffic. In this work an adaptive DRX mechanism is proposed and evaluated as the wireless terminal battery saving algorithm. The second approach is co-channel interference mitigation. To increase the system’s capacity and avoid spectrum scarcity, small cells such as Femtocells are deployed and operate on the same frequency bands as the Macrocell. Although these small nodes increase the system capacity, however, the challenges will be in the femtocells planning and management in addition to the interference issues. Here a dynamic interference cancellation approach is presented to enable the Femtocell to track the allocated resources to the Macro-users, and to avoid using them. The third approach concerns mobility management in heterogeneous networks. The wireless terminal may have different mobility levels during handover which increases the handover failures due to failure in handover commands and aging of the reported parameters. This issue is presented in detail with the aim to avoid performance degradation and improve the reporting mechanisms during fast mobility levels. For this regard the presented method proposes more cooperation between the serving cell and the end-user so that the large amount of overhead and measurement are reduced. Simulations with different configurations are conducted to present the results of the proposed models. Results show that the proposed models bring improvements to the LTE system. The enhanced self-organized architecture in the three presented approaches performs well in terms of power saving, dynamic spectrum utilization by Femtocells, and mitigation of sudden throughput degradation due to the serving cell’s downlink signal outage during mobility.
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Garmabdari, Rasoul. "Multi-Energy Microgrid Systems Planning and Energy Management Optimisation." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/398878.

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Conventional power systems are predominantly composed of centralised large-scale generation sites that provide electricity to a large number of customers in a top-down unidirectional fashion and through transmission and distribution networks. To increase penetration of Renewable Energy Resources (RES) into this traditional power system and promotion of Distributed Energy Resources (DER) concept as an effective solution to deal with the challenges being faced in the conventional power system such as the energy losses, peak demand, peak generation, the infrastructure depreciation, and environmental effect, Microgrid (MG) concept is introduced. MG is defined as a locally controlled small unit of the power system that usually is in interaction with the main grid and is regarded as the building blocks of future Smart Grids (SGs). However, these systems are also capable of operating independently and isolated from the main grid, particularly in remote areas where access to the main grid is impossible or there is a disruptive event on the power system. The emergence of cutting-edge advances in the energy conversion and energy storage technologies and their commercial availability as well as introduction of various lucrative grid services that both grid and customers can benefit from derived the planners and engineers to further expand the flexibility, resilience and efficiency of MGs. To achieve this, Multi-Energy Microgrid System (MEMGS) concept as an expanded definition of MG was introduced to improve the efficiency of conventional energy systems, reduce air pollution as well as energy utilisation. MEMGS incorporates various energy technologies such as axillary boiler, gas turbine, RESs, thermal and battery energy storage systems that are fed by multiple energywares such as gas and electricity to supply multiple types of demands simultaneously such as electrical, heating and cooling loads. However, the integration of clusters of various technologies and concurrent delivery of different energy services causes additional complexities into the modelling and optimisation of these systems due to the potential interactions of energy vectors and various technologies at the consumer level. The economic viability of MGs and MEMGSs rely on the configuration and operating management of the technologies. Therefore, is a need to develop an effective and efficient planning framework that can handle the interaction complexities and nonlinearities of the system, determining the most appropriate architecture, selecting the energy conversion and energy storage technologies and energy supply alternatives from a candidate pool. This thesis aims at addressing these challenges by initially developing a comprehensive and accurate dynamic model for MGs and MGESs components, investigating the technical and economic aspects, the nonlinear behaviour, maintenance and degradation phenomena, and uncertainties associated with technologies through Mixed-Integer Linear Programming (MILP) and Mixed Integer Quadratic Programming (MIQP). Then the established model is employed to establish and propose a multi-objective linearised planning optimisation approach. The architecture and choice of equipment of MEMGSs involve various elements such as availability and costs of the energy sources and equipment, and characteristics of the energy demand. Considering these factors, the proposed strategy allocates the size of the components utilised in the MGs and EMMGSs while meeting the defined performance indices such as degradation factor, reliability and grid power fluctuations smoothing indices. Once, the configuration and capacity of components are optimally determined, efficient energy management is required. The last part of this research focuses on energy management system scheduling and optimisation where the EMS scheduling module for MGs and MEMGSs are inspected considering the Time of Use tariff, peak shaving and valley filling functions, degradation of energy storage devices, along with the operating criteria and cost of the energy conversion units. Moreover, a real-time EMS solution is provided to deal with intermittent behaviour of RESs while participating in arbitrage market. The real-time EMS manages the energy flow optimally according to the acquired real-time data and its deviation from the original schedule attained in the scheduling optimisation stage. The primary objective of the EMS module development is to maximise profit while improving the performance of the MEMGSs. Throughout this research, the MILP and MIQP optimisation approach is adopted to achieve a fast convergence while avoiding complexity and long computation time that would cause due to the nonlinear behaviour and complex interaction of the technologies. Finally, having a practical insight into the challenges and concerns with connection adjacent MGs in distribution networks, an efficient centralised EMS optimisation framework is proposed to cope with the limitations and optimise the performance of the system, considering power losses, voltage deviations and nonlinear degradation of the components. The primary objective of this section of research is to achieve the optimal techno-economic solution.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Eng & Built Env
Science, Environment, Engineering and Technology
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Books on the topic "Planning optimisation"

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Mishra, Ajay R., ed. Advanced Cellular Network Planning and Optimisation. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/9780470057629.

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Laiho, Jaana, Achim Wacker, and Tomá?? Novosad, eds. Radio Network Planning and Optimisation for UMTS. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/9780470031407.

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Mishra, Ajay R. Fundamentals of Cellular Network Planning and Optimisation. Chichester, UK: John Wiley & Sons, Ltd, 2004. http://dx.doi.org/10.1002/0470862696.

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Jaana, Laiho, Wacker Achim, and Novosad Tomáš, eds. Radio network planning and optimisation for UMTS. Hoboken, N.J: Wiley, 2005.

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Jaana, Laiho, Wacker Achim, and Novosad Tomáš, eds. Radio network planning and optimisation for UMTS. New York: Wiley, 2002.

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Nawrocki, Maciej J., Mischa Dohler, and A. Hamid Aghvami, eds. Understanding UMTS Radio Network Modelling, Planning and Automated Optimisation. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470030569.

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Mishra, Ajay R. Fundamentals of Network Planning and Optimisation 2G/3G/4G. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119331797.

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Institute of Materials, Minerals, and Mining., ed. Optimisation of manufacturing processes: A response surface approach. London: Maney for the Institute of Materials, Minerals and Mining, 2003.

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Commission, European, ed. Optimisation of policies for transport integration in metropolitan areas: Optima. Luxembourg: Office for Official Publications of the European Communities, 1998.

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Fundamentals of cellular network planning and optimisation: 2G, 2.5G, 3G-- evolution to 4G. Chichester: Wiley, 2004.

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Book chapters on the topic "Planning optimisation"

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O’Sullivan, Patrick, Gary D. Holtzclaw, and Gerald Barber. "Deterministic Optimisation." In Transport Network Planning, 100–114. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003182993-5.

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Monkhouse, P. H. L., and G. A. Yeates. "Beyond Naïve Optimisation." In Advances in Applied Strategic Mine Planning, 3–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69320-0_1.

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Kähkönen, Johanna, Nezha Larhrissi, Cameron Gillis, Mika Särkioja, Ajay R. Mishra, and Tarun Sharma. "Radio Network Planning and Optimisation." In Advanced Cellular Network Planning and Optimisation, 15–195. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/9780470057629.ch2.

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Mishra, Ajay R., and Jussi Viero. "Transmission Network Planning and Optimisation." In Advanced Cellular Network Planning and Optimisation, 197–314. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/9780470057629.ch3.

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Mungai, James, Sameer Mathur, Carlos Crespo, and Ajay R. Mishra. "Core Network Planning and Optimisation." In Advanced Cellular Network Planning and Optimisation, 315–415. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/9780470057629.ch4.

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Öhman, Karin. "Spatial Optimisation in Forest Planning." In Managing Forest Ecosystems, 153–72. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-015-9906-1_7.

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Mattina, Massimiliano. "Appendix D: TETRA Network Planning." In Advanced Cellular Network Planning and Optimisation, 493–506. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/9780470057629.app4.

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Erlebach, Karsten, Zbigniew Jóskiewicz, and Marcin Ney. "UTRAN Transmission Infrastructure Planning and Optimisation." In Understanding UMTS Radio Network Modelling, Planning and Automated Optimisation, 427–95. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470030569.ch17.

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Hazra, Joydeep. "Appendix A: Roll-Out Network Project Management." In Advanced Cellular Network Planning and Optimisation, 439–65. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/9780470057629.app1.

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Sánchez-Mejías, Rafael. "Appendix B: HSDPA." In Advanced Cellular Network Planning and Optimisation, 467–81. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/9780470057629.app2.

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Conference papers on the topic "Planning optimisation"

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Ting, Stephanie Yeo Ken, and Tiong Teck Chai. "WCDMA Network Planning and Optimisation." In 2nd Malaysia Conferenced on Photonics (MCP). IEEE, 2008. http://dx.doi.org/10.1109/nctt.2008.4814295.

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Syu, Wan-Syuan, and Jui-Yuan Lee. "Regional energy planning using mathematical optimisation." In 2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP). IEEE, 2017. http://dx.doi.org/10.1109/adconip.2017.7983813.

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Mueller, A., S. Linder, A. Saade, M. Jedliczka, M. de l’Epine, E. Willdbrett, E. Kjellsson, et al. "Urban planning measures for solar optimisation." In The Sustainable City 2012. Southampton, UK: WIT Press, 2012. http://dx.doi.org/10.2495/sc120562.

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Jamaa, S. B., Z. Altman, J. M. Picard, and B. Fourestie. "Combined coverage and capacity optimisation for UMTS networks." In 11th International Telecommunications Network Strategy and Planning Symposium. IEEE, 2004. http://dx.doi.org/10.1109/netwks.2004.240881.

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Ruiz, S., X. Colet, and J. J. Estevez. "Frequency planning optimisation in real mobile networks." In Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No.99CH36324). IEEE, 1999. http://dx.doi.org/10.1109/vetecf.1999.797304.

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Gill, T. "Radio planning and optimisation - the challenge ahead." In Fourth International Conference on 3G Mobile Communication Technologies. IEE, 2003. http://dx.doi.org/10.1049/cp:20030331.

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Marchant, Roman, and Fabio Ramos. "Bayesian Optimisation for informative continuous path planning." In 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014. http://dx.doi.org/10.1109/icra.2014.6907763.

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Davidson, Shelley Sylvia, Sander Matthijs Mos, and Susie Coppock. "Planning for a Sustainable Production Optimisation Solution." In SPE Intelligent Energy Conference and Exhibition. Society of Petroleum Engineers, 2010. http://dx.doi.org/10.2118/128674-ms.

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Allen, S. "Smart cell planning and optimisation for UMTS." In Fifth IEE International Conference on 3G Mobile Communication Technologies (3G 2004) The Premier Technical Conference for 3G and Beyond. IEE, 2004. http://dx.doi.org/10.1049/cp:20040630.

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Waldock, Antony, and David Corne. "Multiple objective optimisation applied to route planning." In the 13th annual conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001576.2001821.

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Reports on the topic "Planning optimisation"

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Lindquist, Joachim, and Henning de Haas. Creating Supply Chain Resilience Through Scenario Planning: How a Digital Twin Can Be Used To Enhance Supply Chain Resilience Through Scenario Planning. Aarhus University Library, 2021. http://dx.doi.org/10.7146/aul.435.

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This book focusses on the concept of supply chain disruptions and how supply chain resilience can contribute to both preparing for and reacting to the event causing disruption. For building a digital twin of a supply chain, a software named Supply Chain Guru has been used. The software is a supply chain design tool which can be used for different kinds of supply chain network optimisation. The book outlines four scenarios: Covid-19 lockdown, Brexit without deal, Conflagration at a dairy and Political regulations on transport. The scenarios all contain a problem that needs to be solved. This problem is considered as the main disruption for the supply chain. Running the scenario in Supply Chain Guru, constraints are added to the AS-IS model. The constraints are identified as implications of the event in the scenarios. By adding the constraints and running the model, Supply Chain Guru identifies suggestions to solve the problems which were described. The solutions within the scenarios are held up against the theory of supply chain resilience, to describe how the scenario planning can be used to enhance supply chain resilience. Finally, the book discuss how scenario planning can be related to supply chain resilience as well as how scenario planning can be used to increase supply chain resilience.
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Leidermark, Daniel, and Magnus Andersson, eds. Reports in Applied Mechanics 2022. Linköping University Electronic Press, February 2024. http://dx.doi.org/10.3384/9789180754156.

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This is the first volume of the concurring series of Reports in Applied Mechanics, which is based on the outcome of the advanced project course TMPM07 in Applied Mechanics at Link¨oping University during the autumn of 2022. The course lay-up is based on several industrial related projects within the field of Solid Mechanics, concerning fatigue, topology optimisation, structural dimensioning, contacts etc, and Fluid Mechanics, concerning fluid dynamics, flow, aerodynamics, heat transfer etc. The students tackle industry relevant projects in close collaboration with industry from near and neighbouring regions and work in project groups to solve the given tasks within the time limit of the course. Close collaboration with the industry is necessary to define planning, update and feedback for further evaluation at the industry. Three projects were performed during the course of 2022, two within Solid Mechanics and one in Fluid Mechanics. The projects were all performed in tight collaboration with industry partners, and had a close application to real industrial problems. A good opportunity for the students to show-off all their gained knowledge and apply in the best possible way to make innovative solutions in the respective projects. Something they all managed to do with success!
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Fent, Thomas, Stefan Wrzaczek, Gustav Feichtinger, and Andreas Novak. Fertility decline and age-structure in China and India. Verlag der Österreichischen Akademie der Wissenschaften, May 2024. http://dx.doi.org/10.1553/0x003f0d14.

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China and India, two Asian countries that experienced a rapid decline in fertility since the middle of the twentieth century, are the focus of this paper. Although there is no doubt that lower fertility levels have many positive effects on the economy, development and sustainability, little is known about the optimal transition from high to medium or even low levels of fertility. Firstly, implementing policies that have the potential to reduce fertility is costly. Secondly, additional costs arise from adapting the infrastructure to a population that fluctuates quickly not only in terms of size but also with respect to the age structure. We apply an intertemporal optimisation model that takes the costs and benefits of fertility decline into account. The optimal time path depends on the cost structure, the planning horizon and the initial conditions. In the case of a long planning horizon and high initial fertility, it may even be optimal to reduce fertility temporarily below replacement level in order to slow down population growth at an early stage. A key finding of our formal investigation is that, under the same plausible parameter settings, the optimal paths for China and India differ substantially. Moreover, our analysis shows that India, where the fertility decline emerged as a consequence of societal and economic developments, followed a path closer to the optimal fertility transition than China, where the fertility decline was state-imposed. The mathematical approach deployed for this analysis provides insights into the optimal long-term development of fertility and allows for policy conclusions to be drawn for other countries that are still in the fertility transition process.
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