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Littérature scientifique sur le sujet « Planning Optimization »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 10 mars 2023

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Articles de revues sur le sujet "Planning Optimization"

1

Baltas, D. « INVERSE PLANNING OPTIMIZATION ». Radiotherapy and Oncology 92 (août 2009) : S99. http://dx.doi.org/10.1016/s0167-8140(12)72851-7.

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Ouazene, Yassine, Taha Arbaoui et Farouk Yalaoui. « Planning and Scheduling Optimization ». Applied Sciences 11, n^o 19 (27 septembre 2021) : 8980. http://dx.doi.org/10.3390/app11198980.

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Pepelyaev, V. A. « Planning optimization-simulation experiments ». Cybernetics and Systems Analysis 42, n^o 6 (novembre 2006) : 866–75. http://dx.doi.org/10.1007/s10559-006-0126-z.

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Shih, Kuo-Chuan, et Shu-Shun Liu. « AN OPTIMIZATION MODEL FOR PRECAST PROJECT PLANNING USING GROUP CONCEPTS ». Journal of the Operations Research Society of Japan 53, n^o 3 (2010) : 189–206. http://dx.doi.org/10.15807/jorsj.53.189.

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Brown, Gerald G., Dennis M. Coulter et Alan R. Washburn. « Sortie Optimization and Munitions Planning ». Military Operations Research 1, n^o 1 (1 juin 1994) : 13–18. http://dx.doi.org/10.5711/morj.1.1.13.

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Melnikova, Yu. « OPTIMIZATION MODEL FOR TRAFFIC PLANNING ». Agrosvit, n^o 9 (20 mai 2020) : 127. http://dx.doi.org/10.32702/2306-6792.2020.9.127.

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Harada, Kensuke. « Optimization in Robot Motion Planning ». Journal of the Robotics Society of Japan 32, n^o 6 (2014) : 508–11. http://dx.doi.org/10.7210/jrsj.32.508.

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Paudyal, Guna Nidhi, et Ashim Das Gupta. « Irrigation Planning by Multilevel Optimization ». Journal of Irrigation and Drainage Engineering 116, n^o 2 (mars 1990) : 273–91. http://dx.doi.org/10.1061/(asce)0733-9437(1990)116:2(273).

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Bermon, Stuart, et Sarah Jean Hood. « Capacity Optimization Planning System (CAPS) ». Interfaces 29, n^o 5 (octobre 1999) : 31–50. http://dx.doi.org/10.1287/inte.29.5.31.

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Knowles, Thomas W. « Optimization models for mine planning ». Computers & ; Industrial Engineering 37, n^o 1-2 (octobre 1999) : 469–72. http://dx.doi.org/10.1016/s0360-8352(99)00120-5.

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Thèses sur le sujet "Planning Optimization"

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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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Livres sur le sujet "Planning Optimization"

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Network optimization. London : Chapman & Hall, 1995.

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1954-, Pardalos P. M., Hearn D. W et Hager William W. 1948-, dir. Network optimization. Berlin : Springer, 1997.

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Hamada, Michael. Experiments : Planning, analysis, and optimization. 2^e éd. New Jersey : John Wiley & Sons Inc, 2009.

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Wu, Chien-Fu. Experiments : Planning, analysis, and optimization. 2^e éd. Hoboken, N.J : Wiley, 2009.

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Morey, Christopher. Optimization models for synchronization planning. Monterey, Calif : Naval Postgraduate School, 1991.

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Salmelin, Juha, et Esa Metsälä. LTE backhaul : Planning and optimization. Hoboken : John Wiley and Sons, Inc., 2015.

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Zhang, Yan. WiMAX network planning and optimization. Boca Raton, FL : Auerbach Publications, 2009.

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Brown, Gerald Gerard. Sortie optimization and munitions planning. Monterey, Calif : Naval Postgraduate School, 1993.

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Khosla, Anita, et Monika Aggarwal, dir. Renewable Energy Optimization, Planning and Control. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4663-8.

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Schiewe, Philine. Integrated Optimization in Public Transport Planning. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46270-3.

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Chapitres de livres sur le sujet "Planning Optimization"

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Brauner, Nadia, Gerd Finke et Maurice Queyranne. « Production Planning ». Dans Applications of Combinatorial Optimization, 73–109. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118600283.ch4.

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Brauner, Nadia, Gerd Finke et Maurice Queyranne. « Production Planning ». Dans Applications of Combinatorial Optimization, 73–109. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119005384.ch4.

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Schmidt, Marie E. « Line Planning ». Dans Springer Optimization and Its Applications, 9–71. New York, NY : Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9566-6_2.

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Yang, Leping, Yanwei Zhu, Xianhai Ren et Yuanwen Zhang. « Spacecraft Multi-Mission Planning ». Dans SpringerBriefs in Optimization, 7–22. New York, NY : Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0838-7_2.

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Yang, Leping, Yanwei Zhu, Xianhai Ren et Yuanwen Zhang. « Proximity Relative Motion Planning ». Dans SpringerBriefs in Optimization, 37–90. New York, NY : Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0838-7_4.

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Yang, Leping, Yanwei Zhu, Xianhai Ren et Yuanwen Zhang. « Multi-Spacecraft Coordinated Planning ». Dans SpringerBriefs in Optimization, 91–112. New York, NY : Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0838-7_5.

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Gkiotsalitis, Konstantinos. « Operational Planning and Control ». Dans Public Transport Optimization, 545–87. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12444-0_13.

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Siefer, Thomas, et Dirk Hauptmann. « Computer Aided Planning of Railroad Operation ». Dans Applied Optimization, 37–47. Boston, MA : Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3357-0_3.

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Lu, Yong-Zai, Yu-Wang Chen, Min-Rong Chen, Peng Chen et Guo-Qiang Chen. « EO for Production Planning and Scheduling ». Dans Extremal Optimization, 271–96. Boca Raton : Auerbach Publications, 2015. : Auerbach Publications, 2018. http://dx.doi.org/10.1201/b19572-8.

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Chevallier, Christophe. « RF Planning and Optimization ». Dans WCDMA (UMTS) Deployment Handbook, 21–72. Chichester, UK : John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470035749.ch2.

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Actes de conférences sur le sujet "Planning Optimization"

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Kulbida, U. N., O. N. Kaneva et A. V. Zykina. « Media planning optimization treatment ». Dans 2014 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2014. http://dx.doi.org/10.1109/dynamics.2014.7005673.

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Ademoye, Taoridi A., Asad Davari, Charles C. Castello, Sharon Fan et Jeffrey Fan. « Path Planning Via CPLEX Optimization ». Dans 2008 40th Southeastern Symposium on System Theory (SSST). IEEE, 2008. http://dx.doi.org/10.1109/ssst.2008.4480197.

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Marzi, Hosein, et Yi Jia Lian. « Optimization in radiosurgery treatment planning ». Dans 2011 IEEE International Systems Conference (SysCon). IEEE, 2011. http://dx.doi.org/10.1109/syscon.2011.5929035.

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Gam, Marwa, Dimitri Lefebvre, Lotfi Nabli et Achraf Jabeur Telmoudi. « Optimization of maintenance patrols planning ». Dans 2021 29th Mediterranean Conference on Control and Automation (MED). IEEE, 2021. http://dx.doi.org/10.1109/med51440.2021.9480321.

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« Agile Release Planning through Optimization ». Dans International Conference on Evaluation of Novel Approaches to Software Engineering. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001865001490160.

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Saranya, S., et T. Amudha. « Crop planning optimization with social spider optimization algorithm ». Dans 2017 International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2017. http://dx.doi.org/10.1109/iss1.2017.8389281.

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Kalsch, Marcel T., et Katrin Tschirpke. « Access network optimization ». Dans 2014 16th International Telecommunications Network Strategy and Planning Symposium (Networks). IEEE, 2014. http://dx.doi.org/10.1109/netwks.2014.6959220.

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Lund, Jay R. « Integrated Flood Planning with Risk-Based Optimization ». Dans 29th Annual Water Resources Planning and Management Conference. Reston, VA : American Society of Civil Engineers, 1999. http://dx.doi.org/10.1061/40430(1999)229.

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Ormsbee, Lindell, et Sirinavasa Lingireddy. « Optimization of Water Resource System Using Genetic Optimization with Both Deductive and Inductive Simulation Models ». Dans 29th Annual Water Resources Planning and Management Conference. Reston, VA : American Society of Civil Engineers, 1999. http://dx.doi.org/10.1061/40430(1999)21.

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Liu, Shuai, et Pengcheng Liu. « Robot motion planning benchmarking and optimization through motion planning pipeline ». Dans 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE). IEEE, 2021. http://dx.doi.org/10.1109/case49439.2021.9551646.

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Rapports d'organisations sur le sujet "Planning Optimization"

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Brown, Gerald G., Robert F. Dell et Alexandra M. Newman. Optimization-Based Military Capital Planning. Fort Belvoir, VA : Defense Technical Information Center, octobre 2004. http://dx.doi.org/10.21236/ada428086.

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Peles, S. Open source Modeling and optimization tools for Planning. Office of Scientific and Technical Information (OSTI), février 2017. http://dx.doi.org/10.2172/1343841.

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Zilberstein, Shlomo. Adaptive Optimization Techniques for Large-Scale Stochastic Planning. Fort Belvoir, VA : Defense Technical Information Center, juin 2011. http://dx.doi.org/10.21236/ada563724.

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Marcus, Steven I., Michael C. Fu et Jiaqiao Hu. Simulation-Based Methodologies for Global Optimization and Planning. Fort Belvoir, VA : Defense Technical Information Center, octobre 2013. http://dx.doi.org/10.21236/ada591505.

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Judi, David R., Byron A. Tasseff, Russell W. Bent et Feng Pan. Topography-based Flood Planning and Optimization Capability Development Report. Office of Scientific and Technical Information (OSTI), février 2014. http://dx.doi.org/10.2172/1122029.

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Sheu, Phillip. Query Optimization and Planning in Object-Oriented Knowledge Bases. Fort Belvoir, VA : Defense Technical Information Center, août 1992. http://dx.doi.org/10.21236/ada256006.

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Deits, Robin, et Russ Tedrake. Footstep Planning on Uneven Terrain with Mixed-Integer Convex Optimization. Fort Belvoir, VA : Defense Technical Information Center, août 2014. http://dx.doi.org/10.21236/ada609276.

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Pin, Francois G. Multi-optimization Criteria-based Robot Behavioral Adaptability and Motion Planning. Office of Scientific and Technical Information (OSTI), juin 2002. http://dx.doi.org/10.2172/835385.

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Pin, Francois G. Multi-optimization Criteria-based Robot Behavioral Adaptability and Motion Planning. Office of Scientific and Technical Information (OSTI), juin 2003. http://dx.doi.org/10.2172/835388.

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Pin, Grancois G. Multi-optimization Criteria-based Robot Behavioral Adaptability and Motion Planning. Office of Scientific and Technical Information (OSTI), juin 2004. http://dx.doi.org/10.2172/839107.

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