Journal articles: 'Multi-Objective Planning'

1

Roijers, Diederik M. "Multi-objective decision-theoretic planning." AI Matters 2, no. 4 (December 8, 2016): 11–12. http://dx.doi.org/10.1145/3008665.3008670.

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Craft, D. L., T. Halabi, and T. Bortfeld. "Multi-Objective IMRT Treatment Planning." International Journal of Radiation Oncology*Biology*Physics 69, no. 3 (November 2007): S658. http://dx.doi.org/10.1016/j.ijrobp.2007.07.2009.

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Ramirez-Atencia, Cristian, and David Camacho. "Constrained multi-objective optimization for multi-UAV planning." Journal of Ambient Intelligence and Humanized Computing 10, no. 6 (July 4, 2018): 2467–84. http://dx.doi.org/10.1007/s12652-018-0930-0.

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C.Kavitha, C. Kavitha, and C. Vijayalakshmi C. Vijayalakshmi. "Design and Implementation of Fuzzy Multi Objective Optimization Model for Production Planning." Indian Journal of Applied Research 3, no. 12 (October 1, 2011): 372–75. http://dx.doi.org/10.15373/2249555x/dec2013/113.

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Geißer, Florian, Patrik Haslum, Sylvie Thiébaux, and Felipe Trevizan. "Admissible Heuristics for Multi-Objective Planning." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 100–109. http://dx.doi.org/10.1609/icaps.v32i1.19790.

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Planning problems of practical relevance commonly include multiple objectives that are difficult to weight a priori. Several heuristic search algorithms computing the Pareto front of non-dominated solutions have been proposed to handle these multi-objective (MO) planning problems. However, the design of informative admissible heuristics to guide these algorithms has not received the same level of attention. The standard practice is to use the so-called ideal point combination, which applies a single-objective heuristic to each objective independently, without capturing any of the trade-offs between them. This paper fills this gap: we extend several classes of classical planning heuristics to the multi-objective case, in such a way as to reflect the tradeoffs underlying the various objectives. We find that MO abstraction heuristics achieve overall the best performance, but that not every MO generalisation pays off.

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Agha, Amin Al. "Multi-objective methods in development planning." International Journal of Applied Nonlinear Science 2, no. 1/2 (2015): 3. http://dx.doi.org/10.1504/ijans.2015.076519.

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Gharakhani, Mohsen. "A robust multi-objective production planning." International Journal of Industrial Engineering Computations 1, no. 1 (July 1, 2010): 73–78. http://dx.doi.org/10.5267/j.ijiec.2010.01.007.

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Yu, Zhao, Shuanbao Niu, Chao Huo, Ning Chen, Kaige Song, Xiaohui Wang, and Yu Bai. "Multi-objective partition planning for multi-infeed HVDC system." Global Energy Interconnection 4, no. 1 (February 2021): 81–90. http://dx.doi.org/10.1016/j.gloei.2021.03.008.

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Xia, Zheng Hong, and Wei Jun Pan. "Multi-Objective Distribution of Multi-Helicopters and Track Planning." Applied Mechanics and Materials 380-384 (August 2013): 1637–40. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1637.

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The multi-objective distribution of multi-helicopter based on the feasibility matrix was proposed in this paper, which enhanced the effectiveness of aviation emergency rescue. The horizontal track planning of helicopters was put forward based on the morphology graphic process technology and heuristic A* search algorithm, which can obtain the feasible shortest flight path with the condition of safety separation.

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Trzaskalik, T. "Multi-objective, multi-period planning for a manufacturing plant." Engineering Costs and Production Economics 20, no. 2 (October 1990): 113–20. http://dx.doi.org/10.1016/0167-188x(90)90095-y.

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Al-Ashhab, Mohamad Sayed, Taiser Attia, and Shadi Mohammad Munshi. "Multi-Objective Production Planning Using Lexicographic Procedure." American Journal of Operations Research 07, no. 03 (2017): 174–86. http://dx.doi.org/10.4236/ajor.2017.73012.

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TABUCANON, M. T., and S. MUKYANGKOON. "Multi-objective microcomputer-based interactive production planning." International Journal of Production Research 23, no. 5 (September 1985): 1001–23. http://dx.doi.org/10.1080/00207548508904762.

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Guariso, Giorgio, and Matteo Sangiorgio. "Multi-objective planning of building stock renovation." Energy Policy 130 (July 2019): 101–10. http://dx.doi.org/10.1016/j.enpol.2019.03.053.

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Davoodi, Mansoor, Fatemeh Panahi, Ali Mohades, and Seyed Naser Hashemi. "Multi-objective path planning in discrete space." Applied Soft Computing 13, no. 1 (January 2013): 709–20. http://dx.doi.org/10.1016/j.asoc.2012.07.023.

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Pieprzycki, Adam, and Wiesław Ludwin. "Selected issues of multi-objective WLAN planning." Science, Technology and Innovation 3, no. 2 (December 27, 2018): 69–78. http://dx.doi.org/10.5604/01.3001.0012.8170.

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The aim of the article is to apply a multicriteria approach to MOO (Multi Objective Optimization) planning for WLAN (Wireless Local Area Network) using selected swarm optimization methods. For this purpose, in the process of searching for the extremum of two criterion functions, which are an optimization index, two swarm algorithms were used: MOCS (Multi Objective Cuckoo Search) and MOPSO (Multi Objective Particle Swarm Optimization). The results were compared with the single-criterion SOO (Single Objective Optimization) range-based network planning based on the regular distribution of TP (test point) using the CS Cuckoo Search algorithm.

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Kůdela, Jakub, Radovan Šomplák, Vlastimír Nevrlý, Tomáš Lipovský, Veronika Smejkalová, and Ladislav Dobrovský. "Multi-objective strategic waste transfer station planning." Journal of Cleaner Production 230 (September 2019): 1294–304. http://dx.doi.org/10.1016/j.jclepro.2019.05.167.

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Tazaki, Yuichi, Shuichi Suzuki, and Tatsuya Suzuki. "Constraint-Based Multi-Objective Trajectory Planning for Multi-Body Systems." Journal of the Robotics Society of Japan 31, no. 5 (2013): 508–16. http://dx.doi.org/10.7210/jrsj.31.508.

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Zhang, Zetian, Zhenghan Liu, and Xiaoping Jia. "Multi-Objective Sustainable Planning of Chemical Production Chains." IOP Conference Series: Earth and Environmental Science 687, no. 1 (March 1, 2021): 012072. http://dx.doi.org/10.1088/1755-1315/687/1/012072.

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Abed, Baraa M., and Wesam M. Jasim. "Multi-objective optimization path planning with moving target." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (September 1, 2022): 1184. http://dx.doi.org/10.11591/ijai.v11.i3.pp1184-1196.

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<div align="left"><span>Path planning or finding a collision-free path for mobile robots between starting position and its destination is a critical problem in robotics. This study is concerned with the multi objective optimization path planning problem of autonomous mobile robots with moving targets in dynamic environment, with three objectives considered: path security, length and smoothness. Three modules are presented in the study. The first module is to combine particle swarm optimization algorithm (PSO) with bat algorithm (BA). The purpose of PSO is to optimize two important parameters of BA algorithm to minimize distance and smooth the path. The second module is to convert the generated infeasible points into feasible ones using a new local search algorithm (LS). The third module obstacle detection and avoidance (ODA) algorithm is proposed to complete the path, which is triggered when the mobile robot detects obstacles in its field of vision. ODA algorithm based on simulating human walking in a dark room. Several simulations with varying scenarios are run to test the validity of the proposed solution. The results show that the mobile robots are able to travel clearly and completely safe with short path, proving the effectiveness of this method.</span><span> </span> </div>

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Zheng, Aoyu, Bingjie Li, Mingfa Zheng, and Haitao Zhong. "Multi-Objective UAV Trajectory Planning in Uncertain Environment." Symmetry 13, no. 11 (November 11, 2021): 2160. http://dx.doi.org/10.3390/sym13112160.

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UAV trajectory planning is one of the research focuses in artificial intelligence and UAV technology. The asymmetric information, however, will lead to the uncertainty of the UAV trajectory planning; the probability theory as the most commonly used method to solve the trajectory planning problem in uncertain environment will lead to unrealistic conclusions under the condition of lacking samples, while the uncertainty theory based on uncertain measures is an efficient method to solve such problems. Firstly, the uncertainties in trajectory planning are sufficiently considered in this paper; the fuel consumption, concealment and threat degree with uncertain variables are taken as the objective functions; the constraints are analyzed according to the maneuverability; and the uncertain multi-objective trajectory planning (UMOTP) model is established. After that, this paper takes both the long-term benefits and its stability into account, and then, the expected-value and standard-deviation efficient trajectory model is established. What is more, this paper solves the Pareto front of the trajectory planning, satisfying various preferences, which avoids the defects of the trajectory obtained by traditional model only applicable to a certain specific situation. In order to obtain a better solution set, this paper proposes an improved backbones particle swarm optimization algorithm based on PSO and NSGA-II, which overcomes the shortcomings of the traditional algorithm such as premature convergence and poor robustness, and the efficiency of the algorithm is tested. Finally, the algorithm is applied to the UMOTP problem; then, the optimal trajectory set is obtained, and the effectiveness and reliability of the model is verified.

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Gurav, Jyotiba B., and D. G. Regulwar. "Multi-objective fuzzy optimization for sustainable irrigation planning." H2Open Journal 3, no. 1 (January 1, 2020): 373–89. http://dx.doi.org/10.2166/h2oj.2020.032.

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Abstract The objective of the present work is to determine an optimal cropping pattern under uncertainty, which maximizes four objectives simultaneously, including net benefits (NBF), crop production (CPD), employment generation (EGN) and manure utilization (MUT). Except the objective of maximizing the NBF, the other objectives are related to sustainability. To deal with uncertainty, a multi-objective fuzzy linear programming (MOFLP) model has developed along with fuzziness in decision parameters (objective function coefficient, cost coefficients, technological coefficients and resources) and decision variables (area to be irrigated under each crop in each season) and applied the same to Jayakwadi Project Stage-I, Maharashtra, India. The present study is in the form of a successful attempt to deal with irrigation planning associated with sustainability and uncertainty.

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Chen, Y. L. "Optimal Multi-Objective Single-Tuned Harmonic Filter Planning." IEEE Transactions on Power Delivery 20, no. 2 (April 2005): 1191–97. http://dx.doi.org/10.1109/tpwrd.2002.844282.

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Iwata, Yoshio, Kouichi Taji, and Hiroyuki Tamura. "Multi-objective capacity planning for agile semiconductor manufacturing." Production Planning & Control 14, no. 3 (April 2003): 244–54. http://dx.doi.org/10.1080/09537280310000102586.

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Xu, Xiandong, Yi Guan, Bingrong Hong, Wende Ke, Qiubo Zhong, and Songhao Piao. "Multi-objective optimisation for humanoid robot motion planning." International Journal of Wireless and Mobile Computing 10, no. 2 (2016): 112. http://dx.doi.org/10.1504/ijwmc.2016.076178.

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Tseng, Fan-Hsun, Chi-Yuan Chen, and Han-Chieh Chao. "Multi-objective optimisation for heterogeneous cellular network planning." IET Communications 13, no. 3 (February 19, 2019): 322–30. http://dx.doi.org/10.1049/iet-com.2018.5332.

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Yuan, Fang, and Lee D. Han. "A multi-objective optimization approach for evacuation planning." Procedia Engineering 3 (2010): 217–27. http://dx.doi.org/10.1016/j.proeng.2010.07.020.

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Raju, K. Srinivasa, A. Vasan, Piyush Gupta, Karthik Ganesan, and Hitesh Mathur. "Multi-objective differential evolution application to irrigation planning." ISH Journal of Hydraulic Engineering 18, no. 1 (March 2012): 54–64. http://dx.doi.org/10.1080/09715010.2012.662428.

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Farmakis, Panagiotis M., and Athanasios P. Chassiakos. "Dynamic Multi-objective Layout Planning of Construction Sites." Procedia Engineering 196 (2017): 674–81. http://dx.doi.org/10.1016/j.proeng.2017.08.057.

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Krishna Priya, G. S., and Santanu Bandyopadhyay. "Multi-objective pinch analysis for power system planning." Applied Energy 202 (September 2017): 335–47. http://dx.doi.org/10.1016/j.apenergy.2017.05.137.

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Fotakis, Dimitris G., Epameinondas Sidiropoulos, Dimitriοs Myronidis, and Kostas Ioannou. "Spatial genetic algorithm for multi-objective forest planning." Forest Policy and Economics 21 (August 2012): 12–19. http://dx.doi.org/10.1016/j.forpol.2012.04.002.

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Baykasoglu, Adil, and Tolunay Gocken. "Multi-objective aggregate production planning with fuzzy parameters." Advances in Engineering Software 41, no. 9 (September 2010): 1124–31. http://dx.doi.org/10.1016/j.advengsoft.2010.07.002.

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32

Yuan-Lin Chen. "Weak bus-oriented optimal multi-objective VAr planning." IEEE Transactions on Power Systems 11, no. 4 (1996): 1885–90. http://dx.doi.org/10.1109/59.544659.

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Zeferino, João A., António P. Antunes, and Maria C. Cunha. "Multi-objective model for regional wastewater systems planning." Civil Engineering and Environmental Systems 27, no. 2 (June 2010): 95–106. http://dx.doi.org/10.1080/09540250802658988.

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34

Onta, Pushpa R., Ashim Das Gupta, and Guna N. Paudyal. "Integrated irrigation development planning by multi‐objective optimization." International Journal of Water Resources Development 7, no. 3 (September 1991): 185–93. http://dx.doi.org/10.1080/07900629108722511.

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35

Perlack, Robert D., and Cleve E. Willis. "Multi‐Objective Decision‐Making in Waste Disposal Planning." Journal of Environmental Engineering 111, no. 3 (June 1985): 373–85. http://dx.doi.org/10.1061/(asce)0733-9372(1985)111:3(373).

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Brauers, Willem Karel M. "Multi-objective seaport planning by MOORA decision making." Annals of Operations Research 206, no. 1 (January 26, 2013): 39–58. http://dx.doi.org/10.1007/s10479-013-1314-7.

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HUGO, A., P. RUTTER, S. PISTIKOPOULOS, A. AMORELLI, and G. ZOIA. "Hydrogen infrastructure strategic planning using multi-objective optimization." International Journal of Hydrogen Energy 30, no. 15 (December 2005): 1523–34. http://dx.doi.org/10.1016/j.ijhydene.2005.04.017.

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TSOU, C. "Multi-objective inventory planning using MOPSO and TOPSIS." Expert Systems with Applications 35, no. 1-2 (July 2008): 136–42. http://dx.doi.org/10.1016/j.eswa.2007.06.009.

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Fotakis, Dimitrios G. "Multi-objective spatial forest planning using self-organization." Ecological Informatics 29 (September 2015): 1–5. http://dx.doi.org/10.1016/j.ecoinf.2015.06.001.

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Tezcaner, Diclehan, and Murat Köksalan. "An Interactive Algorithm for Multi-objective Route Planning." Journal of Optimization Theory and Applications 150, no. 2 (April 19, 2011): 379–94. http://dx.doi.org/10.1007/s10957-011-9838-y.

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Arabali, Amirsaman, Seyed Hamid Hosseini, and Moein Moeini-Aghtaie. "Probabilistic multi-objective transmission investment and expansion planning." International Transactions on Electrical Energy Systems 25, no. 9 (May 10, 2014): 1884–904. http://dx.doi.org/10.1002/etep.1940.

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Thabit, Sahib, and Ali Mohades. "Multi-Robot Path Planning Based on Multi-Objective Particle Swarm Optimization." IEEE Access 7 (2019): 2138–47. http://dx.doi.org/10.1109/access.2018.2886245.

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Badhotiya, Gaurav Kumar, Gunjan Soni, and M. L. Mittal. "Multi-site integrated production and distribution planning: a multi-objective approach." International Journal of Product Development 22, no. 6 (2018): 488. http://dx.doi.org/10.1504/ijpd.2018.095923.

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Soni, Gunjan, M. L. Mittal, and Gaurav Kumar Badhotiya. "Multi-site integrated production and distribution planning: a multi-objective approach." International Journal of Product Development 22, no. 6 (2018): 488. http://dx.doi.org/10.1504/ijpd.2018.10017046.

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Wang, Ni, Petra W. Heijnen, and Pieter J. Imhof. "A multi-actor perspective on multi-objective regional energy system planning." Energy Policy 143 (August 2020): 111578. http://dx.doi.org/10.1016/j.enpol.2020.111578.

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Johnston, Mark D., and Mark E. Giuliano. "Multi-Objective Scheduling for Space Science Missions." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 8 (October 20, 2011): 1140–48. http://dx.doi.org/10.20965/jaciii.2011.p1140.

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We have developed an architecture called MUSE (Multi-User Scheduling Environment) to enable the integration of multi-objective evolutionary algorithms with existing domain planning and scheduling tools. Our approach is intended to make it possible to reuse existing software, while obtaining the advantages of multi-objective optimization algorithms. This approach enables multiple participants to actively engage in the optimization process, each representing one or more objectives in the optimization problem. As initial applications, we apply our approach to scheduling the James Webb Space Telescope, where three objectives aremodeled: minimizing wasted time, minimizing the number of observations that miss their last planning opportunity in a year, and minimizing the (vector) build up of angularmomentumthat would necessitate the use of mission critical propellant to dump the momentum. As a second application area, we model aspects of the Cassini science planning process, including the trade-off between collecting data (subject to onboard recorder capacity) and transmitting saved data to Earth. A third mission application is that of scheduling the Cluster 4-spacecraft constellation plasma experiment. In this paper we describe our overall architecture and our adaptations for these different application domains. We also describe our plans for applying this approach to other science mission planning and scheduling problems in the future.

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Do, M., and S. Kambhampati. "SAPA: A Multi-objective Metric Temporal Planner." Journal of Artificial Intelligence Research 20 (December 1, 2003): 155–94. http://dx.doi.org/10.1613/jair.1156.

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SAPA is a domain-independent heuristic forward chaining planner that can handle durative actions, metric resource constraints, and deadline goals. It is designed to be capable of handling the multi-objective nature of metric temporal planning. Our technical contributions include (i) planning-graph based methods for deriving heuristics that are sensitive to both cost and makespan (ii) techniques for adjusting the heuristic estimates to take action interactions and metric resource limitations into account and (iii) a linear time greedy post-processing technique to improve execution flexibility of the solution plans. An implementation of SAPA using many of the techniques presented in this paper was one of the best domain independent planners for domains with metric and temporal constraints in the third International Planning Competition, held at AIPS-02. We describe the technical details of extracting the heuristics and present an empirical evaluation of the current implementation of SAPA.

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Khalili-Damghani, Kaveh, Ayda Shahrokh, and Alireza Pakgohar. "Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain." International Journal of Production Management and Engineering 5, no. 2 (July 28, 2017): 85. http://dx.doi.org/10.4995/ijpme.2017.6633.

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<p>In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method.</p>

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Nguyen, Hoa Van, Hamid Rezatofighi, Ba-Ngu Vo, and Damith C. Ranasinghe. "Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Objects." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7227–35. http://dx.doi.org/10.1609/aaai.v34i05.6213.

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We consider the challenging problem of online planning for a team of agents to autonomously search and track a time-varying number of mobile objects under the practical constraint of detection range limited onboard sensors. A standard POMDP with a value function that either encourages discovery or accurate tracking of mobile objects is inadequate to simultaneously meet the conflicting goals of searching for undiscovered mobile objects whilst keeping track of discovered objects. The planning problem is further complicated by misdetections or false detections of objects caused by range limited sensors and noise inherent to sensor measurements. We formulate a novel multi-objective POMDP based on information theoretic criteria, and an online multi-object tracking filter for the problem. Since controlling multi-agent is a well known combinatorial optimization problem, assigning control actions to agents necessitates a greedy algorithm. We prove that our proposed multi-objective value function is a monotone submodular set function; consequently, the greedy algorithm can achieve a (1-1/e) approximation for maximizing the submodular multi-objective function.

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Mnif, Mouna, and Sadok Bouamama. "A New Multi-Layer Distributed Approach for a Multi-objective Planning Problem." Procedia Computer Science 159 (2019): 1406–20. http://dx.doi.org/10.1016/j.procs.2019.09.311.

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Journal articles on the topic 'Multi-Objective Planning'

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