Relevant bibliographies by topics / Bi-criteria optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Bi-criteria optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Bi-criteria optimization"

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Luca, Traian Ionut, and Dorel I. Duca. "Approximations of bi-criteria optimization problem." Studia Universitatis Babes-Bolyai Matematica 63, no. 4 (December 8, 2018): 549–59. http://dx.doi.org/10.24193/subbmath.2018.4.10.

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Mankowski, Michal, and Mikhail Moshkov. "Dynamic programming bi-criteria combinatorial optimization." Discrete Applied Mathematics 284 (September 2020): 513–33. http://dx.doi.org/10.1016/j.dam.2020.04.016.

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Sokalski, Krzysztof Z. "Bi-Criteria Optimization In Designing Magnetic Composites." Advanced Materials Letters 8, no. 6 (June 1, 2017): 689–94. http://dx.doi.org/10.5185/amlett.2017.1441.

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Z. Sokalski, Krzysztof, Barbara Slusarek, and Marek Przybylski. "Bi-criteria optimization in designing magnetic composites." Advanced Materials Letters 8, no. 10 (October 30, 2017): 987–92. http://dx.doi.org/10.5185/amlett.2017.1597.

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Alsolami, Fawaz, Talha Amin, Igor Chikalov, and Mikhail Moshkov. "Bi-criteria optimization problems for decision rules." Annals of Operations Research 271, no. 2 (June 5, 2018): 279–95. http://dx.doi.org/10.1007/s10479-018-2905-0.

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Osiadacz, Andrzej J., and Niccolo Isoli. "Multi-Objective Optimization of Gas Pipeline Networks." Energies 13, no. 19 (October 2, 2020): 5141. http://dx.doi.org/10.3390/en13195141.

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The main goal of this paper is to prove that bi-objective optimization of high-pressure gas networks ensures grater system efficiency than scalar optimization. The proposed algorithm searches for a trade-off between minimization of the running costs of compressors and maximization of gas networks capacity (security of gas supply to customers). The bi-criteria algorithm was developed using a gradient projection method to solve the nonlinear constrained optimization problem, and a hierarchical vector optimization method. To prove the correctness of the algorithm, three existing networks have been solved. A comparison between the scalar optimization and bi-criteria optimization results confirmed the advantages of the bi-criteria optimization approach.
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Pietrzak, J. "Bi-criteria optimization of structures liable to instability." Structural Optimization 7, no. 1-2 (February 1994): 61–65. http://dx.doi.org/10.1007/bf01742506.

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Maeda, Takashi. "Fuzzy linear programming problems as bi-criteria optimization problems." Applied Mathematics and Computation 120, no. 1-3 (May 2001): 109–21. http://dx.doi.org/10.1016/s0096-3003(99)00237-4.

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Krivulin, Nikolai. "Using tropical optimization techniques in bi-criteria decision problems." Computational Management Science 17, no. 1 (November 26, 2018): 79–104. http://dx.doi.org/10.1007/s10287-018-0341-x.

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Ozturk, Onur. "A bi-criteria optimization model for medical device sterilization." Annals of Operations Research 293, no. 2 (June 20, 2019): 809–31. http://dx.doi.org/10.1007/s10479-019-03296-x.

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Dissertations / Theses on the topic "Bi-criteria optimization"

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Ibrahem, Al-mehdi Mohamed M. "Scheduling optimization of cellular flowshop with sequence dependent setup times." Proceedings of the 47th CIRP Conference on Manufacturing Systems, 2014. http://hdl.handle.net/1993/30730.

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In cellular manufacturing systems, minimization of the completion time has a great impact on the production time, material flow, and productivity. An effective scheduling is crucial to attaining the advantages of cellular manufacturing systems. This dissertation attempts to solve the Flowshop Manufacturing Cell (cellular flowshop) Scheduling Problem with Sequence Dependent Setup Times (FMCSP with SDSTs) considering two performance measures: the total flow time as a mono objective, and the makespan and total flow time combined as a bi-criteria scheduling problem. The proposed problem is known to be the NP-hard problem because of its complexity. Several metaheuristic algorithms based on Genetic Algorithm (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) are developed for scheduling part families as well as jobs within each part family for FMCSP with SDSTs to minimize the total flow time. A local search method based on SA combined with PSO (named as PSO-SA) is proposed to enhance the intensification and improve the quality of the solution obtained by pure PSO. The effectiveness and efficiency of the proposed metaheuristics are evaluated based on the Relative Percentage Deviation (RPD) from its lower bound, and the robustness. Results indicate PSO-SA is performed similar to best available algorithms for small and medium size test problems. Yet, there is a very small deviation from best results for large problems. A Multi-objective Particle Swarm Optimization (MPSO) and a Multi-objective Simulated Annealing (MOSA) Algorithm are further proposed to solve the bi-criteria optimization problem to minimize the total flow time and makespan simultaneously. An improved PSO is combined with Threshold Acceptance (TA) algorithm to improve effectiveness of the proposed MPSO (named as IMPSO-TA) for the convergence of the obtained Pareto Front. The proposed algorithms are evaluated using several Quality Indicators (QI) measures for multiobjective optimization problems. The proposed algorithms can generate approximated Pareto Fronts in a reasonable CPU time. The proposed IMPSO-SA outperforms MOSA algorithm in terms of CPU time and minimizing the objective functions.
October 2015
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Vijayaragavan, Koushiik Ravindran A. "Bi-criteria optimization model for a four stage centralized supply chain." 2008. http://etda.libraries.psu.edu/theses/approved/PSUonlyIndex/ETD-3213/index.html.

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Chuang, Wei-Che, and 莊偉哲. "An Inheritable Heuristic Algorithm for Bi-criteria Vehicle Routing Optimization Problems with Time Windows." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/57579153613937772463.

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碩士
逢甲大學
資訊工程所
92
In this thesis, an inheritable heuristic algorithm is proposed to solve the bi-criteria vehicle routing optimization problems with time windows (BCVRPTW). The purpose of VRPTW is to find a shortest deliver path under the constraints of time windows, which involves the following criteria: schedule time, overload, and the number of vehicles. Most of existing methods formulate this problem as a single objective optimization problem with a weight sum function for the above-mentioned criteria. The disadvantage of these methods is that an appropriate number of vehicles should be predefined, which requires experts’ domain knowledge. In the thesis, the problem is formulated as a bi-criteria optimization problem containing the objectives of the number of vehicles and costs of routing. In the proposed algorithm, a set of solutions can be obtained in a single run, which can support users to choose an appropriate routing strategy. The proposed algorithm can fast obtain the set of best routing solutions to various numbers of vehicles with a novel inheritance mechanism. In this thesis, two well-known heuristic algorithms are chosen to cope with the VRPTW: a genetic algorithms and a simulated annealing. The test data are selected from the Solomon’s benchmarks, include 56 international benchmarks and three large data set of customers. Experimental results show the effectiveness of the proposed algorithm to solve the VRPTW.
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Huang, Meng-Hsun, and 黃盟勛. "An Inherutable Bi-criteria Genetic Algorithm for Combinatorial Optimization Problem C(N,n) and Its Applications." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/53878653050929634036.

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碩士
逢甲大學
資訊工程學系
89
In this paper, an inheritable genetic algorithm (IGA) with an orthogonal array crossover (OAX) is proposed to efficiently find a complete set of Pareto-optimal solutions to bi-criteria combinatorial optimization problems BCCOPs. The investigated BCCOP has an NP-hard search space of C(n, r) instances, i.e., the number of ways of choosing r out of n samples, and two incommensurable and often competing objectives: minimizing the number r of selected instances while maximizing the system performance. The merits of IGA are as follows: 1) OAX with the systematic reasoning ability based on orthogonal experimental design can efficiently explore and exploit the search space of BCCOP, especially for large BCCOP which is intractable for conventional GAs; 2) An inheritable mechanism of IGA can efficiently find the solution of C(n, r 1) when the solution of C(n, r) has been obtained; and 3) IGA is a general purpose algorithm without need of domain knowledge for finding a complete set of Pareto-optimal solutions or solutions to single objective problems C(n, r) with a constant value r. To illustrate the effectiveness of the proposed algorithm, IGA is applied to solving both shape approximation problems and the problem of editing the minimal reference set of a k-NNR classifier. The high performance of IGA is compared with those of some existing GA-based algorithms. It is shown empirically that IGA is superior to existing algorithms in finding a complete set of Pareto-optimal solutions to BCCOPs in terms of convergence speed and solution quality, especially in solving large BCCOPs.
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Hussain, Shahid. "Extensions of Dynamic Programming: Decision Trees, Combinatorial Optimization, and Data Mining." Diss., 2016. http://hdl.handle.net/10754/615875.

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This thesis is devoted to the development of extensions of dynamic programming to the study of decision trees. The considered extensions allow us to make multi-stage optimization of decision trees relative to a sequence of cost functions, to count the number of optimal trees, and to study relationships: cost vs cost and cost vs uncertainty for decision trees by construction of the set of Pareto-optimal points for the corresponding bi-criteria optimization problem. The applications include study of totally optimal (simultaneously optimal relative to a number of cost functions) decision trees for Boolean functions, improvement of bounds on complexity of decision trees for diagnosis of circuits, study of time and memory trade-off for corner point detection, study of decision rules derived from decision trees, creation of new procedure (multi-pruning) for construction of classifiers, and comparison of heuristics for decision tree construction. Part of these extensions (multi-stage optimization) was generalized to well-known combinatorial optimization problems: matrix chain multiplication, binary search trees, global sequence alignment, and optimal paths in directed graphs.
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Azad, Mohammad. "Decision and Inhibitory Trees for Decision Tables with Many-Valued Decisions." Diss., 2018. http://hdl.handle.net/10754/628023.

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Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set of decision trees for the given decision table which was previously only attainable by brute-force algorithms. We study decision tables with many-valued decisions (each row may contain multiple decisions) because they are more reasonable models of data in many cases. To address this problem in a broad sense, we consider not only decision trees but also inhibitory trees where terminal nodes are labeled with “̸= decision”. Inhibitory trees can sometimes describe more knowledge from datasets than decision trees. As for cost functions, we consider depth or average depth to minimize time complexity of trees, and the number of nodes or the number of the terminal, or nonterminal nodes to minimize the space complexity of trees. We investigate the multi-stage optimization of trees relative to some cost functions, and also the possibility to describe the whole set of strictly optimal trees. Furthermore, we study the bi-criteria optimization cost vs. cost and cost vs. uncertainty for decision trees, and cost vs. cost and cost vs. completeness for inhibitory trees. The most interesting application of the developed technique is the creation of multi-pruning and restricted multi-pruning approaches which are useful for knowledge representation and prediction. The experimental results show that decision trees constructed by these approaches can often outperform the decision trees constructed by the CART algorithm. Another application includes the comparison of 12 greedy heuristics for single- and bi-criteria optimization (cost vs. cost) of trees. We also study the three approaches (decision tables with many-valued decisions, decision tables with most common decisions, and decision tables with generalized decisions) to handle inconsistency of decision tables. We also analyze the time complexity of decision and inhibitory trees over arbitrary sets of attributes represented by information systems in the frameworks of local (when we can use in trees only attributes from problem description) and global (when we can use in trees arbitrary attributes from the information system) approaches.
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Book chapters on the topic "Bi-criteria optimization"

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Mankowski, Michal, and Mikhail Moshkov. "Bi-Criteria Optimization of Matchings." In Dynamic Programming Multi-Objective Combinatorial Optimization, 173–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63920-4_16.

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Mankowski, Michal, and Mikhail Moshkov. "Bi-Criteria Optimization of Elements." In Dynamic Programming Multi-Objective Combinatorial Optimization, 35–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63920-4_4.

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AbouEisha, Hassan, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov. "Bi-criteria Optimization of Element Partition Trees." In Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining, 225–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91839-6_15.

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AbouEisha, Hassan, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov. "Bi-criteria Optimization Problem for Decision Trees: Cost Versus Cost." In Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining, 85–104. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91839-6_7.

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AbouEisha, Hassan, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov. "Bi-criteria Optimization Problem for Decision Trees: Cost Versus Uncertainty." In Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining, 105–22. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91839-6_8.

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Turkin, Igor, and Aleksandr Vdovitchenko. "Energy-Efficient Scheduling for Portable Computers as Bi-Criteria Optimization Problem." In Green IT Engineering: Concepts, Models, Complex Systems Architectures, 87–100. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44162-7_5.

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Alsolami, Fawaz, Mohammad Azad, Igor Chikalov, and Mikhail Moshkov. "Bi-criteria Optimization Problem for Decision and Inhibitory Trees: Cost Versus Cost." In Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions, 121–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12854-8_8.

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Alsolami, Fawaz, Mohammad Azad, Igor Chikalov, and Mikhail Moshkov. "Bi-criteria Optimization Problem for Decision (Inhibitory) Trees: Cost Versus Uncertainty (Completeness)." In Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions, 141–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12854-8_9.

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Kim, Bosun, Esma S. Gel, W. Matthew Carlyle, and John W. Fowler. "A New Technique to Compare Algorithms for Bi-criteria Combinatorial Optimization Problems." In Lecture Notes in Economics and Mathematical Systems, 113–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56680-6_10.

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García-Martínez, Carlos, Oscar Cordón, and Francisco Herrera. "An Empirical Analysis of Multiple Objective Ant Colony Optimization Algorithms for the Bi-criteria TSP." In Ant Colony Optimization and Swarm Intelligence, 61–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28646-2_6.

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Conference papers on the topic "Bi-criteria optimization"

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Fadel, Georges, Suman Konda, Vincent Blouin, and Margaret Weicek. "Epsilon-Optimality in Bi-Criteria Optimization." In 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-1233.

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Shidong, Wang, Zheng Li, and Zhang Zhihai. "Bi-ACO: A Tool for Solving Bi-Criteria Optimization." In 2007 IEEE International Conference on Automation and Logistics. IEEE, 2007. http://dx.doi.org/10.1109/ical.2007.4338671.

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Guo, Xiaolei, and Hai Yang. "Bi-Criteria System Optimization with Heterogeneous Users." In Sixth International Conference of Traffic and Transportation Studies Congress (ICTTS). Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40995(322)46.

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Hunt, Brian, Margaret Wiecek, and Georges Fadel. "Matrices as Preference Modeling Tools in Bi-criteria Engineering Design." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4320.

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Verma, Amandeep, and Sakshi Kaushal. "Bi-Criteria Priority based Particle Swarm Optimization workflow scheduling algorithm for cloud." In 2014 Recent Advances in Engineering and Computational Sciences (RAECS). IEEE, 2014. http://dx.doi.org/10.1109/raecs.2014.6799614.

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Gomes, Pedro Henrique, Nelson L. S. da Fonseca, and Omar C. Branquinho. "Bi-Criteria Optimization of Radio Resources for Radio-Over-Fiber Access Networks." In ICC 2011 - 2011 IEEE International Conference on Communications. IEEE, 2011. http://dx.doi.org/10.1109/icc.2011.5962818.

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Khalouli, Safa, Fatima Ghedjati, and Abdelaziz Hamzaoui. "Ant colony optimization for solving a bi-criteria hybrid flow shop problem." In 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2008. http://dx.doi.org/10.1109/icsmc.2008.4811488.

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Junqing Li, Quanke Pan, Shengxian Xie, Jing Liang, Liping Zheng, and Kaizhou Gao. "A hybrid particle swarm optimization algorithm for bi-criteria flexible job-shop scheduling problem." In 2010 Chinese Control and Decision Conference (CCDC). IEEE, 2010. http://dx.doi.org/10.1109/ccdc.2010.5498266.

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Yang, Kaibing, and Xiaobing Liu. "A Bi-criteria Optimization Model and Algorithm for Scheduling in a Real-World Flow Shop with Setup Times." In 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2008. http://dx.doi.org/10.1109/icicta.2008.271.

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Nguyen, Trung Thanh, and Jörg Rothe. "Approximate Pareto Set for Fair and Efficient Allocation: Few Agent Types or Few Resource Types." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/41.

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In fair division of indivisible goods, finding an allocation that satisfies fairness and efficiency simultaneously is highly desired but computationally hard. We solve this problem approximately in polynomial time by modeling it as a bi-criteria optimization problem that can be solved efficiently by determining an approximate Pareto set of bounded size. We focus on two criteria: max-min fairness and utilitarian efficiency, and study this problem for the setting when there are only a few item types or a few agent types. We show in both cases that one can construct an approximate Pareto set in time polynomial in the input size, either by designing a dynamic programming scheme, or a linear-programming algorithm. Our techniques strengthen known methods and can be potentially applied to other notions of fairness and efficiency as well.
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