Solving the multi-objective flexible job shop scheduling problem with a novel parallel branch and bound algorithm

Swarm and Evolutionary Computation - Tập 53 - Trang 100632 - 2020
Carlos Soto1,2, Bernabé Dorronsoro1, Héctor Fraire2, Laura Cruz-Reyes2, Claudia Gomez-Santillan2, Nelson Rangel2
1Escuela Superior de Ingeniería, Universidad de Cádiz, Avda. Universidad de Cádiz, 10, 11519, Campus Universitario de Puerto Real, Cádiz, Spain
2Instituto Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, Av. 1o. de Mayo, 89440, Ciudad Madero, Tamaulipas, Mexico

Tài liệu tham khảo

Talbi, 2015, Hybrid metaheuristics for multi-objective optimization, J. Algorithms Comput. Technol., 9, 41, 10.1260/1748-3018.9.1.41

Deb, 2001, vol. 16

Coello, 2007

Talbi, 2008, Parallel approaches for multiobjective optimization, 349

Lu, 2018, Analysis of the similarities and differences of job-based scheduling problems, Eur. J. Oper. Res., 270, 809, 10.1016/j.ejor.2018.01.051

Burdett, 2018, An integrated approach for scheduling health care activities in a hospital, Eur. J. Oper. Res., 264, 756, 10.1016/j.ejor.2017.06.051

Deng, 2017, A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem, Swarm and Evolutionary Computation, 32, 121, 10.1016/j.swevo.2016.06.002

Han, 2019, Evolutionary multiobjective blocking lot-streaming flow shop scheduling with machine breakdowns, IEEE Transactions on Cybernetics, 49, 184, 10.1109/TCYB.2017.2771213

Lin, 2017, A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem, Swarm and Evolutionary Computation, 36, 124, 10.1016/j.swevo.2017.04.007

Cinar, 2015, A taxonomy for the flexible job shop scheduling problem, 17, 10.1007/978-3-319-18567-5_2

Chaudhry, 2016, A research survey: review of flexible job shop scheduling techniques, Int. Trans. Oper. Res., 23, 551, 10.1111/itor.12199

Fattahi, 2007, Mathematical modeling and heuristic approaches to flexible job shop scheduling problems, J. Intell. Manuf., 18, 331, 10.1007/s10845-007-0026-8

Kacem, 2002, Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic, Math. Comput. Simulat., 60, 245, 10.1016/S0378-4754(02)00019-8

Vincent, 2013, Multiple objective branch and bound for mixed 0-1 linear programming: corrections and improvements for the biobjective case, Comput. Oper. Res., 40, 498, 10.1016/j.cor.2012.08.003

Yang, 2013, A grid-based evolutionary algorithm for many-objective optimization, IEEE Trans. Evol. Comput., 17, 721, 10.1109/TEVC.2012.2227145

Mezmaz, 2014, A multi-core parallel branch-and-bound algorithm using factorial number syste, 1203

Brucker, 2007, vol. 93

Amjad, 2018, Recent research trends in genetic algorithm based flexible job shop scheduling problems, Math. Probl. Eng., 2018, 1, 10.1155/2018/9270802

Fuchigami, 2018, A survey of case studies in production scheduling: analysis and perspectives, Journal of Computational Science, 25, 425, 10.1016/j.jocs.2017.06.004

Genova, 2015, A survey of solving approaches for multiple objective flexible job shop scheduling problems, Cybern. Inf. Technol., 15, 3

Rabiee, 2012, Bi-objective partial flexible job shop scheduling problem: Nsga-ii, nrga, moga and paes approaches, Int. J. Prod. Res., 50, 7327, 10.1080/00207543.2011.648280

Phu-ang, 2017, Memetic algorithm based on marriage in honey bees optimization for flexible job shop scheduling problem, Memetic Computing, 9, 295, 10.1007/s12293-017-0230-9

Wang, 2012, An effective artificial bee colony algorithm for the flexible job-shop scheduling problem, Int. J. Adv. Manuf. Technol., 60, 303, 10.1007/s00170-011-3610-1

Wang, 2017, Flexible job shop scheduling problem using an improved ant colony optimization, Sci. Program., 2017, 1

Zhang, 2009, An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem, Comput. Ind. Eng., 56, 1309, 10.1016/j.cie.2008.07.021

Moslehi, 2011, A pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search, Int. J. Prod. Econ., 129, 14, 10.1016/j.ijpe.2010.08.004

Teekeng, 2016, A new algorithm for flexible job-shop scheduling problem based on particle swarm optimization, Artif. Life Robot., 21, 18, 10.1007/s10015-015-0259-0

Cruz-Chávez, 2017, Accelerated simulated annealing algorithm applied to the flexible job shop scheduling problem, Int. Trans. Oper. Res., 24, 1119, 10.1111/itor.12195

Kacem, 2002, Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 32, 1, 10.1109/TSMCC.2002.1009117

Ho, 2008, Solving multiple-objective flexible job shop problems by evolution and local search, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38, 674, 10.1109/TSMCC.2008.923888

Frutos, 2015, Choice of a pisa selector in a hybrid algorithmic structure for the fjssp, Decision Science Letters, 4, 247, 10.5267/j.dsl.2014.11.001

Fattahi, 2009, A hybrid multi objective algorithm for flexible job shop scheduling, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 3, 178

Hansmann, 2014, Flexible job shop scheduling with blockages, Math. Methods Oper. Res., 79, 135, 10.1007/s00186-013-0456-3

Roshanaei, 2013, Mathematical modelling and a meta-heuristic for flexible job shop scheduling, Int. J. Prod. Res., 51, 6247, 10.1080/00207543.2013.827806

Morrison, 2016, Branch-and-bound algorithms: a survey of recent advances in searching, branching, and pruning, Discrete Optim., 19, 79, 10.1016/j.disopt.2016.01.005

Sourd, 2008, A multiobjective branch-and-bound framework: application to the biobjective spanning tree problem, Inf. J. Comput., 20, 472, 10.1287/ijoc.1070.0260

Przybylski, 2017, Multi-objective branch and bound, Eur. J. Oper. Res., 260, 856, 10.1016/j.ejor.2017.01.032

Paulavičius, 2011, Parallel branch and bound for global optimization with combination of lipschitz bounds, Optim. Methods Softw., 26, 487, 10.1080/10556788.2010.551537

Zhang, 2008

Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017

Melab, 2015, Parallel branch-and-bound using private ivm-based work stealing on xeon phi mic coprocessor, 394

Rönngren, 1997, A comparative study of parallel and sequential priority queue algorithms, ACM Trans. Model Comput. Simulat, 7, 157, 10.1145/249204.249205

Pacheco, 2011

Mesghouni, 1999

Kacem, 2001, Approach by localization and genetic manipulation algorithm for flexible job-shop scheduling problem, vol. 4, 2599

Pezzella, 2008, A genetic algorithm for the flexible job-shop scheduling problem, Comput. Oper. Res., 35, 3202, 10.1016/j.cor.2007.02.014

Wang, 2017, A novel memetic algorithm based on decomposition for multiobjective flexible job shop scheduling problem, Math. Probl. Eng., 2017, 10.1155/2017/2857564

Zitzler, 2003, Performance assessment of multiobjective optimizers: an analysis and review, IEEE Trans. Evol. Comput., 7, 1214, 10.1109/TEVC.2003.810758

Veldhuizen, 1998, Multiobjective evolutionary algorithm research: a history and analysis, Analysis, 1998, 1

Ishibuchi, 2015, A study on performance evaluation ability of a modified inverted generational distance indicator, 695

van Veldhuizen, 1999, Multiobjective evolutionary algorithm test suites, 351

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Swarm and Evolutionary Computation

Tập 53

100632

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