Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem
Journal of King Saud University - Computer and Information Sciences - Tập 34 - Trang 2926-2937 - 2022
Tài liệu tham khảo
Hmida, 2010, Discrepancy search for the flexible job shop scheduling problem, Comput. Oper. Res., 37, 2192, 10.1016/j.cor.2010.03.009
AitZai, 2013, Parallel branch-and-bound and parallel PSO algorithms for job shop scheduling problem with blocking, Int. J. Oper. Res., 16, 14, 10.1504/IJOR.2013.050538
Golmakani, 2014, An artificial immune algorithm for multiple-route job shop scheduling problem with preventive maintenance constraints, Int. J. Oper. Res., 19, 457, 10.1504/IJOR.2014.060414
Ahani, 2014, A tabu search algorithm for no-wait job shop scheduling problem, Int. J. Oper. Res., 19, 246, 10.1504/IJOR.2014.058954
Ho, 2007, An effective architecture for learning and evolving flexible job-shop schedules, Eur. J. Oper. Res., 179, 316, 10.1016/j.ejor.2006.04.007
Garey, 1976, The complexity of flowshop and jobshop scheduling, Math. Oper. Res., 1, 117, 10.1287/moor.1.2.117
Tay, 2004, An effective chromosome representation for evolving flexible job shop schedules, Gen. Evol. Comput. Conf., 210
Genova, 2015, A survey of solving approaches for multiple objective flexible job shop scheduling problems, Cybernet. Inf. Technol., 15, 3, 10.1515/cait-2015-0025
Zhang, 2017, Using blind optimization algorithm for hardware/software partitioning, IEEE Access, 5, 1353, 10.1109/ACCESS.2017.2669481
Duan, 2004, Development on ant colony algorithm theory and its application, Control Decis., 19, 1321
Sengupta, 2019, Particle Swarm Optimization: a survey of historical and recent developments with hybridization perspectives, Mach. Learning Knowl. Extr., 1, 157, 10.3390/make1010010
Yu, Y., Tian, Y.-f., Yin, Z.-f., 2005. Multiuser detector based on adaptive artificial fish school algorithm. In: IEEE International Symposium on Communications and Information Technology, 2005. ISCIT 2005, pp. 1480–1484.
Eusuff, 2006, Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization, Eng. Optim., 38, 129, 10.1080/03052150500384759
Karaboga, 2007, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, J. Global Optim., 39, 459, 10.1007/s10898-007-9149-x
Alzaqebah, 2016, Modified artificial bee colony for the vehicle routing problems with time windows, SpringerPlus, 5, 1298, 10.1186/s40064-016-2940-8
Dai, 2016, Manipulator path-planning avoiding obstacle based on screw theory and ant colony algorithm, J. Comput. Theor. Nanosci., 13, 922, 10.1166/jctn.2016.4894
Hashim, 2016, Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm, J. Network Comput. Appl., 64, 239, 10.1016/j.jnca.2015.09.013
Fong, 2015, Accelerated PSO swarm search feature selection for data stream mining big data, IEEE Trans. Serv. Comput., 9, 33, 10.1109/TSC.2015.2439695
Alzaqebah, Am., Abdullah, S., Malkawi, R., Jawarneh, A., in press. Self-adaptive bee colony optimisation algorithm for the flexible job shop scheduling problem. Int. J. Oper. Res.
Qin, 2015, Vehicle routing problem based on heuristic artificial fish school algorithm, Appl. Mech. Mater., 56
Alzaqebah, 2018, Bees algorithm for vehicle routing problems with time windows, Int. J. Mach. Learning Comput., 8, 234
Alzaqebah, M.A., Alrefai, A., Ahmed, E., Jawarneh, A., Alsmadi, A., 2020. Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems. Int. J. Electr. Comput. Eng. 10.
Zhang, 2018, Optimal local dimming based on an improved shuffled frog leaping algorithm, IEEE Access, 6, 40472, 10.1109/ACCESS.2018.2858827
Zheng, 2014, Comparative study of heuristics algorithms in solving flexible job shop scheduling problem with condition based maintenance, J. Ind. Eng. Manage. (JIEM), 7, 518
Shao, 2013, Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem, Int. J. Adv. Manuf. Technol., 67, 2885, 10.1007/s00170-012-4701-3
Nouiri, 2017, Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns, Comput. Ind. Eng., 112, 595, 10.1016/j.cie.2017.03.006
Wang, 2017, Flexible job shop scheduling problem using an improved ant colony optimization, Sci. Program., 2017
Bagheri, 2010, An artificial immune algorithm for the flexible job-shop scheduling problem, Fut. Gener. Comput. Syst., 26, 533, 10.1016/j.future.2009.10.004
Dorigo, M., Stützle, T., 2004. Ant colony optimization. Cambridge, Massachusetts: A Bradford Book, MIT Press
Shi, Y., 2011. Brain storm optimization algorithm. In: International conference in swarm intelligence, pp. 303–309.
Cheng, 2016, Brain storm optimization algorithm: a review, Artif. Intell. Rev., 46, 445, 10.1007/s10462-016-9471-0
Jordehi, 2015, Brainstorm optimisation algorithm (BSOA): An efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems, Int. J. Electr. Power Energy Syst., 69, 48, 10.1016/j.ijepes.2014.12.083
Mafteiu-Scai, 2015, A new approach for solving equations systems inspired from brainstorming, IJNCAA, 5, 10, 10.17781/P001642
Chen, J., Cheng, S., Chen, Y., Xie, Y., Shi, Y., 2015. Enhanced brain storm optimization algorithm for wireless sensor networks deployment. In: International Conference in Swarm Intelligence, pp. 373–381.
Qiu, 2014, Receding horizon control for multiple UAV formation flight based on modified brain storm optimization, Nonlinear Dyn., 78, 1973, 10.1007/s11071-014-1579-7
Zhan, Z.-h., Zhang, J., Shi, Y.-h., Liu, H.-l., 2012. A modified brain storm optimization. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8.
Zhou, D., Shi, Y., Cheng, S., 2012. Brain storm optimization algorithm with modified step-size and individual generation. In: International Conference in Swarm Intelligence, pp. 243–252.
Yang, Z., Shi, Y., 2015. Brain storm optimization with chaotic operation. In: 2015 Seventh International Conference on Advanced Computational Intelligence (ICACI), pp. 111–115.
Yang, 2015, Advanced discussion mechanism-based brain storm optimization algorithm, Soft. Comput., 19, 2997, 10.1007/s00500-014-1463-x
Krishnanand, K., Hasani, S.M.F., Panigrahi, B.K., Panda, S.K., 2013. Optimal power flow solution using self–evolving brain–storming inclusive teaching–learning–based algorithm. In: International Conference in Swarm Intelligence, pp. 338–345.
Jia, 2016, Hybrid brain storm optimisation and simulated annealing algorithm for continuous optimisation problems, Int. J. Bio-Inspired Comput., 8, 109, 10.1504/IJBIC.2016.076326
Talbi, E.-G., 2009. Metaheuristics: from design to implementation, vol. 74. John Wiley & Sons, 2009.
Yang, X.-S., Cui, Z., Xiao, R., Gandomi, A.H., Karamanoglu, M., 2013. Swarm intelligence and bio-inspired computation: theory and applications. Newnes.
Fattahi, 2007, Mathematical modeling and heuristic approaches to flexible job shop scheduling problems, J. Intell. Manuf., 18, 331, 10.1007/s10845-007-0026-8
Osborn, 2012, Applied Imagination-Principles and Procedures of Creative Writing, Read Books Ltd
Zhang, 2011, An effective genetic algorithm for the flexible job-shop scheduling problem, Expert Syst. Appl., 38, 3563, 10.1016/j.eswa.2010.08.145
Drugan, M.M., Talbi, E.-G., 2014. Adaptive multi-operator metaheuristics for quadratic assignment problems. In: EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, ed. Springer, pp. 149–163.
Brandimarte, 1993, Routing and scheduling in a flexible job shop by tabu search, Ann. Oper. Res., 41, 157, 10.1007/BF02023073
Mastrolilli, 2000, Effective neighbourhood functions for the flexible job shop problem, J. Sched., 3, 3, 10.1002/(SICI)1099-1425(200001/02)3:1<3::AID-JOS32>3.0.CO;2-Y
Murovec, 2004, A repairing technique for the local search of the job-shop problem, Eur. J. Oper. Res., 153, 220, 10.1016/S0377-2217(02)00733-6
Burke, E.K., Bykov, Y., 2008. A late acceptance strategy in hill-climbing for exam timetabling problems. In: PATAT 2008 Conference, Montreal, Canada, pp. 1–7.
Turky, A., Sabar, N.R., Sattar, A., Song, A., 2016. Parallel late acceptance hill-climbing algorithm for the Google machine reassignment problem. In: Australasian Joint Conference on Artificial Intelligence, pp. 163–174.
Alzaqebah, 2014, An adaptive artificial bee colony and late-acceptance hill-climbing algorithm for examination timetabling, J. Sched., 17, 249, 10.1007/s10951-013-0352-y
Bazargani, 2018, Late acceptance hill climbing for constrained covering arrays, 778
Fonseca, 2016, Late acceptance hill-climbing for high school timetabling, J. Sched., 19, 453, 10.1007/s10951-015-0458-5
Barnes, J., Chambers, J., 1996. Flexible job shop scheduling by tabu search, Graduate program in operations research and industrial engineering. The University of Texas at Austin.
Fisher, H., 1963. Probabilistic learning combinations of local job-shop scheduling rules. Industrial scheduling, pp. 225–251.
Lawrence, S., 1984. Resouce constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement). Graduate School of Industrial Administration, Carnegie-Mellon University.
Bholowalia, P., Kumar, A., 2014. EBK-means: A clustering technique based on elbow method and k-means in WSN. Int. J. Comput. Appl. 105.
Friedman, 1940, A comparison of alternative tests of significance for the problem of m rankings, Ann. Math. Stat., 11, 86, 10.1214/aoms/1177731944
Holm, S., 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 65–70.
Bożejko, 2010, The new golf neighborhood for the exible job shop problem, Proc. Comput. Sci., 1, 289, 10.1016/j.procs.2010.04.032
Nouri, 2018, “Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model, J. Ind. Eng. Int., 14, 1, 10.1007/s40092-017-0204-z
Rahmati, 2012, A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem, Int. J. Adv. Manuf. Technol., 58, 1115, 10.1007/s00170-011-3437-9
Henchiri, A., Ennigrou, M., 2013. Particle swarm optimization combined with tabu search in a multi-agent model for flexible job shop problem. In: International Conference in Swarm Intelligence, pp. 385–394.
Nouiri, 2018, An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem, J. Intell. Manuf., 29, 603, 10.1007/s10845-015-1039-3
Zarrouk, 2019, A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem, Swarm Intell., 13, 145, 10.1007/s11721-019-00167-w
Ennigrou, 2008, New local diversification techniques for flexible job shop scheduling problem with a multi-agent approach, Auton. Agent. Multi-Agent Syst., 17, 270, 10.1007/s10458-008-9031-3
Hurink, 1994, Tabu search for the job-shop scheduling problem with multi-purpose machines, Oper.-Res.-Spektrum, 15, 205, 10.1007/BF01719451
Jurisch, B. (1992). Scheduling jobs in shops with multi-purpose machines (Doctoral dissertation, Verlag nicht ermittelbar).