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Journal of Intelligent Manufacturing

Erschienen in:

06.01.2018

Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm

verfasst von: Yaping Fu, Hongfeng Wang, Guangdong Tian, Zhiwu Li, Hesuan Hu

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 5/2019

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Abstract

Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem.

Vorheriger Artikel A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction

Nächster Artikel A Cloud-based solution/reference architecture for establishing collaborative networked organizations

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Bai, L. P., Wu, N. Q., Li, Z. W., & Zhou, M. C. (2016). Optimal one-wafer cyclic scheduling and buffer space configuration for single-arm multicluster tools with linear topology. IEEE Transactions on Systems Man & Cybernetics Systems, 46(10), 1456–1467.

Branke, J., Su, N., Pickardt, C. W., & Zhang, M. J. (2016). Automated design of production scheduling heuristics: A review. IEEE Transactions on Evolutionary Computation, 20(1), 110–114.

Cai, X. Y., Li, Y. X., Fan, Z., & Zhang, Q. F. (2015). An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization. IEEE Transactions on Evolutionary Computation, 19(4), 508–523.

Chang, P. C., Chen, S. H., Zhang, Q. F., & Lin, J. L. (2008). MOEA/D for flowshop scheduling problems. In Proceeding of 2008 IEEE congress on evolutionary computation (pp. 1433–1438).

Cheng, M., Sun, S., & He, L. (2014). Discrete optimization flow shop scheduling problems with deteriorating jobs on no-idle dominant machines. European Journal of Operational Research, 183(1), 58–62.

Cheng, M., Tadikamalla, P. R., Shang, J., & Zhang, B. (2014a). Two-machine flow shop scheduling with deteriorating jobs: Minimizing the weighted sum of makespan and total completion time. Journal of the Operational Research Society, 66(5), 709–719.

Cheng, M., Tadikamalla, P. R., Shang, J., & Zhang, S. Q. (2014b). Bicriteria hierarchical optimization of two-machine flow shop scheduling problem with time-dependent deteriorating jobs. European Journal of Operational Research, 234(3), 650–657.

Deb, K. (2014). Multi-objective optimization search methodologies (pp. 403–449). US: Springer.

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

Fan, B. Q., & Cheng, T. C. E. (2016). Two-agent scheduling in a flowshop. European Journal of Operational Research, 252(2), 376–384.

Fu, Y. P., Wang, H. F., & Huang, M. (2014). Locate multiple pareto optima using a species-based multi-objective genetic algorithm. In Proceeding of 2014 International Conference on Bio-inspired Computing: Theories and Applications (pp. 128–137).

Fu, Y. P., Wang, H. F., Huang, M., Ding, J. L., & Tian, G. D. (2017) Multiobjective flow shop deteriorating scheduling problem via an adaptive multipopulation genetic algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. https://doi.org/10.1177/0954405417691553.

Fu, Y. P., Wang, H. F., Huang, M., & Wang, J. W. (2016). A decomposition based multiobjective genetic algorithm with adaptive multipopulation strategy for flowshop scheduling problem. Natural Computing. https://doi.org/10.1007/s1104.

Gen, M., & Lin, L. (2014). Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849–866.

Gupta, J. N. D., & Gupta, S. K. (1988). Single facility scheduling with nonlinear processing times. Computers & Industrial Engineering, 14(44), 387–393.

Hou, Y., Wu, N. Q., Zhou, M. C., & Li, Z. W. (2017). Pareto-optimization for scheduling of crude oil operations in refinery via genetic algorithm. IEEE Transactions on Systems Man & Cybernetics Systems, 47(3), 517–530.

Hu, H. S., Zhou, C. M., Li, Z. W., & Tang, Y. (2013). An optimization approach to improved Petri net controller design for automated manufacturing systems. IEEE Transactions on Automation Science & Engineering, 10(3), 772–782.

Ishibuchi, H., & Murata, T. (1998). A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems Man & Cybernetics Part C Applications & Reviews, 28(3), 392–403.

Ishibuchi, H., Yoshida, T., & Murata, T. (2003). Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation, 7(2), 204–223.

Jiang, Z. G., Zhou, T. T., Zhang, H., Wang, Y., Cao, H. J., & Tian, G. D. (2016). Reliability and cost optimization for remanufacturing process planning. Journal of Cleaner Production, 135, 1602–1610.

Jin, L. L., Zhang, C. Y., & Shao, X. Y. (2015). An effective hybrid honey bee mating optimization algorithm for integrated process planning and scheduling problems. The International Journal of Advanced Manufacturing Technology, 80(5), 1253–1264.

Lee, W. C., Chen, S. K., Chen, C. W., & Wu, C. C. (2011). A two-machine flowshop problem with two agents. Computers & Operations Research, 38(1), 98–104.

Lee, W. C., Yen, W. C., & Chung, Y. H. (2014). Total tardiness minimization in permutation flowshop with deterioration consideration. Applied Mathematical Modelling, 38(13), 3081–3092.

Lei, D. (2015). Variable neighborhood search for two-agent flow shop scheduling problem. Computers & Industrial Engineering, 80, 125–131.

Li, J. Q., Pan, Q. K., & Mao, K. (2016). A hybrid fruit fly optimization algorithm for the realistic hybrid flowshop rescheduling problem in steelmaking systems. IEEE Transactions on Automation Science & Engineering, 13(2), 932–949.

Lin, S. W., & Ying, K. C. (2013). Minimizing makespan and total flowtime in permutation flowshops by a bi-objective multi-start simulated-annealing algorithm. Computers & Operations Research, 40(6), 1625–1647.

Liu, H. L., Chen, L., Deb, K., & Goodman, E. (2016). Investigating the effect of imbalance between convergence and diversity in evolutionary multi-objective algorithms. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2016.2606577.

Liu, L. L., Wang, D. W., & Ip, W. H. (2009). A permutation-based dual genetic algorithm for dynamic optimization problems. Soft Computing, 13(7), 725–738.

Liu, Y. F., Pan, Q. K., & Chai, T. Y. (2015). Magnetic material group furnace problem modeling and the specialization of the genetic algorithm. IEEE Transactions on Engineering Management, 62(1), 51–64.

Long, J. Y., Zheng, Z., Gao, X. Q., & Pardalos, P. M. (2016). A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants. Journal of the Operational Research Society. https://doi.org/10.1057/jors.2016.17.

Luo, W. C., Chen, L., & Zhang, G. C. (2012). Approximation schemes for two-machine flow shop scheduling with two agents. Journal of Combinatorial Optimization, 24, 229–239.

Ma, X. L., Liu, F., Qi, Y. T., Wang, X. D., Li, L. L., Jiao, L. C., et al. (2016). A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables. IEEE Transactions on Evolutionary Computation, 20(2), 275–298.

Miettinen, K. (2012). Nonlinear multiobjective optimization. Berlin: Springer.

Mor, B., & Mosheiov, G. (2014). Polynomial time solutions for scheduling problems on a proportionate flowshop with two competing agents. Journal of Operational Research Society, 65, 151–157.

Pinedo, M. (2012). Scheduling, theory, algorithms and systems. New Jersey: Prentice Hall.

Tian, G. D., Zhang, H., Feng, Y., Wang, D., Peng, Y., & Jia, H. (2018). Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method. Renewable and Sustainable Energy Reviews, 81(1), 682–692.

Tian, G. D., Zhou, M. C., & Chu, J. W. (2013). A chance constrained programming approach to determine the optimal disassembly sequence. IEEE Transactions on Automation Science & Engineering, 10(4), 1004–1013.

Tian, G. D., Zhou, M. C., Li, P. G., Zhang, C. Y., & Jia, H. F. (2016). Multiobjective optimization models for locating vehicle inspection stations subject to stochastic demand, varying velocity and regional constraints. IEEE Transactions on Intelligent Transportation Systems, 17(7), 1978–1986.

Trivedi, A., Srinivasan, D., Sanyal, K., & Ghosh, A. (2016). A survey of multiobjective evolutionary algorithms based on decomposition. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2016.2608507.

Wang, H. F., Fu, Y. P., Huang, M., & Wang, J. W. (2015). Multiobjective optimisation design for enterprise system operation in the case of scheduling problem with deteriorating jobs. Enterprise Information Systems, 10(3), 1–18.

Wang, J. B., & Wang, M. Z. (2013). Solution algorithms for the total weighted completion time minimization flow shop scheduling with decreasing linear deterioration. International Journal of Advanced Manufacturing Technology, 67(1–4), 243–253.

Wang, S. Y., Wang, L., Liu, M., & Xu, Y. (2013). An enhanced estimation of distribution algorithm for solving hybrid flow-shop scheduling problem with identical parallel machines. International Journal of Advanced Manufacturing Technology, 68(9–12), 2043–2056.

Wang, X., Khemaissia, I., Khalgui, M., Li, Z. W., Mosbahi, O., & Zhou, M. C. (2015). Dynamic low-power reconfiguration of real-time systems with periodic and probabilistic tasks. IEEE Transactions on Automation Science and Engineering, 12(1), 258–271.

Wang, X., Li, Z. W., & Wonham, W. M. (2016). Dynamic multiple-period reconfiguration of real-time scheduling based on timed DES supervisory control. IEEE Transactions on Industrial Informatics, 12(1), 101–111.

Wang, X. Y., & Wang, J. J. (2014). Scheduling deteriorating jobs with a learning effect on unrelated parallel machines. Applied Mathematical Modelling, 38(21), 5231–5238.

Yin, Y., Wu, W. H., Cheng, T. C. E., & Wu, C. C. (2014). Due-date assignment and single-machine scheduling with generalised position-dependent deteriorating jobs and deteriorating multi-maintenance activities. International Journal of Production Research, 52(8), 2311–2326.

Zhang, Q. F., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.

Zhang, Q. F., Zhou, A. M., & Jin, Y. (2008). RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation, 12(1), 41–63.

Zhang, J. R., Tang, Q. H., Li, P., Deng, D. X., & Che, Y. L. (2016). A modified MOEA/D approach to the solution of multi-objective optimal power flow problem. Applied Soft Computing, 47, 494–514.

Zhou, A. M., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhang, Q. F. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm & Evolutionary Computation, 1(1), 32–49.

Zhou, A. M., & Zhang, Q. F. (2016). Are all the subproblems equally important? Resource allocation in decomposition-based multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 20(1), 52–64.

Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & Fonseca, V. G. D. (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE transactions on evolutionary computation, 7(2), 117–132.

Titel: Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm
verfasst von: Yaping Fu
Hongfeng Wang
Guangdong Tian
Zhiwu Li
Hesuan Hu
Publikationsdatum: 06.01.2018
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 5/2019
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-017-1385-4

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