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Research on Multi-objective Job Shop Scheduling with Dual Particle Swarm Algorithm Based on Greedy Strategy

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Abstract

In order to solve the multi-objective job shop scheduling problem effectively, this paper proposes a greedy strategy to establish the mathematical model of minimizing the maximum completion time, the average flow time and the machine idle time. The algorithm sets up a dual-population structure: leading-population and searching-population. Firstly, the algorithm calculates the fitness values of every particle for each population for the three objectives, and the optimize solutions will be incorporated in the individual optimal value set and the global optimal value set accordingly. Then the algorithm performs a crossover operation which includes two types: one is the leading-cross to lead the searching-population to convergence to the optimal solution quickly, and the other is intro-population crossover of each population in order to increase the diversity of individuals. The mutation operation of each population is followed to further enhance the diversity of individuals. And the simulated annealing algorithm is employed to avoid the algorithm falling into the local optimum effectively. The simulation experiments of various scale instances are carried out and compared with the simulation results of the other two popular algorithms to verify the effectiveness of the algorithm. The simulation results show that the proposed algorithm is superior to the other two algorithms in both the quality of the solution and the time-consuming.

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References

  1. Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research, 1(2), 117–129.

    Article  MathSciNet  Google Scholar 

  2. Ma, P. C., Tao, F., Liu, Y. L., et al. (2014). A hybrid particle swarm optimization and simulated annealing algorithm for job-shop scheduling. In IEEE international conference on automation science and engineering. IEEE (pp. 125–130).

  3. Ryan, D. M., & Foster, B. A. (1981). An integer programming approach to scheduling. Publication of Elsevier Science Publishers Bv, 1, 269–280.

    Google Scholar 

  4. Brucker, P., Jurisch, B., & Sievers, B. (1998). A branch and bound algorithm for the job-shop scheduling problem. In A. Drexl & A. Kimms (Eds.), Beyond manufacturing resource planning (MRP II) (pp. 107–127). Berlin: Springer.

    Google Scholar 

  5. Nawaz, M., Enscore, E. E., Jr., & Ham, I. (1983). A heuristic algorithm for the m -machine, n -job flow-shop sequencing problem. Omega, 11(1), 91–95.

    Article  Google Scholar 

  6. Willems, T. M., & Rooda, J. E. (1994). Neural networks for job-shop scheduling. Control Engineering Practice, 2(1), 31–39.

    Article  Google Scholar 

  7. Yahyaoui, A., Fnaiech, N., & Fnaiech, F. (2011). A suitable initialization procedure for speeding a neural network job-shop scheduling. Industrial Electronics IEEE Transactions, 58(3), 1052–1060.

    Article  Google Scholar 

  8. Zhou, D. N., Cherkassky, V., Baldwin, T. R., et al. (1991). A neural network approach to job-shop scheduling. IEEE Transactions on Neural Networks, 2(1), 175–179.

    Article  Google Scholar 

  9. Sun, Y., & Xiong, H. (2009). Job-shop scheduling problem based on particle swarm optimization algorithm. Information Technology, 21(12), 486–489.

    Google Scholar 

  10. Adewumi, A. O., & Arasomwan, A. M. (2014). Improved particle swarm optimization based on greedy and adaptive features. In IEEE (pp. 237–242).

  11. Liu, X., Jiao, X., Li, Y., et al. (2013). Improved new particle swarm algorithm solving job shop scheduling optimization problem. In International conference on computer science and network technology, IEEE (pp. 148–150).

  12. Surekha, P., Raajan, P. M., & Sumathi, S. (2010). Genetic algorithm and particle swarm optimization approaches to solve combinatorial job shop scheduling problems. In IEEE international conference on computational intelligence and computing research, IEEE (pp. 1–5).

  13. Lian, Z., Gu, X., & Jiao, B. (2006). A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan. Applied Mathematics and Computation, 183(2), 1008–1017.

    Article  MathSciNet  Google Scholar 

  14. Tang, H., & Ye, C. (2011). A hybrid particle swarm optimization for job shop scheduling. In International Conference on Information Management, Innovation Management and Industrial Engineering (Vol. 28, pp. 187–190). IEEE.

  15. Sha, D. Y., & Hsu, C. Y. (2006). A hybrid particle swarm optimization for job shop scheduling problem. Computers & Industrial Engineering, 51(4), 791–808.

    Article  Google Scholar 

  16. Li, J., Pan, Q., Xie, S., et al. (2011). A hybrid algorithm for multi-objective job shop scheduling problem (pp. 3630–3634).

  17. Sha, D. Y., & Lin, H. H. (2010). A multi-objective PSO for job-shop scheduling problems. Expert Systems with Applications An International Journal, 37(2), 1065–1070.

    Article  Google Scholar 

  18. Tavakkoli-Moghaddam, R., Azarkish, M., & Sadeghnejad-Barkousaraie, A. (2011). A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem. Expert Systems with Applications An International Journal, 38(9), 10812–10821.

    Article  Google Scholar 

  19. Tavakkoli-Moghaddam, R., Azarkish, M., & Sadeghnejad, A. (2010). A new hybrid multi-objective Pareto archive pSO algorithm for a classic job shop scheduling problem with ready times. Expert Systems with Applications, 93, 61–68.

    MATH  Google Scholar 

  20. Qin, C., Zhu, J., & Zheng, J. (2009). Hybrid evolutionary algorithm for multi-objective job shop scheduling. https://doi.org/10.1109/ICICISYS.2009.5358297.

  21. Ripon, K. S. N. (2007). Hybrid evolutionary approach for multi-objective job-shop scheduling problem. Malaysian Journal of Computer Science, 20(2), 183–198.

    Article  Google Scholar 

  22. Lee, K. B., & Kim, J. H. Multi-objective particle swarm optimization with preference-based sorting. In Evolutionary computation, IEEE (pp. 2506–2513).

  23. Piroozfard, H., & Wong, K. Y. (2015). Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm. In International conference on mathematics. AIP Publishing LLC (pp. 225–251).

  24. Geyik, F., & Cedimoglu, I. H. (2004). The Strategies and parameters of tabu search for job-shop scheduling. Journal of Intelligent Manufacturing, 15, 439–448.

    Article  Google Scholar 

  25. Kennedy, J., & Eberhart, R. (1995) Particle swarm optimization. In IEEE international conference on neural networks, 1995. Proceedings. IEEE Xplore (vol. 4, pp. 1942–1948).

  26. Zhang, H., Li, X., Li, H., et al. (2005). Particle swarm optimization-based schemes for resource-constrained project scheduling. Automation in Construction, 15(3), 393–404.

    Article  Google Scholar 

  27. Meng, Q., Zhang, L., & Fan, Y. (2016). A hybrid particle swarm optimization algorithm for solving job shop scheduling problems. In Asian Simulation Conference (pp. 71–78). Singapore: Springer.

    Chapter  Google Scholar 

  28. Montgomery, D. C. (2005). Design and analysis of experiments. Arizona: John Wiley & Sons.

    MATH  Google Scholar 

  29. Beasley, J. E. (1990). OR-Library: Distributing test problems by electronic mail. Journal of the Operational Research Society, 41(11), 1069–1072.

    Article  Google Scholar 

  30. Hu, N., & Wang, P. (2011). An algorithm for solving flexible job shop scheduling problems based on multi-objective particle swarm optimization. In International symposium on information science and engineering, IEEE (pp. 507–511).

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Acknowledgements

We are very grateful for the support of the National Science and Technology Supporting Plan of China No. 2012BAF15G01.

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Authors and Affiliations

  1. State CIMS Engineering Research Center at Tsinghua University, Beijing, 100084, China

    Qiaofeng Meng, Linxuan Zhang & Yushun Fan

  2. The PLA Rocket Force Command College, Wuhan, 430012, China

    Qiaofeng Meng

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  1. Qiaofeng Meng
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  2. Linxuan Zhang
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  3. Yushun Fan
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Correspondence to Qiaofeng Meng.

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Meng, Q., Zhang, L. & Fan, Y. Research on Multi-objective Job Shop Scheduling with Dual Particle Swarm Algorithm Based on Greedy Strategy. Wireless Pers Commun 103, 255–274 (2018). https://doi.org/10.1007/s11277-018-5440-z

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