Abstract
This paper investigates the application of particle swarm optimization (PSO) to the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time. To achieve this goal, alternative particle representations and problem mapping mechanisms were implemented within the PSO paradigm. This resulted in the development of four PSO-based heuristics. Benchmarking on real customer data indicated that using the priority-based representation resulted in a significant performance improvement over the existing rule-based algorithms commonly used to solve this problem. Additional investigation into algorithm scalability led to the development of a priority-based differential evolution algorithm. Apart from the academic significance of the paper, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability, and overall competitive advantage.
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References
Abdedda’im, Y., & Maler, O. (2002). Pre-emptive job shop scheduling problem using stopwatch automata. In Proceedings of the 8th international conference on tools and algorithms for the construction and analysis of systems (pp. 113–126).
Aldakhilallah, K. A., & Ramesh, R. (2001). Cyclic scheduling heuristics for a re-entrant job shop manufacturing environment. International Journal of Production Research, 39(12), 2635–2657.
Anghinolfi, D., & Paolucci, M. (2009). A new discrete particle swarm optimization approach for the single-machine total weighted tardiness scheduling problem with sequence-dependent setup times. European Journal of Operational Research, 193(1), 73–85.
Aydin, M. E., & Oztemel, E. (2000). Dynamic job-shop scheduling using reinforcement learning agents. Robotics and Autonomous Systems, 33, 169–178.
Bertel, S., & Billaut, J. C. (2004). A genetic algorithm for an industrial multiprocessor flow shop scheduling problem with recirculation. European Journal of Operational Research, 159, 651–662.
Blazewicz, J., Domschke, W., & Pesch, E. (1996). The job shop scheduling problem: Conventional and new solution techniques. European Journal of Operational Research, 93, 1–33.
Brizuela, C.A., Zhao, Y., & Sannomiya, N. (2001). No-wait and blocking job-shops: challenging problems for GA’s. In Proceedings of the 2001 IEEE international conference on systems, man, and cybernetics (pp. 2349–2354).
Brucker, P. (2004). Scheduling algorithms (4th ed.). Berlin: Springer.
Brucker, P., & Kampmeyer, T. (2005). Tabu search algorithms for cyclic machine scheduling problems. Journal of Scheduling, 8, 303–322.
Brucker, P., & Kramer, A. (1996). Polynomial algorithms for resource-constrained and multiprocessor task scheduling problems. European Journal of Operational Research, 90, 214–226.
Cavory, G., Dupas, R., & Goncalves, G. (2005). A genetic approach to solving the problem of cyclic job shop scheduling with linear constraints. European Journal of Operational Research, 161, 73–85.
Chen, J., & Pan, J. C. (2006). Integer programming models for the re-entrant shop scheduling problem. Engineering Optimization, 38(5), 577–592.
Cheung, W., & Zhou, H. (2001). Using genetic algorithms and heuristics for job shop scheduling with sequence-dependent setup times. Annals of Operations Research, 107, 65–81.
Chung, D., Lee, K., Shin, K., & Park, J. (2005). A new approach to jobshop scheduling problems with due date constraints considering operation subcontracts. International Journal of Production Economics, 98, 238–250.
Engelbrecht, A. P. (2005). Fundamentals of computational swarm intelligence. New York: Wiley.
Ercan, M. F., & Fung, Y. (2007). Performance of particle swarm optimization in scheduling hybrid flow-shops with multiprocessor tasks. In Lecture notes in computer science (Vol. 4707, pp. 309–318). Berlin: Springer.
Essafi, I., Mati, Y., & Dauzère-Pérès, D. (2008). A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem. Computers and Operations Research, 35, 2599–2616.
Fredendall, L. D., Melnyk, S. A., & Ragatz, G. (1996). Information and scheduling in a dual resource constrained job shop. International Journal of Production Research, 34(10), 2783–2802.
Gao, J., Gen, M., & Sun, L. (2006). A hybrid of genetic algorithm and bottleneck shifting for flexible job shop scheduling problem. In Proceedings of the 8th annual conference on genetic and evolutionary computation (pp. 1157–1164).
Giffler, J., & Thompson, G. L. (1960). Algorithms for solving production scheduling problems. Operations Research, 8, 487–503.
Gonzalez, M. A., Vela, C. R., Sierra, M., Gonzales, I., & Varela, R. (2006). Comparing schedule generation schemes in memetic algorithms for the job shop scheduling problem with sequence dependent setup times. In Lecture notes in artificial intelligence (Vol. 4293, pp. 472–482). Berlin: Springer.
Grobler, J., & Engelbrecht, A. P. (2007). A scheduling-specific modeling approach for real world scheduling. In Proceedings of the 2007 IEEE international conference on industrial engineering and engineering management (pp. 85–89).
Grobler, J., Engelbrecht, A. P., Joubert, J. W., & Kok, S. (2007). A starting-time based-approach to production scheduling with particle swarm optimization. In Proceedings of the 2007 IEEE symposium on computational intelligence in scheduling (pp. 121–128).
Hoitomt, D. J., Luh, P. B., & Pattipati, K. R. (1993). A practical approach to job-shop scheduling problems. IEEE Transactions on Robotics and Automation, 9(1), 1–13.
Hwang, H., & Sun, J. U. (1997). Production sequencing problem with reentrant work flows and sequence-dependent set-up times. Computers and Industrial Engineering, 33(3), 773–776.
Jain, A. S., & Meeran, S. (1999). Deterministic job-shop scheduling: past, present and future. European Journal of Operational Research, 113, 390–434.
Jansen, K., Mastrolilli, M., & Solis-Oba, R. (2005). Approximation schemes for job shop scheduling problems with controllable processing times. European Journal of Operational Research, 167, 297–319.
Jia, Z., Chen, H., & Tang, J. (2007a). An improved particle swarm optimization for multi-objective flexible job-shop scheduling problem. In Proceedings of the 2007 IEEE international conference on grey systems and intelligent services (pp. 1587–1592).
Jia, Z., Chen, H., & Tang, J. (2007b). A new multi-objective fully-informed particle swarm algorithm for flexible job-shop scheduling problems. In Proceedings of the 2007 international conference on computational intelligence and security workshops (pp. 191–194).
Kacem, I., Hammadi, S., & Borne, P. (2002a). Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 32(1), 1–13.
Kacem, I., Hammadi, S., & Borne, P. (2002b). Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation, 60, 245–276.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942–1948).
Kennedy, J., & Mendes, R. (2002). Population structure and particle performance. In Proceedings of the IEEE congress on evolutionary computation (Vol. 2, pp. 1671–1676).
Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm intelligence. San Mateo: Morgan Kaufmann.
Le Pape, C., & Baptiste, P. (1999). Heuristic control of a constraint-based algorithm for the preemptive job-shop scheduling problem. Journal of Heuristics, 5, 305–325.
Lee, C. (2004). Machine scheduling with availability constraints. In J. Leung (Ed.), Handbook of scheduling: algorithms, models and performance analysis (Vol. 22). London: Chapman and Hall/CRC.
Lei, D. (2008). A Pareto archive particle swarm optimization for multi-objective job shop scheduling. Computers and Industrial Engineering, 54(4), 960–971.
Lei, D., & Xiong, H. (2007). An efficient evolutionary algorithm for multi-objective stochastic job shop scheduling. In Proceedings of the 6th international conference on machine learning cybernetics (pp. 867–872).
Lian, Z., Jiao, B., & Gu, X. (2006a). A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan. Applied Mathematics and Computation, 183, 1008–1017.
Lian, Z., Jiao, B., & Gu, X. (2006b). A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan. Applied Mathematics and Computation, 175, 773–785.
Liu, B., Wang, L., & Jin, Y. (2005). Hybrid particle swarm optimization for flow shop scheduling with stochastic processing time. In Lecture notes in artificial intelligence (pp. 630–637). Berlin: Springer.
Liu, H., Abraham, A., Choi, O., & Moon, S. H. (2006). Variable neighborhood particle swarm optimization for multi-objective flexible job-shop scheduling problems. In Lecture notes in computer science (pp. 197–204). Berlin: Springer.
Liu, B., Wang, L., & Jin, Y. (2007a). An effective hybrid particle swarm optimization for no-wait flow shop scheduling. International Journal of Advanced Manufacturing Technology, 31, 1001–1011.
Liu, H., Abraham, A., & Grosan, C. (2007b). A novel variable neighborhood particle swarm optimization for multi-objective flexible job-shop scheduling problems. In Proceedings of the 2nd international conference on digital information management (pp. 138–145).
Liu, B., Wang, L., & Jin, Y. (2008). An effective hybrid pso-based algorithm for flow shop scheduling with limited buffers. Computers and Operations Research, 35, 2791–2806.
Mascis, A., & Pacciarelli, D. (2002). Job-shop scheduling with blocking and no-wait constraints. European Journal of Operational Research, 143, 498–517.
Meloni, C., Pacciarelli, D., & Pranzo, M. (2004). A rollout metaheuristic for job shop scheduling problems. Annals of Operations Research, 131, 215–235.
Nakamura, M., Tome, H., Hachiman, K., Ombuki, B. M., & Onaga, K. (2000). Cyclic job-shop-scheduling based on evolutionary petri nets. In Proceedings of the 26th annual conference of the IEEE industrial electronics society (pp. 2855–2860).
Natarajan, K., Mohanasundaram, K. M., Shoban Babu, B., Suresh, S., Antony Arokia Durai Raj, K., & Rajendran, C. (2007). Performance evaluation of priority dispatching rules in multi-level assembly job shops with jobs having weights for flowtime and tardiness. International Journal of Advanced Manufacturing Technology, 31, 751–761.
Norman, B. A., & Bean, J. C. (1999). A genetic algorithm methodology for complex scheduling problems. Naval Research Logistics, 46(2), 199–211.
Nuijtne, W. P. M., & Aarts, E. H. L. (1996). A computational study of constraint satisfaction for multiple capacitated job shop scheduling. European Journal of Operational Research, 90, 269–284.
Pan, Q., Tasgetiren, M. F., & Liang, Y. (2006). Minimizing total earliness and tardiness penalties with a common due date on a single-machine using a discrete particle swarm optimization algorithm. In Lecture notes in computer science (pp. 460–467). Berlin: Springer.
Pan, Q., & Wang, L. (2008). No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm. International Journal of Advanced Manufacturing Technology, 39(7–8), 796–807.
Pan, Q., Wang, L., Tasgetiren, M. F., & Zhao, B. H. (2008). A hybrid discrete particle swarm optimization algorithm for the no-wait flow shop scheduling problem with makespan criterion. International Journal of Advanced Manufacturing Technology, 38(3–4), 337–347.
Petrovic, S., Carole, F., & Petrovic, D. (2005). Job shop scheduling with lot-sizing and batching in an uncertain real-world environment. In Proceedings of the 2nd multidisciplinary international conference on scheduling (pp. 363–379).
Potts, C. N., & Kovalyov, M. Y. (2000). Scheduling with batching: a review. European Journal of Operational Research, 120, 228–249.
Potts, C. N., Strusevich, V. A., & Tautenhahn, T. (1998). Scheduling batches with simultaneous job processing for two-machine shop problems (Technical report). University of Southampton.
Qi, J. G., Burns, G. R., & Harrison, D. K. (2000). The application of parallel multipopulation genetic algorithms to dynamic job-shop scheduling. The International Journal of Advanced Manufacturing Technology, 16(8), 609–615.
Schuster, C. J., & Framinan, J. M. (2003). Approximative procedures for no-wait job shop scheduling. Operations Research Letters, 31, 308–318.
Sha, D. Y., & Hsu, C. (2006). A hybrid particle swarm optimization for job shop scheduling problem. Computers and Industrial Engineering, 51, 791–808.
Singer, M. (2000). Forecasting policies for scheduling a stochastic due date job shop. International Journal of Production Research, 38(15), 3623–3637.
Singer, M., & Pinedo, M. (1998). A computational study of branch and bound techniques for minimizing the total weighted tardiness in job shops. IIE Transactions, 30, 109–118.
Storn, R., & Price, K. (1997). Differential evolution — a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.
Tasgetiren, M.F., Sevkli, M., Liang, Y. & Gencyilmaz (2007). A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. European Journal of Operational Research, 177, 1930–1947.
Van den Bergh, F., & Engelbrecht, A.P. (2002). A new locally convergent particle swarm optimiser. In Proceedings of the IEEE international conference on systems, man and cybernetics (Vol. 3, pp. 6–12).
Verhoeven, M. G. A. (1998). Tabu search for resource-constrained scheduling. European Journal of Operational Research, 106, 266–276.
Weng, J. H., Hiroki, O., & Hisashi, O. (2003). An integrated algorithm based on tabu search for flexible assembly job-shop scheduling. Journal of Japan Industrial Management Association, 5(4), 245–252.
Xia, W., & Wu, Z. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers and Industrial Engineering, 48, 409–425.
Xia, W., & Wu, Z. (2006). A hybrid particle swarm optimization approach for the job-shop scheduling problem. International Journal of Advanced Manufacturing Technology, 29, 360–366.
Xia, W., Wu, Z., & Yang, G. (2004). A new hybrid optimization algorithm for the job-shop scheduling problem. In Proceedings of the 2004 American control conference (pp. 5552–5557).
Yoshitomi, Y., & Yamaguchi, R. (2003). A genetic algorithm and the Monte Carlo method for stochastic job-shop scheduling. International Transactions in Operations Research, 10, 577–596.
Yu, H., & Liang, W. (2001). Neural network and genetic algorithm-based approach to expanded job-shop scheduling. Computers and Industrial Engineering, 39, 337–356.
Yun, S. Y. (2002). Genetic algorithm with fuzzy logic controller for preemptive and non-preemptive job-shop scheduling problems. Computers and Industrial Engineering, 43, 623–644.
Zandieh, M., Ghomi, S. M. T. F., & Husseini, S. M. M. (2006). An immune algorithm approach to hybrid flow shop scheduling with sequence-dependent setup times. Applied Mathematics and Computation, 180(1), 111–127.
Zhao, F., Hong, Y., & Yu, D. (2005). A hybrid approach based on artificial neural network and genetic algorithm for job-shop scheduling problem. In Proceedings of the 2005 international conference on neural networks and brain (pp. 1687–1692).
Zhou, Y., Li, B., & Yang, J. (2006). Study on job shop scheduling with sequence-dependent setup times using biological immune algorithm. International Journal of Advanced Manufacturing Technology, 30, 105–111.
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Grobler, J., Engelbrecht, A.P., Kok, S. et al. Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time. Ann Oper Res 180, 165–196 (2010). https://doi.org/10.1007/s10479-008-0501-4
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DOI: https://doi.org/10.1007/s10479-008-0501-4