Integrated Process Planning and Active Scheduling in a Supply Chain-A Learnable Architecture Approach

Document Type : Research Paper

Authors

1 School of Industrial Engineering and Management, Oklahoma State University, Stillwater, USA

2 Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran

Abstract

Through the lens of supply chain management, integrating process planning decisions and scheduling plans becomes an issue of great challenge and importance. Dealing with the problem paves the way to devising operation schedules with minimum makespan; considering the flexible process sequences, it can be viewed as a fundamental tool for achieving the scheme, too. To deal with this integration, the modeling approach to problem with MIP structure is common in the literature. These models take precedence constraints into consideration to select machines and to determine sequences. In order to obtain viable sequences, we employed a proposed transformation matrix (TM). We also took advantage of an evolutionary search, called Learnable genetic Architecture (LEGA). Based on LEGA, we developed an integrated process planning and scheduling learnable genetic algorithm (IPPSLEGA). Our approach was evaluated with problems with various sizes. The experimental results show that our proposed architecture outperforms prior approaches, or it performs, at least, as efficiently as they do.

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References

Ausaf, M. F., Gao, L., & Li, X. (2015). Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm. Frontiers of Mechanical Engineering, 10(4), 392–404.
Beamon, B. M. (1998). Supply chain design and analysis: Models and methods. International Journal of Production Economics 55, 281–294.
Bensmaine, A., Dahane, M., & Benyoucef, L. (2014). A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems. International Journal of Production Research, 52(12), 3583-3594.
Bierwirth, C. (1995). A generalized permutation approach to job shop scheduling with genetic algorithms. OR Spektrum, 17,87-92.
Brandimarte, P., (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research, 22, 158-183.
Brandimarte, P., & Calderini, M. (1995). A heuristic bi-criterion approach to integrated process plan selection and job shop scheduling. International Journal of Production Research 33, 161–181.
Chen, H., Ihlow, J., & Lehmann, C. (1999). A genetic algorithm for flexible job-shop scheduling. Proceedings of the IEEE International Conference on Robotics and Automation 2, 1120-1125.
Cheng, R., Gen, M., & Tsujimura, Y. (1996). A tutorial survey of job shop scheduling problems using genetic algorithms. I. representation. Computers and Industrial Engineering, 30(4), 983-997.
Cochran, J. K., Horng, S., & Fowler, J. W. (2003). A multi-population GA to solve multi-objective scheduling problems for parallel resources. Computers and Operations Research 30, 1087–1102.
Guinet, A. (2001). Multi-site planning: A transshipment problem. International Journal of Production Economics, 74,21–32.
Hankins, S. L., Wysk, R. A., & Fox, K. R. (1984). Using a CATS database for alternative machine loading. Journal of Manufacturing Systems, 3, 115–120.
Ho, N. B., Tay, J. C., & Lai, E. M. (2007). An effective architecture for learning and evolving flexible job-shop schedules. European Journal of Operational Research, 179, 316–333.
Kacem, I., Hammadi, S., & Borne, P. (2002a). Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man and Cybernetics, 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.
Mesghouni, k., Hammadi, S., & Borne, P. (1997). Evolution programs for job-shop scheduling. Proceedings of the IEEE International Conference on Computational Cybernetics and Simulation, 1, 720–725.
Leung, C. W., Wong, T. N., Mak, K. L., & Fung, R. Y. K. (2010). Integrated process planning and scheduling by an agent-based ant colony optimization. Computers and Industrial Engineering, 59(1), 166-180.
Li, X., Gao, L., & Shao, X. (2012). An active learning genetic algorithm for integrated process planning and scheduling, Expert Systems with Applications, 39(8), 6683-6691.
Lian, K., Zhang, C., Gao, L., & Li, X. (2012). Integrated process planning and scheduling using an imperialist competitive algorithm. International Journal of Production Research, 50(15), 4326-4343.
Luo, G., Wen, X., Li, H., Ming, W., & Xie, G. (2017). An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling. The International Journal of Advanced Manufacturing Technology, 91(9–12), 3145–3158.
Mohapatra, P., Benyoucef. L., & Tiwari, M.K. (2013). Integration of process planning and scheduling through adaptive setup planning: A multi-objective approach. International Journal of Production Research, 51, 23-24.
Moon, C., Kim, J., & Hur, S. (2002). Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain. Computers and Industrial Engineering, 43,331–349.
Moon, C., Lee, Y. H., Jeong, C. S., & Yun, Y. (2008). Integrated process planning and scheduling in a supply chain. Computers and Industrial Engineering, 54, 1048-1061.
Nasr, N., Elsayed, A. (1990). Job shop scheduling with alternative machines. International Journal of Production Research, 28, 1595–1609.
Palmer, G. J. (1996). A simulated annealing approach to integrated production scheduling. Journal of Intelligent Manufacturing, 7, 163–176.
Petrović, M., Vuković, N., Mitić, M., & Miljković, Z. (2016). Integration of process planning and scheduling using chaotic particle swarm optimization algorithm. Expert Systems with Applications, 64, 569-588.
Shah, N. K., & Ierapetritou, M. G. (2012). Integrated production planning and scheduling optimization of multisite, multiproduct process industry. Computers and Chemical Engineering, 37, 214-226.
Shao, X., Li, X., Gao, L., & Zhang, C. (2009). Integration of process planning and scheduling-A modified genetic algorithm-based approach. Computers and Operations Research, 36, 2082-2096.
Tan, W., & Khoshnevis, B. (2004). A linearized polynomial mixed integer programming model for the integration of process planning and scheduling. Journal of Intelligent Manufacturing, 15, 593–605.
Varela, R., Vela, C. R., Puente, J., & Goméz, A. (2003). A knowledge based evolutionary strategy for scheduling problems with bottlenecks. European Journal of Operational Research, 145(1), 57-71.
Wang, J., Fan, X., Zhang, C., & Wan, S. (2014). A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling. Chinese Journal of Chemical Engineering, 22(7), 748-753.
Wong, T. N., Leung, C. W., Mak, K. L., & Fung, R. Y. K. (2006). An agent-based negotiation approach to integrate process planning and scheduling. International Journal of Production Research, 44(7), 1331-1351.
Xia, H., Li, X., & Gao, L. (2016). A hybrid genetic algorithm with variable neighborhood search for dynamic integrated process planning and scheduling, Computers and Industrial Engineering, 102, 99-112.
Zhang, L., & Wong, T. N. (2015). An object-coding genetic algorithm for integrated process planning and scheduling. European Journal of Operational Research, 244(2), 434-444.
Zhang, L., & Wong, T. N. (2016). Solving integrated process planning and scheduling problem with constructive meta-heuristics. Information Sciences 340–341, 1-16.
Zhang, Y. F., Saravanan, A. N., & Fuh, J. Y. H. (2003). Integration of process planning and scheduling by exploring the flexibility of process planning. International Journal of Production Research, 41(3), 611-628.
Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies)
Volume 12, Issue 2
April 2019
Pages 307-333

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