Remanufacturing planning based on constrained ordinal optimization

Abstract

Resource planning for a remanufacturing system is in general extremely difficult in terms of problem size, uncertainties, complicated constraints, etc. In this paper, we present a new method based on constrained ordinal optimization (COO) for remanufacturing planning. The key idea of our method is to estimate the feasibility of plans by machine learning and to select a subset with the estimated feasibility based on the procedure of horse racing with feasibility model (HRFM). Numerical testing shows that our method is efficient and effective for selecting good plans with high probability. It is thus a scalable optimization method for large scale remanufacturing planning problems with complicated stochastic constraints.

Keywords

remanufacturing systems / constrained ordinal optimization (COO) / simulation-based optimization / machine learning

References

[1] Haynsworth H C, Lyons R T. Remanufacturing by design, the missing link. Production and Inventory Management Journal, 1987, 28(2): 24-29

[2] Perry J H. The impact of lot size and production scheduling on inventory investment in a remanufacturing environment. Production and Inventory Management Journal, 1991, 32(3): 41-45

[3] Fourcaud R. Is repair/remanufacturing really different? In: Proceedings of APICS Remanufacturing Seminar. 1993, 4-9

[4] Song C, Guan X H, Zhao Q C, Ho Y C. Machine learning approach for determining feasible plans of a remanufacturing system. IEEE Transactions on Automation Science and Engineering, 2005, 2(3): 262-275

[5] Birge J R, Louveaux F. Introduction to Stochastic Programming. Chapter 7. New York: Springer-Verlag, 1997

[6] Olhager J, Rapp B. Operations research techniques in manufacturing planning and control systems. Operational Research, 1995, 2(1): 29-43

[7] Kusiak A. Rough set theory: A data mining tool for semiconductor manufacturing. Electronics Packaging Manufacturing, 2001, 24(1): 44-50

[8] Maturana F, Gu P, Naumann A, Norrie D H. Object-oriented job-shop scheduling using genetic algorithms. Computer in Industry, 1997, 32(3): 281-294

[9] Cohen G. Neural networks implementations to control realtime manufacturing systems. Computer Integrated Manufacturing Systems, 1998, 11(4): 243-251

[10] Tang Y, Zhou M C, Caudill R. An Integrated approach to disassembly planning and demanufacturing operation. IEEE Transactions on Robotics and Automation, 2001, 17(6): 773-784

[11] Lambert A J D. Determining optimum disassembly sequences in electronic equipment. Computers and Industrial Engineering, 2002, 43(3): 553-575

[12] Kiesmuller G P. Optimal control of a one product recovery system with leadtimes. International Journal of Production Economics, 2003, 81-82(11): 333-340

[13] Gosavi A. Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning. Berlin: Springer, 2003

[14] Ho Y C, Zhao Q C, Jia Q S. Ordinal Optimization: Soft Optimization for Hard Problems. New York: Springer, 2007

[15] Ho Y C, Sreenivas R S, Vakili P. Ordinal optimization of discrete event dynamic systems. Journal of DEDS, 1992, 2(2): 61-88

[16] Dai L. Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems. Journal of Optimization Theory and Applications, 1996, 91(2): 363-388

[17] Xie X L. Dynamics and convergence rate of ordinal comparison of stochastic discrete event systems. IEEE Transactions on Automatic Control, 1997, 42(4): 586-590

[18] Ho Y C, Lee L H, Lau E T K. Explanation of goal softening in ordinal optimization. IEEE Transactions on Automatic Control, 1999, 44(1): 94-99

[19] Cassandras C G. Discrete Event Systems: Modeling and Performance Analysis. Boston: Aksen Associates Inc, 1993

[20] Spall J C. Introduction to Stochastic Search and Optimization: Estimation, Simulation and Control. 1st ed. New York: John Wiley and Sons, 2003

[21] Fu M. Optimization for simulation: Theory vs. practice (feature article). INFORMS Journal on Computing, 2002, 14(3): 192-215

[22] Li D, Lee L H, Ho Y C. Constrainted ordinal optimization. Information Sciences, 2002, 148(1-4): 201-220

[23] Zhao Q C, Ho Y C, Jia Q S. Vector ordinal optimization. Journal of Optimization Theory and Applications, 2005, 125(2): 259-274

[24] Lee L H, Li W G, Ho Y C. Vector ordinal optimization — A new heuristic approach and its application to computer network routing design problems. International Journal of Operations and Quantitative Management, 1999, 5(3): 211-230

[25] Guan X H, Song C, Ho Y C, Zhao Q C. Constrained ordinal optimization — A feasibility model based approach. Discrete Dynamic Event Systems: Theory and Applications, 2006, 16(2): 279-299

[26] Lau T W E, Ho Y C. Universal alignment probabilities and subset selection for ordinal optimization. Journal of Optimization and Theory, 1997, 39(3): 455-490

[27] Pawlak Z. Rough Sets — Theoretical Aspects of Reasoning About Data. Chapter 1. Boston: Kluwer Academic Publishers, 1991

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