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Linear inflation rules for the random yield production control problem with uncertain demand: analysis and computations

Published: 07 December 2008 Publication History

Abstract

Since the dawn of wafer fabrication and the production of microelectronic parts a fundamental characteristic of this environment has been uncertainty in production yields and in demand for product. The impact of the uncertainty is so prevalent that even deterministic models in practice have incorporated some allowance for uncertainty through features such as date effective yields, moving average capacity, etc. In this paper, we propose a simple heuristic approach for the inventory control problem with stochastic demand and multiplicative random yield. Our heuristic tries to find the best candidate within a class of policies which are referred to in the literature as the linear inflation rule (LIR) policies. Our approach is computationally fast, easy to implement and intuitive to understand. Moreover, we find that in a significant number of instances our heuristic performs better than several other well-known heuristics that are available in the literature.

References

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Bollapragada, S., and T. E. Morton. 1999. Myopic heuristics for the random yield problem. Operations Research 47 (5): 713--722.
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Ehrhardt, R., and L. Taube. 1987. An inventory model with random replenishment quantities. International Journal of Production Research 25:1975--1803.
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Glasserman, P., and S. Tayur. 1995. Sensitivity analysis for base-stock levels in multiechelon production-inventory systems. Management Science 41 (2): 263--281.
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Henig, M., and Y. Gerchak. 1990. The structure of periodic review policies in the presence of random yield. Operations Research 38 (4): 634--643.
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Henig, M., and N. Levin. 1992. Joint production planning and product delivery commitment with random yield. Operations Research 40 (2): 404--408.
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Inderfurth, K., and S. Transchel. 2007. Note on "myopic heuristics for the random yield problem". Operations Research 55 (6): 1183--1186.
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Janakiraman, G., and R. O. Roundy. 2004. Lost-sales problems with stochastic lead times: Convexity results for base-stock policies. Operations Research 52 (5): 795--803.
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Li, Q., H. Xu, and S. Zheng. 2006. Periodic review inventory systems with random yield: Bounds and heuristics. Working Paper, HKUST.
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Yano, C. A., and H. L. Lee. 1995. Lot sizing with random yields: A review. Operations Research 43 (2): 311--334.
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Zipkin, P. H. 2000. Foundations of inventory management. The McGraw-Hill Companies, Inc.
  1. Linear inflation rules for the random yield production control problem with uncertain demand: analysis and computations

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      cover image ACM Conferences
      WSC '08: Proceedings of the 40th Conference on Winter Simulation
      December 2008
      3189 pages
      ISBN:9781424427086

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      • IIE: Institute of Industrial Engineers
      • INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
      • ASA: American Statistical Association
      • IEEE/SMC: Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society
      • SIGSIM: ACM Special Interest Group on Simulation and Modeling
      • NIST: National Institute of Standards and Technology
      • (SCS): The Society for Modeling and Simulation International

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      Winter Simulation Conference

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      Published: 07 December 2008

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      WSC08
      Sponsor:
      • IIE
      • INFORMS-SIM
      • ASA
      • IEEE/SMC
      • SIGSIM
      • NIST
      • (SCS)
      WSC08: Winter Simulation Conference
      December 7 - 10, 2008
      Florida, Miami

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      WSC '08 Paper Acceptance Rate 249 of 304 submissions, 82%;
      Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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