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From manufacturing scheduling to supply chain coordination: The control of complexity and uncertainty

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Abstract

With time-based competition and rapid technology advancements, effective manufacturing scheduling and supply chain coordination are critical to quickly respond to changing market conditions. These problems, however, are difficult in view of inherent complexity and various uncertainties involved. Based on a series of results by the authors, decomposition and coordination by using Lagrangian relaxation is identified in this paper as an effective way to control complexity and uncertainty. A manufacturing scheduling problem is first formulated within the job shop context with uncertain order arrivals, processing times, due dates, and part priorities as a separable optimization problem. A solution methodology that combines Lagrangian relaxation, stochastic dynamic programming, and heuristics is developed. Method improvements to effectively solve large problems are also highlighted. To extend manufacturing scheduling within a factory to coordinate autonomic members across chains of suppliers, a decentralized supply chain model is established in the second half of this paper. By relaxing cross-member constraints, the model is decomposed into member-wise subproblems, and a nested optimization structure is developed based on the job shop scheduling results. Coordination is performed through the iterative updating of cross-member prices without accessing other members’ private information or intruding their decision-making authorities, either with or without a coordinator. Two examples are presented to demonstrate the effectiveness of the method. Future prospects to overcome problem inseparability and improve computing efficiency are then discussed.

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Authors and Affiliations

  1. Department of Electrical & Computer Engineering, University of Connecticut, Storrs, Connecticut, 06269-2157, USA

    Peter B. Luh & Weidong Feng

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  1. Peter B. Luh
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Additional information

Peter B. Luh received his B.S. degree in Electrical Engineering from National Taiwan University, Taipei, China in 1973, M.S. degree in Aeronautics and Astronautics Engineering from M.I.T., Cambridge, MA in 1977, and Ph.D. degree from Harvard University in 1980. Since 1980 he has been with the University of Connecticut, and currently is the SNET Professor of Communications & Information Technologies in the Department of Electrical and Computer Engineering, the Director of Taylor L. Booth Engineering Center for Advanced Technology at the University of Connecticut, and a Visiting Professor of Tsinghua University, Beijing, China. He is interested in planning, scheduling, and coordination of design, manufacturing, and supply chain activities; and schedule, bid, and portfolio optimization and load/price forecasting for power systems. He is a Fellow of IEEE, Editor-in-Chief of IEEE Transactions on Robotics and Automation, the founding Editor-in-Chief of the newly created IEEE Transactions on Automation Sciences and Engineering, an Associate Editor of IIE Transactions on Design and Manufacturing, and an Associate Editor of Discrete Event Dynamic Systems.

Weidong Feng received his B.S. degree in Industrial Electrical Engineering from the Light Industrial College of Zhengzhou, Zhengzhou, China in 1990, and M.S. degree in Systems Engineering in 1996 and Ph.D. degree in 1999 from Tianjin University, Tianjin, China. He had two years postdoctoral research experience in the Economics and Management School at Tsinghua University, Beijing, China, and also industrial experience in operations management and integrated product development. Since 2001 he has been a postdoctoral research fellow in the Department of Electrical and Computer Engineering, also a research fellow of Taylor L. Booth Engineering Center for Advanced Technology at the University of Connecticut. His current research interests focus on manufacturing/logistic planning and scheduling, supply chain coordination, service operations management, optimization technologies and their applications.

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Luh, P.B., Feng, W. From manufacturing scheduling to supply chain coordination: The control of complexity and uncertainty. J. Syst. Sci. Syst. Eng. 12, 279–297 (2003). https://doi.org/10.1007/s11518-006-0135-5

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