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From Manufacturing Scheduling to Supply Chain Coordination
Author: Update times: 2013-06-07                          | Print | Close | Text Size: A A A

Prof. Peter B. Luh; School of Electrical & Computer Engineering, University of Connecticut

From Manufacturing Scheduling to Supply Chain Coordination

09:00-11:00 AM, June 9th, 2013

Venue: Meeting room, 4th Floor, Building A, Shenyang Institute of Automation, CAS

From Manufacturing Scheduling to Supply Chain Coordination. 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 author, decomposition and coordination by using Lagrangian relaxation is identified 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 problem sare also highlighted. To extend manufacturing scheduling within a factory to coordinate autonomic members across chains of suppliers, a decentralized supply chain model is established. By relaxing cross-member constraints, the model is decomposed into member-wise sub problems, 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.

Litho Machine Scheduling with Convex Hull Analysis. The increasing requirements for meeting demand are forcing semiconductor manufacturers to seek efficient scheduling methods. Among processing stages, lithography is usually a major bottleneck stage with a limited number of expensive equipment. In the second half of the talk, a litho machine scheduling problem with machine setups, reticle expirations and load balancing is presented. After linearization and simplification, the problem is solved by using the branch-and-cut method. Near-optimal solutions are, however, still difficult to obtain. Through detailed mathematical analysis, it is found that the convex hull of the problem is difficult to obtain and many low-efficient branching operations are performed. A two-phase approach is therefore developed. In the first phase, a simplified problem without the complicating constraints is quickly solved to establish the ranges of decision variables. Based on these ranges, the problem with full constraints is solved in the second phase. Numerical testing shows that the two-phase approach can generate high quality schedules within reasonable amount of time.


Peter B. Luh received his B.S. from National Taiwan University, M.S. from M.I.T., and Ph.D. from Harvard University. He has been with the University of Connecticut since 1980, and currently is the SNET Professor of Communications & Information Technologies. He was the Head of the Department of Electrical and Computer Engineering from 2006 to 2009. He is also a member of the Chair Professors Group, Center for Intelligent and Networked Systems (CFINS) in the Department of Automation, Tsinghua University, Beijing, China. Professor Luh is a Fellow of IEEE. He was the VP of Publications of RAS (2008-2011), the founding Editor-in-Chief of the IEEE Transactions on Automation Science and Engineering (2003-2007), and the Editor-in-Chief of IEEE Transactions on Robotics and Automation (1999-2003). He received IEEE Robotics and Automation Society 2013 Pioneer Award for his pioneering contributions to the development of near-optimal and efficient planning, scheduling, and coordination methodologies for manufacturing and power systems. His research interests include Smart Power Systems – smart grid, design of auction methods for electricity markets, robust renewable (wind and solar) integration to the grid, and electricity load and price forecasting with demand response; Intelligent Manufacturing Systems – planning, scheduling, and coordination of design, manufacturing, and service activities; Smart and Green Buildings and Eco Communities – optimized energy management, HVAC fault detection and diagnosis, emergency crowd guidance, and eco communities. 

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