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We consider a short‐term capacity allocation problem with tool and setup constraints that arises in the context of operational planning in a semiconductor wafer fabrication facility. The problem is that of allocating the available capacity of parallel nonidentical machines to available work‐in‐process (WIP) inventory of operations. Each machine can process a subset of the operations and a tool setup is required on a machine to change processing from one operation to another. Both the number of tools available for an operation and the number of setups that can be performed on a machine during a specified time horizon are limited. We formulate this problem as a degree‐constrained network flow problem on a bipartite graph, show that the problem is NP‐hard, and propose constant factor approximation algorithms. We also develop constructive heuristics and a greedy randomized adaptive search procedure for the problem. Our computational experiments demonstrate that our solution procedures solve the problem efficiently, rendering the use of our algorithms in real environment feasible. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005 相似文献
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Erdem Eskigun Reha Uzsoy Paul V. Preckel George Beaujon Subramanian Krishnan Jeffrey D. Tew 《海军后勤学研究》2007,54(3):282-300
We present a large‐scale network design model for the outbound supply chain of an automotive company that considers transportation mode selection (road vs. rail) and explicitly models the relationship between lead times and the volume of flow through the nodes of the network. We formulate the problem as a nonlinear zero‐one integer program, reformulate it to obtain a linear integer model, and develop a Lagrangian heuristic for its solution that gives near‐optimal results in reasonable time. We also present scenario analyses that examine the behavior of the supply chain under different parameter settings and the performance of the solution procedures under different experimental conditions. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007 相似文献
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A fundamental difficulty in developing effective production planning models has been accurately reflecting the nonlinear dependency between workload and lead times. We develop a mathematical programming model for production planning in multiproduct, single stage systems that captures the nonlinear dependency between workload and lead times. We then use outer linearization of this nonlinear model to obtain a linear programming formulation and extend it to multistage systems. Extensive computational experiments validate the approach and compare its results to conventional models that assume workload‐independent planning lead times. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009 相似文献
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