Performance Analysis and Optimization of Assemble-to-Order Systems with Random Lead Times

Jing-Sheng Song () and David D. Yao ()
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Jing-Sheng Song: Graduate School of Management, University of California, Irvine, California 92697
David D. Yao: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027

Operations Research, 2002, vol. 50, issue 5, 889-903

Abstract: We study a single-product assembly system in which the final product is assembled to order whereas the components (subassemblies) are built to stock. Customer demand follows a Poisson process, and replenishment lead times for each component are independent and identically distributed random variables. For any given base-stock policy, the exact performance analysis reduces to the evaluation of a set of M / G /(infinity) queues with a common arrival stream. We show that unlike the standard M / G /(infinity) queueing system, lead time (service time) variability degrades performance in this assembly system. We also show that it is desirable to keep higher base-stock levels for components with longer mean lead times (and lower unit costs). We derive easy-to-compute performance bounds and use them as surrogates for the performance measures in several optimization problems that seek the best trade-off between inventory and customer service. Greedy-type algorithms are developed to solve the surrogate problems. Numerical examples indicate that these algorithms provide efficient solutions and valuable insights to the optimal inventory/service trade-off in the original problems.

Keywords: Inventory/production: multi-item; operating characteristics; stochastic models (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (52)

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