Product Cycling With Uncertain Yields: Analysis and Application to the Process Industry

Kumar Rajaram () and Uday S. Karmarkar
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Kumar Rajaram: Decision, Operations, and Technology Management, The Anderson School, University of California at Los Angeles, 110 Westwood Plaza, Los Angeles, California 90095--1481
Uday S. Karmarkar: Decision, Operations, and Technology Management, The Anderson School, University of California at Los Angeles, 110 Westwood Plaza, Los Angeles, California 90095--1481

Operations Research, 2002, vol. 50, issue 4, 680-691

Abstract: We formulate the dynamic product-cycling problem with yield uncertainty and buffer limits to determine how much product to produce at what time to minimize total expected switching, production, inventory storage, and backorder costs. A “restricted” Lagrangian technique is used to develop a lower bound and a model-based Lagrangian heuristic. We also develop an operational heuristic and a greedy heuristic. The operational heuristic has been implemented at seven refineries at Cerestar, Europe's leading manufacturer of wheat-and corn-based starch products in the food-processing industry . This has already reduced total costs by around 5 percent or $3 million annually at these sites. Tests of the Lagrangian heuristic on data from these refineries during this period have shown the potential to further reduce total costs by at least 2 percent or about $1 million. In addition, the Lagrangian heuristic has provided an objective basis to evaluate the economic impact of several strategic decisions involving issues such as buffer expansion, variability reduction, and product selection.

Keywords: Production planning: product cycling with yield uncertainty. Production application: process industry; food-processing sector . Production heuristics: Lagrangian heuristic; operational heuristic (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (14)

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