A Simple Heuristic for Computing Nonstationary (s, S) Policies

Srinivas Bollapragada and Thomas E. Morton
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Srinivas Bollapragada: GE Corporate Research and Development Center, 1 Research Circle, Schenectady, New York 12309
Thomas E. Morton: Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Operations Research, 1999, vol. 47, issue 4, 576-584

Abstract: Nonstationary inventory problems with set-up costs, proportional ordering costs, and stochastic demands occur in a large number of industrial, distribution, and service contexts. It is well known that nonstationary ( s , S ) policies are optimal for such problems. In this paper, we propose a simple, myopic heuristic for computing the policies. The heuristic involves approximating the future problem at each period by a stationary one and obtaining the solution to the corresponding stationary problem. We numerically compare our heuristic with an earlier myopic heuristic and the optimal dynamic programming solution procedure. Over all problems tested, the new heuristic averaged 1.7% error, compared with 2.0% error for the old procedure, and is on average 399 times as fast as the D.P. and 2062 as fast as the old heuristic. Moreover, our heuristic, owing to its myopic nature, requires the demand data only a few periods into the future, while the dynamic programming solution needs the demand data for the entire time horizon—which are typically not available in most practical situations.

Keywords: inventory/production; heuristics; myopic heuristics; inventory; stochastic; random demands; inventory; review/lead times; periodic review and ordering set-up costs (search for similar items in EconPapers)
Date: 1999
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Citations: View citations in EconPapers (20)

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