The Nonstationary Stochastic Lead-Time Inventory Problem: Near-Myopic Bounds, Heuristics, and Testing

Ravi Anupindi, Thomas E. Morton and David Pentico
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Ravi Anupindi: J. L. Kellogg Graduate School of Management, Northwestern University, Evanston, Illinois 60208
Thomas E. Morton: Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
David Pentico: School of Business, Duquesne University, Pittsburgh, Pennsylvania 15282

Management Science, 1996, vol. 42, issue 1, 124-129

Abstract: The purpose of the current paper is to combine the classical results of Kaplan (Kaplan, R. 1970. Dynamic inventory model with stochastic lead times. Management Sci. 16(2) 491--507.) and Ehrhardt (Ehrhardt, R. 1984. (s, S) Policies for a dynamic inventory model with stochastic lead times. Oper. Res. 32(1) 121--132.) for stochastic leadtime problems with recent work of Morton and Pentico (Morton, T., D. Pentico. 1995. The finite horizon nonstationary stochastic inventory problem near-myopic bounds, heuristics, testing. Management Sci. 41(2) 334--343.), which assumed zero lag, to obtain near-myopic bounds and heuristics for the nonstationary stochastic leadtime problem with arbitrary sequences of demand distributions, and to obtain planning horizon results. Four heuristics have been tested on a number of different demand scenarios over a number of random trials for four different leadtime distributions. The myopic (simplest) heuristic performs well only for moderately varying problems without heavy end of season salvaging, giving errors for this type of problem that are less than 1.5%. However, the average error for the myopic heuristic over all scenarios tested is 20.0%. The most accurate heuristic is the near-myopic heuristic which averages 0.5% form optimal across all leadtime distributions with a maximum error of 4.7%. The average error with increases in variance of the leadtime distribution.

Keywords: stochastic inventory; heuristics; leadtimes; nonstationary (search for similar items in EconPapers)
Date: 1996
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