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

• Published in: Management science Vol. 42; no. 1; pp. 124 - 129
• Main Authors: Anupindi, Ravi, Morton, Thomas E, Pentico, David
• Format: Journal Article
• Language: English
• Published: Linthicum, MD INFORMS 01.01.1996; Institute for Operational Research and the Management Sciences; Institute for Operations Research and the Management Sciences
• Series: Management Science
• Subjects: Applied sciences; Carrying costs; Exact sciences and technology; Fixed costs; Graduate schools; Heuristic; Heuristics; Inventories; Inventory control; Inventory control, production control. Distribution; leadtimes; Management; nonstationary; Operational research and scientific management; Operational research. Management science; Statistical variance; stochastic inventory; Stochastic models; Stochastic processes; Studies; University administration; Lower bound; Stochastic model; Inventory control; Upper bound; Heuristic method
• ISSN: 0025-1909; 1526-5501
• DOI: 10.1287/mnsc.42.1.124

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.