THE NONSTATIONARY STOCHASTIC LEAD-TIME INVENTORY PROBLEM - NEAR-MYOPIC BOUNDS, HEURISTICS, AND TESTING

The purpose of the current paper is to combine the classical results o f Kaplan (1970) and Ehrhardt (1984) for stochastic leadtime problems w ith recent work of Morton and Pentico (1991), which assumed zero lag, to obtain near-myopic bounds and heuristics for the nonstationary stoc hastic leadtime problem with arbitrary sequences of demand distributio ns, and to obtain planning horizon results. Four heuristics have been tested on a number of different demand scenarios over a number of rand om trials for four different leadtime distributions. The myopic (simpl est) heuristic performs well only for moderately varying problems with out heavy end of season salvaging, giving errors for this type of prob lem that are less than 1.5%. However, the average error for the myopic heuristic over all scenarios tested is 20.0%. The most accurate heuri stic is the near-myopic heuristic which averages 0.5% from optimal acr oss all leadtime distributions with a maximum error of 4.7%. The avera ge error increases with increase in variance of the leadtime distribut ion.