Production Lot-Sizing Under Learning Effects: An Efficient Solution Technique

Abstract

In this paper, we consider a production-inventory model which assumes that learning occurs as a function of the number of units produced. We analyze two cases: the first case allows for no forgetting between production runs and the second case (a generalization of the first case) allows for some given degree of forgetting between production runs. In the first case, we show that learning only has an impact on initial lot-sizes for large order quantities and that steady state lot-sizes will approach the traditional EOQ amount. In addition, we show that succeeding lot-sizes are always nonincreasing. Applying these results to the second case when forgetting occurs, we develop efficient heuristic algorithms with complexity O(N logN) to determine order quantities. Results from our algorithms are compared to optimal solutions; these comparisons indicate that our algorithms usually provide solutions within one percent of the optimal cost.