Myopic heuristics for the random yield problem

We consider a single item periodic review inventory problem with random yie ld and stochastic demand. The yield is proportional to the quantity ordered , with the multiplicative factor being a random variable. The demands are s tochastic and are independent across the periods, but they need not be stat ionary. The holding, penalty, and ordering costs are linear. Any unsatisfie d demands are backlogged. Two cases for the ordering cost are considered: T he ordering cost can be proportional to either the quantity ordered (e.g., in house production) or the quantity received (e.g., delivery by an externa l supplier). Random yield problems have been addressed previously in the li terature, but no constructive solutions or algorithms are presented except for simple heuristics that are far from optimal. In this paper, we present a novel analysis of the problem in terms of the inventory position at the e nd of a period. This analysis provides interesting insights into the proble m and leads to easily implementable and highly accurate myopic heuristics. A detailed computational study is done to evaluate the heuristics. The stud y is done for the infinite horizon case, with stationary yields and demands and for the finite horizon case with a 26-period seasonal demand pattern. The best of our heuristics has worst-case errors of 3.0% and 5.0% and avera ge errors of 0.6% and 1.2% for the infinite and finite horizon cases, respe ctively.