A new stochastic simulation optimization methodology for supply chain inventory optimization with imperfect quality items

This paper proposes a new stochastic simulation optimization methodology that integrates meta-heuristic with sample average approximation for supply chain inventory optimization under supply uncertainty. The supply uncertainty specifically considered is quality imperfection/deviation modelled as a discrete or continuous distribution function. In order to approximate the expected total inventory-related cost, sufficient samples of quality deviations are generated and then the corresponding sample average function is optimized by a newly developed hybrid meta-heuristic algorithm. The proposed methodology and its individual components are presented. Numerical results of a single-distributor-multiple-retailer supply chain system with each adopting the (s, S) replenishment policy indicate that the proposed methodology is capable of obtaining high quality supply chain inventory policies with percentage optimality gap within 0.01%.