Dual-mode inventory management under a chance credit constraint

We study a dual-mode inventory management problem of a high-value component where the customer demand and the regular transportation lead time are stochastic, and the review periods of the two modes are different. The manufacturer is subject to a chance credit constraint that bounds the working capital. To solve the resulting chance-constrained stochastic optimization problem, we develop a hybrid simulation optimization algorithm that combines the modified nested partitions method as the global search framework, a feasibility detection procedure for chance constraint verification, and a $$\hbox {KN}{++}$$ KN + + procedure as the final “cleanup” procedure to ensure solution quality. We are then able to analyze the impact of the chance credit constraint on the inventory policies and operational cost. Our numerical study shows that the effects of the reduction in mean or variance of the regular transportation lead time depend on whether the chance credit constraint is loose or tight. We show in this way that this tightness may lead to different mechanisms dominating the observed behavior. Further, we show that substantially extending the deterministic credit limit is less effective than having a slight increase in the probability parameter of the chance credit constraint.