Closed-loop supply chain network design under demand, return and quality uncertainty

Abstract We consider the problem of designing a closed-loop supply chain (CLSC) network in presence uncertainty demand quantities, return rates, and quality returned products. formulate as two-stage stochastic mixed-integer program (SMIP) that maximizes total expected profit. The first-stage decisions our model are facility location capacity decisions, second-stage forward/reverse flows on hence production/recovery quantities defined by flow amounts. solve using L-shaped method iterative branch-and-cut frameworks. To improve computational efficiency, we various cut generation strategies. Besides testing performance considered solution methods, also use numerical results to estimate value (VSS), perfect information (EVPI), benefit utilizing CLSC network. Our indicate has highest impact rate lowest VSS EVPI values, including reverse increases profit significantly.