A decision making framework for joint replenishment and delivery scheduling problems under mixed uncertainty

Concerning the essence of risk, a joint replenishment and delivery scheduling problem with fuzzy cost-related parameters and random number of imperfect quality items is developed to make it suitable for the inherent uncertainties of procurement-shipment process. The mathematical modelling-based decision system is formulated as a chance-constrained programming with the idea of embedding decision makers’ risk tolerance. Following this notion, the model is translated into an equivalent non-linear counterpart and a neighbourhood heuristic search is designed based on the properties of the cost function. We introduce an integrated cross-entropy algorithm, incorporating the heuristic in the cross-entropy framework, to solve it. The numerical results demonstrate that ICE is quite effective in comparison to state-of-the-art algorithms. Our framework is helpful for decision makers to determine economically acceptable performance objectives in the presence of uncertain issues, and thus to build resilience in supply chain.