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 modeling-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 by employing an advanced operational law. We established several properties of the cost function, which permit us to derive a heuristic search procedure. Furthermore, we design an innovative integrated cross-entropy algorithm incorporating the proposed heuristic approach into the cross-entropy framework. The numerical results provide strong evidence that our approach is both effective and efficient, especially for handling large-scale problems. Finally, our framework can be used by decision makers and industry to determine economically acceptable performance objectives for coordinating the ordering of several items, and thus to build resilience in supply chain