ROBUST OPTIMIZATION APPROACHES FOR THE PERISHABLE PRODUCT INVENTORY ROUTING PROBLEM WITH DEMAND UNCERTAINTY

In this study, we focused on determining routing, inventory, and delivery quantities in a multi-period inventory routing problem for perishable products with demand uncertainty. Product lifetime and gradual deterioration were considered to handle perishability. A robust optimization model based on a nominal problem was formulated to handle demand uncertainty. We propose exact approaches called Robust Counterpart Reformulation(RCR) based on the duality theorem and Benders Decomposition(BD) based on the cutting plane. For small-scale instances, computational results demonstrate that RCR has advantages in terms of cost saving, computational time, and the number of instances solved. For medium- or large-scale instances, we developed a heuristic called Iterated Local search based on Benders Decomposition(ILSBD) to solve problems approximately. Computational results demonstrate that the solutions generated by ILS-BD have advantages in terms of quality and robustness.