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($ ILS $-$ BD $) to solve problems approximately. Computational results demonstrate that the solutions generated by $ ILS $-$ BD $ have advantages in terms of quality and robustness.