A Robust Modeling of Inventory Routing in Collaborative Reverse Supply Chains

We propose a robust model for optimizing collaborative reverse supply chains. The primary idea is to develop a collaborative framework that can achieve the best solutions in an uncertain environment. Firstly, we model the exact problem in the form of a mixed integer nonlinear program. To consider uncertainty, robust optimization is employed that searches for an optimal solution with nearly all possible deviations in mind. In order to allow the decision maker to vary the protection level, we use the "budget of uncertainty" approach. To solve the NP-hard problem, we suggest a hybrid heuristic algorithm combining dynamic programming, ant colony optimization and tabu search. To assess the performance of the algorithm, two validity tests are made, first by comparing with the previously solved problems and next by solving a sample problem with more than 900 combinations of parameters and comparing the results with the nominal case. In conclusion, the results of different combinations and prices of robustness are compared and some directions for future research are suggested.