Joint Pricing and Inventory Routing Modeling in a Two Echelon Closed Supply Chain

This paper studies the pricing issue in a multi-period and multi-product closed-loop supply chain with price-dependent demands. The aim is to assign a location for the collection and disassemble center, vehicle routing, and material ordering in order to maximize the profit. In the study, a non-linear mathematical model is presented for small scale problems. Due to the NP-hardness of the problem, two met heuristics, genetic algorithm and particle swarm optimization algorithm, are applied to solve medium and large scale problems. The algorithms are validated by comparing their results with those of the mathematical model. Finally, the performance comparison of the two met heuristics through statistical analysis is demonstrated that the particle swarm optimization algorithm performance outperforms the genetic algorithm.