The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

• VRPs in distribution centers with cross-docking are more complex than the traditional ones.
• This paper addresses the VRP of distribution centers with multiple cross-docks for processing multiple products.
• The formulated model is solved by using PSO with a Self-Learning strategy.
• The results obtained by SLPSO are compared with a GA based approach.

Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO.