Development and comparison of genetic algorithms for vehicle routing problem with simultaneous deliveries and pickups in a supply chain network

The increased focus on environmental awareness and protection compelled many organizations across the world to adopt reverse logistics as their key functional business area. Vehicle routing problem with simultaneous deliveries and pickups (VRPSDP) is one of the major issues under reverse logistics and in this paper, an attempt is made to solve the same. Since this problem belongs to NP-hard class, an increase in number of customer nodes will increase the computational complexity of the problem. Due to the robustness of genetic algorithm in solving complex problem, Genetic Algorithms (GAs) with four different designs are proposed to solve VRPSDP. Experimental results are tested statistically using ANOVA with three factors, viz., “Problem Size”, “Algorithm” and “Mutation Probability”. Based on the significance using ANOVA with respect to the factor “Algorithm”, Duncan’s multiple range test is carried out to draw inferences on proposed algorithms.