An Ant based simulation optimization for Vehicle Routing Problem with stochastic demands

The Vehicle Routing Problem (VRP) is of considerable economic significance in logistic systems as it manages the distribution of goods to make an efficient transportation system. Considering a practical application, this paper solves a vehicle routing problem with stochastic demand (VRPSD) in which the customer demand has been modeled as a stochastic variable as opposed to conventional VRP. To deal with the additional computational complexity, this paper uses a simulation optimization approach to solve the VRPSD. To enhance the algorithm performance, a neighborhood-search embedded Adaptive Ant Algorithm (ns-AAA), an improved Ant Colony Optimization approach, is proposed. The performance of the proposed methodology is benchmarked against a set of test instances generated using Design of Experiment (DOE) techniques. The results verified the robustness of the proposed algorithm against Ant Colony Optimization and Genetic Algorithm, over which it always demonstrated better results, thereby proving its supremacy on the concerned problem.