Optimization Vehicle Routing Problem with Cross Entropy Method and Branch & Bound Algorithm

Abstract

An alternate formulation of the classical vehicle routing problem (VRP) is considered for distributing fresh potatoes from warehouse to the food outlet or shop. We propose a new-heuristic (meta-heuristic) method to solve the problem, base on the Cross-Entropy method. In order to better estimate the objective function at each point in the domain, we incorporate Monte Carlo sampling. This creates many practical issues, especially the decision as to when to draw new samples and how many samples to use. We Also develop a framework for obtaining exact solutions and tight lower bounds for the problem under various conditions by grouping the destination. This is used to assess the performance of the algorithm. Numerical results are presented for various problem instances to illustrate the ideas. Finally, solution obtained by Cross-Entropy compared with Branch and Bound Algorithm. It is proved that two methods have similar solution, with number of iteration was similar relatively.