A multi level priority clustering GA based approach for solving heterogeneous Vehicle Routing Problem (PCGVRP)

Abstract

This research presents a two phase heuristic - evolutionary combined algorithmic approach to solve multiple-depot routing problem with heterogeneous vehicles. It has been derived from embedding a heuristic-based two level clustering algorithm within a MDVRP optimization framework. In logistic applications, customers have priority based on some logistic point of view. The priority levels of customers, affect distribution strategy specially in clustering level. In this research we have developed an integrated VRP model using heuristic clustering method and a genetic algorithm, GA, of which operators and initial population are improved. In the first phase of the algorithm, a high level heuristic clustering is performed to cluster customers serviced by a special depot. Next, a low level clustering is done for each depot to find clusters serviced by a single vehicle. Likewise other optimization approaches, the new formulation can efficiently solve case studies involving at most 25 nodes to optimality. To overcome this limitation, a preprocessing stage which clusters nodes together is initially performed to yield a more compact cluster-based problem formulation. In this way, a hierarchical hybrid procedure involving one heuristic and one evolutionary phase was developed.