A multi level priority clustering NN based approach for solving heterogeneous vehicle routing problem

This research presents a two phases heuristic neural network 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 multiple depot vehicle routing problem 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 vehicle routing problem model using heuristic clustering method with Hopfield network. 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. Despite other optimization approaches, which solve case studies involving at most 25 nodes optimally, the proposed algorithm overcomes this limitation by a preprocessing stage by applying clustering on nodes. In this approach, a hierarchical hybrid procedure involving one heuristic and one neural network phases was developed.