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This paper addresses several algorithms based on self‐organizing neural network approach for routing problems. The algorithm for Traveling Salesman Problem is elaborated and the extension of the proposed algorithm to more complex problems namely, Multiple Traveling Salesmen and Vehicle Routing is discussed. In order to investigate the performance of the algorithms, a comprehensive empirical study has been provided. The simulations, which are conducted on standard data, evaluate the overall performance of this approach by comparing the results with the best known or the optimal solutions of the problems. The proposed algorithm shows significant advances in both qualities of the solution and computational efforts for most of the experimented data.

Distribution planning, which includes Vehicle Routing and Scheduling Problem (VRSP), has become an important element in Supply Chain impacting its service level and efficiency. Computer Aided Routing and Scheduling (CARS) has been developed and implemented, which can handle complicated distribution models using advanced heuristic optimization algorithms. A classification scheme is introduced to classify various types of routing and scheduling problems in a structured manner, based on the main objects of VRSP. The integrated system described in this paper can manage the dynamic aspects of the Supply Chain in practice. The modelling and solution approach in the CARS optimization engine, its user interface, sample performance measurements, and planning and operational features of the system are described in detail.