Genetic Algorithms and Simulation for After-sales Supply Chain Re-engineering Process

A.G. Bruzzone, P. Giribone, and R. Revetria (Italy)


GA, TSP, Simulation


This paper concerns about the physical distribution of parts for the Automotive After-Sales Supply Chain. In order to guarantee an high customer satisfaction level without keeping large inventories companies are trying to serve the customers “Just-In-Time”, from a single Central Distribution Centre (CDC). Due to the great variety of products, the complexity of market channels and the more restrictive customers’ requirements, it appears that the critical aspect in the Automotive After Sales Supply Chain Management is shipment between the CDC and the various Retailers. Due to the many different parameters that may be taken into consideration (i.e. service proposition, lead time, number of vans, minimal ordered quantity) this Logistics problem requires ad hoc techniques to be solved in very proper way. Several approaches were presented (i.e. Cross-Docking, Direct Shipment, etc.) in the recent years, in this paper the authors present an innovative approach based on Genetic Algorithms applied to the Shipment Network Optimization. Starting with the solution of the Travel Salesman Problem (TSP) the proposed approach faces the great complexity of such problem, by integrating in a unique architecture both Simulation and Optimization Techniques. An example from a real life application concerning a 1200 Retailers Network in the UK is presented showing the effectiveness of the proposed methodology.

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