Abstract

This paper presents an integrated network design model for a supply chain consisting of an unreliable supplier, distribution centers (DCs) and customers. Due to unreliable performance of the supplier, the amount of yield which is received at each DC may be less than what was ordered. In this system, customers have random demands and there is flexibility in determining which customers are served. The problem is formulated as a nonlinear integer programming model that minimizes the expected total costs including costs of location, inventory, transportation, and lost sales. This model simultaneously determines which customers are served, where DCs are located and how DCs are assigned to the customers. In order to solve the model, an efficient solution method incorporating Lagrangian relaxation approach and genetic algorithm is developed. Finally, computational results for several instances of the problem are presented to demonstrate the effectiveness of the proposed approach.

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