Supply chains in equipment-intensive service industries often involve repair operations. In this context, tactical inventory planning is concerned with optimally planning supplies and repairs based on demand forecasts and in the face of conflicting business objectives. This paper considers closed-loop supply chains and proposes a mixed-integer programming model and a metaheuristic approach to this problem. The model is open to a variety of network topologies, site functions and transfer policies. It also accommodates multiple objectives by the means of a weighted cost function. We report experiments on pseudorandom instances designed to evaluate plan quality and impact of cost weightings. In particular, we show how appropriate weightings allow to emulate common planning strategies (e.g., just-in-time replenishment, minimal repair) and validate approaches.

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This paper proposes a constraint optimization model accepting different network topologies, site functions and distribution/transfer policies, applies it to a real case study or closed-loop supply chains in telecommunications services, and compares two approaches-a mixed-integer programming and a metaheuristics -to solve this problem. Its cost function is a linear combination of storage, transport, backorder and repair costs. We report experiments on pseudo random instances designed to evaluate plan quality and impact of cost weightings. Experiments validate approaches and compare our 2 methods. Finally, we discuss the possible extensions of the model to fit other specific cases and create interest among supply chain experts.

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