This paper proposes the network design and optimization of a multi-product, multi-time, multi-echelon capacitated closed-loop supply chain in an uncertain environment. The uncertainty related to ill-known parameters like product demand, return volume, fraction of parts recovered for different product recovery processes, purchasing cost, transportation cost, inventory cost, processing, and set-up cost at facility centers is handled with fuzzy numbers. A fuzzy mixed-integer linear programming model is proposed to decide optimally the location and allocation of products/parts at each facility, number of products to be remanufactured, number of parts to be purchased from external suppliers and inventory level of products/parts in order to maximize the profit to the organization. The proposed solution methodology is able to generate a balanced solution between the feasibility degree and the degree of satisfaction of the decision maker. The proposed model has been tested with an illustrative example.
Jindal, A., Sangwan, K. S., & Saxena, S. (2015). Network design and optimization for multi-product, multi-time, multiechelon closed-loop supply chain under uncertainty. In Procedia CIRP (Vol. 29, pp. 656–661). Elsevier B.V. https://doi.org/10.1016/j.procir.2015.01.024