Modelling strategic and tactical planning problems in closed-loop supply chains under crisp and fuzzy environments

Nowadays, there has been a growing interest in recycling, remanufacturing and reusing in the scope of Reverse Logistics (RL) and Closed-Loop Supply Chain (CLSC) concepts due to environmental, economical issues and legal obligations. For this reason, companies should take into account utilized recovery option while preparing both strategic and tactical planning activities. On the other hand, there are lots of studies in literature related to the RL and CLSC network design problem which takes place in strategic planning level but a few of them handles the tactical planning processes. However, multi-objective RL and CLSC network design models are rarely discussed in the literature. For filling these gaps, three mathematical models; Model I, II and III are proposed. A multi-objective, multi-echelon and multi-product mixed integer linear programming Model I is developed for a lead/acid battery CLSC in fuzzy environment. An unhandled objective (maximize collection of spent batteries) is taken into account based on the well known maximal coverage problem. A holistic strategic planning Model II with two objectives; maximization of total CLSC profit and minimization of total environmental impact along the CLSC network is developed for a tire collection and recovery system considering multiple recovery options and time periods. A fuzzy mixed integer programming Model III is proposed for tactical planning in a CLSC related to a conceptual product with remanufacturing option. Since the real world CLSCs are surrounded with uncertainty, capacities, demands, return rates, acceptance ratios, available production/remanufacturing times, transportation upper bounds and objective function value are considered as fuzzy.