Dokuz Eylül University Graduate School of Natural and Applied Sciences Modelling Strategic and Tactical Planning Problems in Closed-loop Supply Chains under Crisp and Fuzzy Environments

Nowadays, there has been a growing interest in recovery options such as recycling, remanufacturing and reusing in the scope of Reverse Logistics (RL) and Closed-Loop Supply Chain (CLSC) concepts due to the environmental, economical issues and legal obligations. Due to this fact, companies should take into account the utilized recovery option while preparing both strategic planning and tactical planning activities. On the other hand, there are lots of studies in the 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 namely Model I, II and III are proposed in this thesis. 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. In addition to minimize total costs of the CLSC, an unhandled objective (maximize collection of spent batteries) is taken into account based on the well known maximal coverage problem. Furthermore, new flexibility criteria namely total recycling and collection volume flexibility are added to the third objective function, total volume flexibility. A holistic strategic planning Model II with two objectives namely 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 medium-term 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.