A hybrid method to design and optimize a battery closed-loop supply chain: multi-objective approach

There are a variety of prominent factors associated with total expected profit of a closed-loop supply chain (CLSC). In a forward flow, volatility in transportation cost, inventory cost, and forecasting the market’s demand are the most challenging issues for decision makers, while determining the rate of returned products and efficiency in recycling the returned products are crucial parameters to predict in reverse flow. In this thesis, it is aimed to develop and apply mixed-integer linear programming (MILP), scenario-based analysis, and fully fuzzy programming (FFP) methods to maximize the profit for a multi-echelon, multi-components, multi-product, multi-period battery CLSC in Vancouver, Canada. Furthermore, the proposed model is extended to multi-objective to consider the green factors related to plants and battery recovery centers. Fuzzy analytic network process (Fuzzy ANP) is utilized to convert the qualitative factors to the measurable parameters. Then, distance technique and ℇ-constraint method are utilized for solving the multi-objective problem.