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Abstract

The paper proposes a novel Economic Production Quantity (EPQ) inventory model within a reverse logistics framework, addressing new and repaired products with varying quality and demand patterns. The model integrates production and remanufacturing rates as functions of lot sizes and cycle numbers to develop a feasible inventory cost function. A key contribution of the study is formulating a multiobjective optimization framework that simultaneously minimizes inventory costs and accounts for environmental sustainability by considering greenhouse gas (GHG) emissions and energy consumption during production processes. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model, with integer constraints on lot sizes and cycle counts and a continuous return rate. Numerical case studies taking test problems from existing literature are used to validate the model through extensive sensitivity analyses. Both mathematical optimization and heuristic optimization methods are applied to solve multiobjective optimization problems, and Pareto fronts are illustrated along with the interpretation of the results. The results, obtained using solvers in MATLAB and AMPL, highlight the models ability to balance operational efficiency and environmental responsibility. Pareto frontiers generated from the analysis provide strategic insights for decision-makers seeking to optimize cost and sustainability in inventory systems.