  We present novel mathematical models for inventory management within a
reverse logistics system. Technological advancements, sustainability
initiatives, and evolving customer behaviours have significantly increased the
demand for repaired products. Our models account for varying demand levels for
newly produced and repaired items. To optimize overall costs with constrained
scenarios, we formulated mixed integer programming problems. Solution
procedures for the proposed problems are introduced, and the accuracy of these
solutions has been validated through numerical experiments. Additionally, we
address the cost of waste disposal as an environmental concern. This paper
develops a multiobjective mathematical model and provides an algorithm for the
Pareto solution. Various scalarization techniques are utilized to identify the
Pareto front, and a comparison of these techniques is presented.
