Optimal inventory control policy of a hybrid manufacturing- remanufacturing system using a possibilistic linear programming approach

Abstract

Remanufacturing is the process of converting used products back to like-new products. Production planning of inventory control policies
in the remanufacturing of used products with different prioritizations (remanufacturing vs manufacturing) is investigated in this case study.
For the hybrid manufacturing/remanufacturing production planning problem, the imprecision of customer demand, related operating costs, and
the number of returned used components are considered to be the main uncertainty. As a result, the fuzzy set theory is employed due to the presence of the imprecise information. To defuzzify imprecise data, the Possibilistic Linear Programming (PLP) with the weighted average method is applied. The proposed approach maximizes the most likely value of the profit, minimizes the risk of obtaining a lower profit, and maximizes the possibility of obtaining a higher profit for each production planning policy at the same time. The result shows that the Priority-To-Remanufacturing (PTR) policy shows a higher profit from each objective function and is solved with a higher satisfaction value than the policy of giving more Priority-To-Manufacturing (PTM). For instance, the most likely profits of the PTR policy from each scenario are ranging from $ 66,774 to $ 114,280 as compared to $ 66,610 to $ 88,882 from the PTM policy and a higher satisfaction values of the linear membership function are ranging from 38.34% to 53.95% from
the PTR policy as compared to 39.13% to 53.40% from the PTM policy. As a result, PLP can help decision makers to be well aware of the risks and
effects of uncertainty on their plans, so they can prepare in advance for such scenarios.