Determining Batch Sizes for Parts on Procurement and Remanufacturing Decisions - An Approach Based on Fuzzy Logic and Genetic Algorithm

Abstract

Recently, growing interest has been dedicated to reverse logistics, including the remanufacturing issue. However, weaknesses of inventory management models make manufacturers challenging in the reverse logistics context. Most models either provide fewer alternatives based on batch approaches, or do not deal with supply and demand uncertainties. Consequently, this paper proposes a batch inventory management model called GAFC (Genetic Algorithm and Fuzzy Logic based approach) for resolving the challenge. GAFC determines the batch sizes for ordering and remanufacturing the part that is procurable and remanufacturable, with the principle of inventory management cost minimization for the part. Experiments are taken for validating the model feasibility. Results show that GAFC outperforms the benchmark model. Results also reveal that, manufacturers can hold batch inventory management approaches even when reverse logistics is regarded as additional sources for the manufacturing systems.