A remanufacturing system with imperfect sorting: Deterministic and probabilistic models

Thesis Type: Post Graduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Industrial Engineering, Turkey

Approval Date: 2018

Student: MERVE TAN



In this study, we focus on inaccurate sorting process with classification errors in a reverse supply chain comprising a remanufacturer and a collector under deterministic demand in a single time period. The collector acquires used items from end-users and the remanufacturer reproduces them to serve the deterministic demand of remanufactured products. There are two sources of uncertainty: uncertain quality of used items and uncertain quality of sorted items due to imperfect testing. Used items are categorized into two quality states by imperfect inspection: remanufacturable or non-remanufacturable and the actual condition of items is revealed after the remanufacturer’s disassembly process. Under this environment, firstly, we construct different settings without incorporation of randomness in the inspection process and compare their optimal solutions to assess the effects of pricing decisions, the change in the agents’ roles and sorting location. We observe that the sorting location does not affect the optimal collection quantity under deterministic market demand. However, the optimal solution changes regarding to the change in the agent responsible for sorting under the case where the demand and supply are price sensitive. We also show that the channel leadership does not affect the optimal solution when the transfer price is exogenous. Secondly, we investigate the impact of ignoring randomness due to sorting errors on the optimal solution and profits. The results show that disregarding randomness hurts the collector more than the remanufacturer. Lastly, we conduct an extensive computational study to analyze the effects of problem parameters on this randomness impact.