Using Fuzzy Sets and Markov Chain Method to Carry out Inventory Strategies with Different Recovery Levels

• Main Authors: Tseng-Fung Ho, Chi-Chung Lin, Chih-Ling Lin
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Symmetry
• Subjects: green supply chain; reverse logistics; inventory; fuzzy; markov chain
• Online Access: https://www.mdpi.com/2073-8994/12/8/1226

In this study, we first analyze the usability of recycling products, and use the fuzzy set method to determine the main impact on recycling items and their corresponding weights by using the Analytic Hierarchy Process (AHP) to identify various impact recycling levels. The Group Decision Supporting System (GDSS) determines the test standards for the recycling rating. It provides a convenient way for recyclers or manufacturers to classify their own products and use fuzzy numbers to select a set of test standards. It can deduce the recovery rate and remanufacturing rate of different recycling processing levels through the Markov chain model to find out the inventory model and total cost. In the numerical analysis, we found that a recycling rate of more than 90% is probably a necessary decision. Since the processing cost of the 100% recovery rate is doubled, the inventory level and total cost will increase with it. Therefore, this study was combined with the reverse logistics method to find the appropriate decision-making strategy and plan, such as the optimal inventory level and recovery rate.