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

Abstract

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.

An Overview of Fuzzy Techniques in Supply Chain Management: Bibliometrics, Methodologies, Applications and Future Directions

“…Fuzzy AHP: Shete et al (2020) used the Pythagorean fuzzy AHP to investigate the sustainable SC innovation enablers to achieve the sustainability of SC. Ho et al (2020) used the fuzzy AHP to identify various impact recycling levels and their impact on recycling items. Narayanan et al (2019) prioritized the barriers in implementing sustainable practices in rubber product manufacturing by adopting the fuzzy AHP.…”

Evaluation of Blended Learning Effect of Engineering Geology Based on Online Surveys

“…The main characteristic of this method is to use less quantitative information to mathematically formalize the thinking process of decision-making, in order to provide a simple decision-making method for complex decision-making problems with multiple objectives, multiple criteria, or no structural characteristics. It comprises a model and method for making decisions on complex systems that are difficult to fully quantify [26,27]. Darko et al have reviewed papers based on the analytic hierarchy process in engineering management journals [28].…”