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

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.

[1]  Stefan Minner,et al.  Dynamic product acquisition in closed loop supply chains , 2012 .

[2]  E. A. van deLaan,et al.  An (s,Q) inventory model with remanufacturing and disposal , 1996 .

[3]  Xin Zhang,et al.  A Robust Fuzzy Optimization Model for Closed-Loop Supply Chain Networks Considering Sustainability , 2019, Sustainability.

[4]  Daniel Kubek,et al.  Framework for Onboard Bus Comfort Level Predictions Using the Markov Chain Concept , 2019, Symmetry.

[5]  Paul H. Zipkin,et al.  Mortgages and Markov chains: a simplified evaluation model , 1993 .

[6]  Mahmod Othman,et al.  Predicting Daily Air Pollution Index Based on Fuzzy Time Series Markov Chain Model , 2020, Symmetry.

[7]  M. Ng,et al.  A multivariate Markov chain model for categorical data sequences and its applications in demand predictions , 2002 .

[8]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Marc Salomon,et al.  Strategic Issues in Product Recovery Management , 1995 .

[10]  K. Aase.  A MARKOV MODEL FOR THE PRICING OF CATASTROPHE INSURANCE FUTURES AND SPREADS , 2001 .

[11]  Tino T. Herden,et al.  Examples from Blockchain Implementations in Logistics and Supply Chain Management: Exploring the Mindful Use of a New Technology , 2018, Logistics.

[12]  Hassan Mina,et al.  An Integrated Hybrid Approach for Circular supplier selection and Closed loop Supply Chain Network Design under Uncertainty , 2020, Journal of Cleaner Production.

[13]  C. Carter,et al.  REVERSE LOGISTICS: A REVIEW OF THE LITERATURE AND FRAMEWORK FOR FUTURE INVESTIGATION , 1998 .

[14]  Luz Judith R. Esparza,et al.  A Hidden Markov Model to Address Measurement Errors in Ordinal Response Scale and Non-Decreasing Process , 2020 .

[15]  Chi-Chung Lin,et al.  Determining the Optimal Inventory and Number of Shipments for a Two-Resource Supply Chain with Correlated Demands and Remanufacturing Products Allowing Backorder , 2020 .

[16]  Chun-Wei R. Lin,et al.  Defective item inventory model with remanufacturing or replenishing in an integrated supply chain , 2011 .

[17]  Chwen-Tzeng Su,et al.  Inventory Model for the Batch Processing of Defective Products in Manufacturing and Remanufacturing , 2013 .

[18]  Lizbeth Naranjo,et al.  A hidden Markov model addressing measurement errors in the response and replicated covariates for continuous nondecreasing processes. , 2019, Biostatistics.

[19]  V. Daniel R. Guide,et al.  The Reverse Supply Chain , 2002 .

[20]  Erwin van der Laan,et al.  Quantitative models for reverse logistics: A review , 1997 .