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Analysis of Evolution Mechanism and Optimal Reward-Penalty Mechanism for Collection Strategies in Reverse Supply Chains: The Case of Waste Mobile Phones in China

Yangke Ding (), Lei Ma (), Ye Zhang () and Dingzhong Feng ()
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Yangke Ding: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Lei Ma: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Ye Zhang: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Dingzhong Feng: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China

Sustainability, 2018, vol. 10, issue 12, 1-17

Abstract: The aim of this paper is to discuss the coopetition (cooperative competition) relationship between a manufacturer and a collector in the collection of waste mobile phones (WMPs) and examine the evolution mechanism and the internal reward-penalty mechanism (RPM) for their collection strategies. A coopetition evolutionary game model based on evolutionary game theory was developed to obtain their common and evolutional collection strategies. The pure-strategy Nash equilibriums of this model were obtained which showed their collection strategy choices of perfect competition or cooperation. The mixed strategy Nash equilibrium was obtained which revealed evolution trends and laws. In addition, the optimal RPM was obtained in the sensitivity analysis of related parameters. The example of WMPs in China was taken to examine the simulation of the RPM. Results show that (i) although the manufacturer and the collector may change their strategies of cooperation and competition over time, cooperation is their best choice to increase payoffs; (ii) the optimal RPM is beneficial to propel their cooperation tendency and then to increase their payoffs.

Keywords: reverse supply chain; collection strategy; waste mobile phones; evolutionary game theory; evolution mechanism; reward-penalty mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2018
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