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Mean-Variance Analysis for Optimal Operation and Supply Chain Coordination in a Green Supply Chain

  • Yamaguchi, Shin (Graduate School of Electrical and Electronic Systems, College of Engineering, Osaka Prefecture University) ;
  • Goto, Hirofumi (Course of Electrical and Electronic Systems, College of Engineering Osaka Prefecture University) ;
  • Kusukawa, Etsuko (Graduate School of Electrical and Electronic Systems, College of Engineering Osaka Prefecture University)
  • Received : 2016.03.22
  • Accepted : 2017.01.06
  • Published : 2017.03.30

Abstract

It is urgently-needed to construct a green supply chain (GSC) from collection of used products through recycling of them to sales of products using the recycled parts. Besides, it is necessary to consider the uncertainty in product demand as a risk in a GSC. This study proposes the optimal operations for a GSC with a retailer and a manufacturer. A retailer pays an incentive for collection of used products from customers and sells a single type of products in a market. A manufacturer produces the products ordered by the retailer, using recyclable parts with acceptable quality and compensates the collection cost of used products as to the recycled parts. This paper discusses the following risk attitudes: risk-neutral attitude, risk-averse attitude, and risk-prone attitude. Using mean-variance analysis, the optimal decisions for product order quantity, collection incentive, and lower limit of quality level, in the decentralized GSC (DGSC) and the integrated GSC (IGSC) are made. DGSC optimizes the utility function of each member. IGSC does that of the whole system. The analysis numerically investigates how (i) risk attitude and (ii) quality of recyclable parts affect the optimal operations. Supply chain coordination between GSC members to shift IGSC from DGSC is discussed.

Keywords

Green Supply Chain;Uncertainty in Product Demand;Risk Attitude;Mean-Variance Analysis;Supply Chain Coordination

Acknowledgement

Supported by : Japan Society for the Promotion of Science

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