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An Optimization Model of a Retailer and a Manufacturer in a Green Supply Chain

EasyChair Preprint 11377

13 pagesDate: November 23, 2023

Abstract

In recent years, the development of a green supply chain model that takes sustainability into consideration has become an urgent issue in corporate management and policy operation. For example, in the fashion industry, the proper circulation of not only used items purchased by consumers, but also unsold items generated in retail stores is one of the most important issues. In this study, we examine a supply chain model for the appropriate circulation of unsold new products from retail outlets. Specifically, we considered a supply chain model in which a retailer's inventory is divided into two states under stochastic demand fluctuations: new and old items, and the unsold old items are collected and reproduced by a manufacturer. By formulating this model as a Markov decision process, the optimal decisions regarding the retailer's ordering policy and the retail price and the manufacturer's wholesale price are obtained. Optimal investment and appropriate institutional design to reduce CO2 emissions generated in a supply chain are also considered. Specifically, we examine the decision of green investment in manufacturing technology to reduce CO2 emissions when a manufacturer produce items. And we examine the design of a carbon tax system to control CO2 emissions. Sensitivity analysis on the carbon tax system shows that raising the carbon tax rate increases the optimal retail price, the optimal wholesale price, and the optimal green investment.

Keyphrases: Green Investment, Markov Decision Process, Supply Chain Management, carbon tax, green supply chain

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:11377,
  author    = {Wataru Sakurai and Koichi Nakade},
  title     = {An Optimization Model of a Retailer and a Manufacturer  in a Green Supply Chain},
  howpublished = {EasyChair Preprint 11377},
  year      = {EasyChair, 2023}}
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