A possibilistic closed-loop supply chain: pricing, advertising and remanufacturing optimization

A.A. Taleizadeh, M. Karimi Mamaghan, S.A. Torabi

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

© 2018, The Natural Computing Applications Forum.This paper addresses pricing and reverse channel selection decisions in a closed-loop supply chain (CLSC) under fuzziness of demand function’s parameters. Despite numerous studies in which the demand is sensitive to selling price, in this paper demand function is considered as a function of both selling price and advertising level. Decisions are made in a CLSC consisting of a manufacturer, a retailer and a third party under centralized and decentralized decision making structures. In the decentralized structure, three different models are examined which differ on the member who collects used products from customers. Collection process is conducted by the manufacturer, the retailer or the third party. The problem is formulated as a Stackelberg game model in which the manufacturer acts as a leader. Moreover, various collection structures are deeply studied through a numerical analysis in which a real case study is provided and useful managerial insights are presented based on numerical results. The results show that the centralized structure outperforms the decentralized one in achieving the highest total expected profit, attaining highest demand by setting lowest selling price, and also by considering the environmental viewpoint and resource usage (achieving highest collection rate). Finally, sensitivity analysis on triangular fuzzy parameters is conducted to examine impact of triangular fuzzy parameters on model’s outputs.
Original languageEnglish
Pages (from-to)1195-1215
JournalNeural Computing and Applications
Volume32
Issue number4
DOIs
Publication statusPublished - 1 Feb 2020
Externally publishedYes

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