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Erschienen in: Neural Computing and Applications 4/2020

17.08.2018 | Original Article

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

verfasst von: Ata Allah Taleizadeh, Maryam Karimi Mamaghan, Seyed Ali Torabi

Erschienen in: Neural Computing and Applications | Ausgabe 4/2020

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Abstract

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.

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Metadaten
Titel
A possibilistic closed-loop supply chain: pricing, advertising and remanufacturing optimization
verfasst von
Ata Allah Taleizadeh
Maryam Karimi Mamaghan
Seyed Ali Torabi
Publikationsdatum
17.08.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 4/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3646-3

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