Designing a multi-objective closed-loop supply chain: a two-stage stochastic programming, method applied to the garment industry in Montréal, Canada Designing a multi-objective closed-loop supply chain: a two-stage stochastic programming, method applied to the garment industry in Montréal, Canada

The global population continues to grow, which expands demand for raw materials. Meanwhile, governments are developing circular economy strategies within cities and their industries. A circular economy utilizes refurbishing, reusing, remanufacturing, and repairing of products and materials. For comp...

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Published in:Environment, development and sustainability Vol. 26; no. 3; pp. 6131 - 6162
Main Authors: Shafiee Roudbari, Erfan, Fatemi Ghomi, S. M. T., Eicker, Ursula
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.03.2024
Springer Nature B.V
Subjects:
Canada
Circular economy
Closed loops
Clothing industry
Companies
Developed countries
Earth and Environmental Science
Ecology
Economic Geology
Economic Growth
Entrepreneurship
Environment
Environmental Economics
Environmental Management
Fashion
Fast fashion
Function
Garment industry
Garments
Networks
Objective function
Objectives
Raw materials
Refurbishment
Remanufacturing
Stochastic programming
Supply
supply chain
Supply chains
Sustainable Development
textile industry
Uncertainty
Canada
Garment industry
Multi-objective optimization
Circular economy
Sustainable network
Closed-loop supply chain
Stochastic programming
ISSN:1573-2975, 1387-585X, 1573-2975
Online Access:Get full text
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Summary:The global population continues to grow, which expands demand for raw materials. Meanwhile, governments are developing circular economy strategies within cities and their industries. A circular economy utilizes refurbishing, reusing, remanufacturing, and repairing of products and materials. For companies, this involves to set targets and to rethink their supply chain. This paper seeks to model an exhaustive multi-echelon closed-loop supply chain (CLSC) network. This network functions within uncertainty, and the model optimizes three different objectives. The first objective function maximizes the network's profit; the second objective function minimizes network emissions. The last objective function maximizes job positions created by the network. Optimizing three contradicting objectives is a problem, so an augmented epsilon constraint method is applied to improve the model. Given the rise of fast fashion in developed countries, this model is used in the clothing industry in Montreal, Canada. The model includes three scenarios over five years with two types of products. The result shows the attractiveness of such a network for companies looking for profit, sustainability, and entrepreneurship in the garment industry.
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ISSN:1573-2975
1387-585X
1573-2975
DOI:10.1007/s10668-023-02953-3