Integrating Recycling and Emission Reduction
A Business Strategy Analysis With Multi‐Objective Mixed‐Integer Linear Programming Framework for Optimising Sustainable Closed‐Loop Supply Chain Network Design
Bibliographic Data
| ID | 22418777 |
|---|---|
| Authors | Reza Eslamipoor (0009-0001-4686-8484, Business School South Bank University London UK, corresponding author) |
| Year | 2026 |
| Publication date | 2026-06-26 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Business Strategy and the Environment (JOURNAL) |
| Journal identifiers | ISSN: 0964-4733 • E-ISSN: 1099-0836 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/bse.71137 |
| OpenAlex | W7166338475 |
| Language | EN |
| References cited | 72 |
This research develops an integrated mixed‐integer linear programming (MILP) model for closed‐loop supply chain network design that optimises competing economic and environmental objectives including profit maximisation, supplier quality improvement and CO 2 emission reduction. The primary aim of this study is to identify which solution techniques perform best when dealing with different sizes of problems. The multi‐objective particle swarm optimisation (MOPSO) method demonstrated superior performance over NSGA‐II by achieving 35%–45% faster convergence rates and delivering 20%–30% better solution quality. The epsilon‐constraint approach with CPLEX showed better performance for solving smaller problems. The analysis revealed that return rates, together with material recovery expenses, function as essential elements which determine the profitability of the disassembly process. The research proves that multi‐objective optimisation methods work efficiently with real supply chain sizes. These findings support the development of decision support systems for sustainable operations management.
Decision support system · Linear programming · Particle swarm optimization · Profitability index · Supply chain · Supply chain management · Supply chain network · Process Optimization and Integration · Supply Chain Resilience and Risk Management · Sustainable Supply Chain Management
| Citation velocity | historical |
|---|---|
| Highly cited | No |