Big data-driven optimization for sustainable reverse logistics network design

The reverse logistics network (RLN) design for sustainable supply chain management is a strategic decision in network configuration, and is higher influenced by uncertainty. This paper applies a bi-level stochastic multi-objective model to design an RLN for a disposable product recycling management...

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Veröffentlicht in:	Journal of ambient intelligence and humanized computing 2023-08, Vol.14 (8), p.10867-10882
Hauptverfasser:	Khoei, Mohammad Amin, Aria, Seyed Sina, Gholizadeh, Hadi, Goh, Mark, Cheikhrouhou, Naoufel
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial Intelligence Big Data Competitive advantage Computational Intelligence Cost control Decision making Design for recycling Electronics industry Engineering Environmental aspects Literature reviews Network design Optimization Original Research Product returns Profits Reverse logistics Robotics and Automation Stochastic models Supply chain management Supply chains Sustainability User Interfaces and Human Computer Interaction
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Beschreibung
Zusammenfassung:	The reverse logistics network (RLN) design for sustainable supply chain management is a strategic decision in network configuration, and is higher influenced by uncertainty. This paper applies a bi-level stochastic multi-objective model to design an RLN for a disposable product recycling management system. The goal is to balance the overall network cost against the associated environmental risks. An LP-metric based sample average approximation is formulated to solve the optimization problem. The model is validated numerically through a disposable product firm.
ISSN:	1868-5137 1868-5145
DOI:	10.1007/s12652-022-04357-z