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
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container_issue 8
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container_title Journal of ambient intelligence and humanized computing
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creator Khoei, Mohammad Amin
Aria, Seyed Sina
Gholizadeh, Hadi
Goh, Mark
Cheikhrouhou, Naoufel
description 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.
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subjects 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
title Big data-driven optimization for sustainable reverse logistics network design
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