A closed-loop supply chain robust optimization for disposable appliances

Supply chain design is one of the important and strategic decisions influencing competitive advantages and economic growth. The increasing importance of using waste products has led many companies to move toward the design of a closed-loop supply chain network. The current research considers a multi...

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Bibliographic Details
Published in:Neural computing & applications 2020-04, Vol.32 (8), p.3967-3985
Main Authors: Gholizadeh, Hadi, Tajdin, Ali, Javadian, Nikbakhsh
Format: Article
Language:English
Subjects:
Artificial Intelligence
Business competition
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Economic development
Genetic algorithms
Image Processing and Computer Vision
Mathematical models
Optimization
Original Article
Parameter sensitivity
Parameter uncertainty
Probability and Statistics in Computer Science
Supply chains
Waste disposal
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Summary:Supply chain design is one of the important and strategic decisions influencing competitive advantages and economic growth. The increasing importance of using waste products has led many companies to move toward the design of a closed-loop supply chain network. The current research considers a multilayered closed-loop supply chain for disposable appliances recycling network. Since many parameters, especially demand and costs, are uncertain, discrete random scenarios are used to describe the parameters. The network modeling aimed at maximizing the value of returning products in the reverse network and products manufactured by the forward network. Optimization of the disposable appliance supply chain network is handled by the combined genetic algorithm and robust optimization. Finally, the computational analysis shows that the proposed model obtains effective solutions for the closed-loop network of disposable appliances. Also, the analysis shows that the genetic algorithm has a good convergence. Comparison of different scenarios shows that the objective function is highly sensitive to uncertain parameters. Hence, network modeling based on different scenarios can be a good approach for deciding under uncertainty of parameters.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-018-3847-9