A Multiobjective Optimization Model in Automotive Supply Chain Networks

In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a...

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Published inMathematical problems in engineering Vol. 2013; no. 2013; pp. 1 - 10
Main Authors Nezamabadi-pour, Hossein, Ariffin, Mohd Khairol Anuar M., Zulkifli, Norzima, Ismail, Napsiah, Sadrnia, Abdolhossein, Mirabi, Hamed
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2013
Hindawi Limited
Subjects
Algorithms
Artificial intelligence
Automotive components
Carbon dioxide
Computer engineering
Economic models
Economics
Emissions
Environmental law
Environmental legislation
Gravitation
International conferences
Logistics
Manufacturing
Mathematical models
Multiple objective analysis
Networks
Objectives
Optimization
Optimization models
Search algorithms
Studies
Supply chains
Swarm intelligence
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Summary:In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA) has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2013/823876