A Genetic Algorithm-based optimization model for supporting green transportation operations |
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| Affiliation: | 1. Department of Electronics Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region;2. Department of Computer Science and Technology, Soochow University, Suzhou 215006, China;1. Department of Information Management at Fortune Institute of Technology, Kaohsiung, Taiwan;2. Thecus Technology Corporation, Taiwan;3. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan;1. Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;2. Graduate Telecommunications and Networking Program, University of Pittsburgh, PA, USA;3. China Internet Research Lab, China Science and Technology Network, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China;4. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China;1. Department of Business Administration, Lunghwa University of Science and Technology, Taiwan;2. Department of Finance, MingDao University, Taiwan;3. Business School, the University of Nottingham, United Kingdom;1. Department of Software Engineering, Faculty of Telecommunication and Information Engineering, University of Engineering and Technology, Taxila, Pakistan;2. School of Electronics and Computer Science, University of Southampton, Highfield Campus, Southampton SO17 1BJ, United Kingdom |
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| Abstract: | Green Logistics (GL) has emerged as a trend in the management of the distribution of goods and the collection of end-of-life products. With its focus on maximizing the economic and environmental value by means of recycling and emission control, GL contributes to the sustainable development of industry but also requires a more comprehensive transportation scheme when conducting logistics services. This study is motivated by the practice of delivering and collecting water carboys. In this paper, a Genetic Algorithm-based optimization model (GOM) is proposed for designing a green transportation scheme of economic and environmental cost efficiency in forward and reverse logistics. Two vehicle routing models with simultaneous delivery and pickup (full or partial pickup) are formulated and solved by a Genetic Algorithm. A cost generation engine is designed to perform a comprehensive cost comparison and analysis based on a set of economic and environmental cost factors, so as to examine the impact of the two models and to suggest optimal transportation schemes. The computational experiments show that the overall cost is evidently lower in the full pickup model. Notably, the impact of product cost after recycling and reusing empty carboys on total cost is more significant than the impact of transportation cost and CO2 emission cost. In summary, the proposed GOM is capable of suggesting a guidance for the logistics service providers, who deal with green operations, to adopt a beneficial transportation scheme so as to eventually achieve a low economic and environmental cost. |
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| Keywords: | Reverse logistics Vehicle routing Genetic Algorithm Recycle Emission |
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