A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment
Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/125893 |
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| _version_ | 1832550749749379072 |
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| author | Yanhui Li Hao Guo Lin Wang Jing Fu |
| author_facet | Yanhui Li Hao Guo Lin Wang Jing Fu |
| author_sort | Yanhui Li |
| collection | DOAJ |
| description | Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment. |
| format | Article |
| id | doaj-art-2a91bbd573484ae9899ee3057a8a9d12 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-2a91bbd573484ae9899ee3057a8a9d122025-02-03T06:05:52ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/125893125893A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain EnvironmentYanhui Li0Hao Guo1Lin Wang2Jing Fu3School of Information Management, Central China Normal University, Wuhan 430079, ChinaSchool of Information Management, Central China Normal University, Wuhan 430079, ChinaSchool of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Information Management, Central China Normal University, Wuhan 430079, ChinaFacility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.http://dx.doi.org/10.1155/2013/125893 |
| spellingShingle | Yanhui Li Hao Guo Lin Wang Jing Fu A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment The Scientific World Journal |
| title | A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment |
| title_full | A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment |
| title_fullStr | A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment |
| title_full_unstemmed | A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment |
| title_short | A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment |
| title_sort | hybrid genetic simulated annealing algorithm for the location inventory routing problem considering returns under e supply chain environment |
| url | http://dx.doi.org/10.1155/2013/125893 |
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