A hybrid multiobjective evolutionary algorithm for solving truck and trailer vehicle routing problems
This paper considers a transportation problem for moving empty or laden containers for a logistic company. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper. The solution to the TTVRP consists of finding a complete routing schedule for serving the j...
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| Veröffentlicht in: | 2003 Congress on Evolutionary Computation Jg. 3; S. 2134 - 2141 Vol.3 |
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| Sprache: | Englisch |
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2003
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| ISBN: | 0780378040, 9780780378049 |
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| Abstract | This paper considers a transportation problem for moving empty or laden containers for a logistic company. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper. The solution to the TTVRP consists of finding a complete routing schedule for serving the jobs with minimum routing distance and number of trucks, subject to a number of constraints such as time windows and availability and multimodal combinatorial optimization problem, a hybrid multiobjective evolutionary algorithm (HMOEA) is applied to find the Pareto optimal routing solutions for the TTVRP. Detailed analysis is performed to extract useful decision-making information from the multiobjective optimization results. The computational results have shown that the HMOEA is effective for solving multiobjective combinatorial problems, such as finding useful trade-off solutions for the TTVRP. |
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| AbstractList | This paper considers a transportation problem for moving empty or laden containers for a logistic company. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper. The solution to the TTVRP consists of finding a complete routing schedule for serving the jobs with minimum routing distance and number of trucks, subject to a number of constraints such as time windows and availability and multimodal combinatorial optimization problem, a hybrid multiobjective evolutionary algorithm (HMOEA) is applied to find the Pareto optimal routing solutions for the TTVRP. Detailed analysis is performed to extract useful decision-making information from the multiobjective optimization results. The computational results have shown that the HMOEA is effective for solving multiobjective combinatorial problems, such as finding useful trade-off solutions for the TTVRP. |
| Author | Lee, L.H. Lee, T.H. Chew, Y.H. Tan, K.C. |
| Author_xml | – sequence: 1 givenname: K.C. surname: Tan fullname: Tan, K.C. organization: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore – sequence: 2 givenname: T.H. surname: Lee fullname: Lee, T.H. organization: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore – sequence: 3 givenname: Y.H. surname: Chew fullname: Chew, Y.H. organization: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore – sequence: 4 givenname: L.H. surname: Lee fullname: Lee, L.H. |
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| Snippet | This paper considers a transportation problem for moving empty or laden containers for a logistic company. A model for this truck and trailer vehicle routing... |
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| StartPage | 2134 |
| SubjectTerms | Availability Constraint optimization Containers Evolutionary computation Logistics Pareto optimization Routing Time factors Transportation Vehicles |
| Title | A hybrid multiobjective evolutionary algorithm for solving truck and trailer vehicle routing problems |
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