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
Hauptverfasser: Tan, K.C., Lee, T.H., Chew, Y.H., Lee, L.H.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 2003
Schlagworte:
Availability
Constraint optimization
Containers
Evolutionary computation
Logistics
Pareto optimization
Routing
Time factors
Transportation
Vehicles
ISBN:0780378040, 9780780378049
Online-Zugang:Volltext
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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.
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.
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  givenname: T.H.
  surname: Lee
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  organization: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore
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  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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Volume 3
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