2010Unpublished venueRequires access

A Variable Neighborhood Tabu Search Algorithm for the Heterogeneous Fleet Vehicle Routing Problem with Time Windows

Wei Luo, Fu Zhuo

Open publisher page 3 citations

Abstract

The heterogeneous fleet vehicle routing problem with time windows is a variant of the classical vehicle routing problem. This paper defines a mathematical model of this problem and proposes a variable neighborhood tabu search algorithm to solve it. The initial solution is obtained by GENIUS and the giant tour algorithm. Our algorithm employs a variable neighborhood mechanism to search the optimal solution based upon four neighborhoods. In addition, the local search results are improved by the tabu search algorithm. The proposed algorithm appears to be effective when tested on benchmark instances from the literature.

About this research paper

What this paper is about

The heterogeneous fleet vehicle routing problem with time windows is a variant of the classical vehicle routing problem. This paper defines a mathematical model of this problem and proposes a variable neighborhood tabu search algorithm to solve it. The initial solution is obtained by GENIUS and the giant tour algorithm. Our algorithm employs a variable neighborhood mechanism to search the optimal solution based upon four neighborhoods. In addition, the local search results are improved by the tabu search algorithm. The proposed algorithm appears to be effective when tested on benchmark instances from the literature.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Available abstract

The heterogeneous fleet vehicle routing problem with time windows is a variant of the classical vehicle routing problem. This paper defines a mathematical model of this problem and proposes a variable neighborhood tabu search algorithm to solve it. The initial solution is obtained by GENIUS and the giant tour algorithm. Our algorithm employs a variable neighborhood mechanism to search the optimal solution based upon four neighborhoods. In addition, the local search results are improved by the tabu search algorithm. The proposed algorithm appears to be effective when tested on benchmark instances from the literature.

Key concepts: Tabu search, Vehicle routing problem, Benchmark (surveying), Guided Local Search, Mathematical optimization, Variable neighborhood search, Variable (mathematics), Computer science

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