An adaptive large neighbourhood search algorithm for the orienteering problem

•New heuristic based on Adaptive Large Neighbourhood Search.•Uses a clustering of the input graph•Extensive computational experiments on existing benchmarks.•Compares favourably with heuristic algorithms from the literature. We propose a new heuristic algorithm for the well-known Orienteering Proble...

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Bibliographic Details
Published inExpert systems with applications Vol. 123; pp. 154 - 167
Main Author Santini, Alberto
Format Journal Article
LanguageEnglish
Published New York Elsevier Ltd 01.06.2019
Elsevier BV
Subjects
Adaptive algorithms
Adaptive large neighbourhood search
Algorithms
Apexes
Clustering
Graph theory
Heuristic methods
Metaheuristics
Orienteering
Orienteering problem
Production scheduling
Search algorithms
Travel time
Upper bounds
Adaptive large neighbourhood search
Clustering
Metaheuristics
Orienteering problem
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Summary:•New heuristic based on Adaptive Large Neighbourhood Search.•Uses a clustering of the input graph•Extensive computational experiments on existing benchmarks.•Compares favourably with heuristic algorithms from the literature. We propose a new heuristic algorithm for the well-known Orienteering Problem, which aims at maximising the prize collected at vertices of a graph, visiting them through a simple closed tour whose length (also known as travel time) is limited by an upper bound. The algorithm is based on the Adaptive Large Neighbourhood Search metaheuristic paradigm, and uses a clustering of the graph to operate on groups of nearby vertices. Extensive computational results show that the proposed algorithm is able to find very good solutions (it produced 27 new best known solutions on a set of benchmark instances) and that it fares favourably compared with other state-of-the-art heuristics when tested with both long and short computation time budgets.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2018.12.050