New benchmark instances for the Capacitated Vehicle Routing Problem

•We show the limitations of existing Capacitated Vehicle Routing Problem instances.•We propose 100 new instances and evaluate recent exact and heuristic methods.•The same generating scheme is used to create an extended benchmark of 600 instances.•Extensive experiments and statistical analyses are do...

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Vydáno v:European journal of operational research Ročník 257; číslo 3; s. 845 - 858
Hlavní autoři: Uchoa, Eduardo, Pecin, Diego, Pessoa, Artur, Poggi, Marcus, Vidal, Thibaut, Subramanian, Anand
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 16.03.2017
Elsevier Sequoia S.A
Témata:
Algorithms
Benchmark instances
Benchmarks
Experimental analysis of algorithms
Heuristic
Routing
Statistical analysis
Studies
Routing
Experimental analysis of algorithms
Benchmark instances
ISSN:0377-2217, 1872-6860
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Shrnutí:•We show the limitations of existing Capacitated Vehicle Routing Problem instances.•We propose 100 new instances and evaluate recent exact and heuristic methods.•The same generating scheme is used to create an extended benchmark of 600 instances.•Extensive experiments and statistical analyses are done on the extended benchmark.•We present a sophisticated website containing all existing and new instances. The recent research on the CVRP is being slowed down by the lack of a good set of benchmark instances. The existing sets suffer from at least one of the following drawbacks: (i) became too easy for current algorithms; (ii) are too artificial; (iii) are too homogeneous, not covering the wide range of characteristics found in real applications. We propose a new set of 100 instances ranging from 100 to 1000 customers, designed in order to provide a more comprehensive and balanced experimental setting. Moreover, the same generating scheme was also used to provide an extended benchmark of 600 instances. In addition to having a greater discriminating ability to identify “which algorithm is better”, these new benchmarks should also allow for a deeper statistical analysis of the performance of an algorithm. In particular, they will enable one to investigate how the characteristics of an instance affect its performance. We report such an analysis on state-of-the-art exact and heuristic methods.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2016.08.012