Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4960031 | European Journal of Operational Research | 2017 | 32 Pages |
Abstract
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.
Keywords
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Eduardo Uchoa, Diego Pecin, Artur Pessoa, Marcus Poggi, Thibaut Vidal, Anand Subramanian,