Abstract
This data article contains data related to the research article entitled, “Solving the multi-vehicle multi-covering tour problem” (Pham et al., 2017) [4]. All data of this article was generated from instances kroA100, kroB100, kroC100, kroD100, kroA200, and kroB200 from TSPLIB. It can be downloaded from public repository. This data can be used as benchmarks for the covering tour problem (CTP) variants, such as m-CTP-p, m-CTP, mm-CTP-p, mm-CTP, mm-CTP-o, mm-CTP-wo. We tested our algorithm on these data and results are shown in Pham et al. (2017) [4].
Highlights
This data article contains data related to the research article entitled, “Solving the multi-vehicle multi-covering tour problem” (Pham et al, 2017) [4]
We considered six variants of the covering tour problem (CTP) problem, which are m-CTP, m-CTP-p, mm-CTP, mm-CTP-p, mm-CTP-o, mm-CTP-wo
Our objective is to investigate the performance of algorithms on these variants, especially on instances of mm-CTP, mm-CTP-p, mm-CTP-o, mm-CTP-wo as described in [4]
Summary
This data article contains data related to the research article entitled, “Solving the multi-vehicle multi-covering tour problem” (Pham et al, 2017) [4]. All data of this article was generated from instances kroA100, kroB100, kroC100, kroD100, kroA200, and kroB200 from TSPLIB. It can be downloaded from public repository. We ran each algorithm 10 times for each data instance and measure average performance (accuracy) and running time Available via Internet All data can be downloaded from: https://bitbucket.org/pta_vrp/ mmctp/. This article provides datasets for six CTP variants, which are available for download from our repository (https://bitbucket.org/pta_vrp/mmctp/). These data instances can be used as benchmark data for the CTP problems [3]
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