Data in Brief 18 (2018) 1146–1148
Contents lists available at ScienceDirect
Data in Brief journal homepage: www.elsevier.com/locate/dib
Data Article
Description of the datasets for the experiments in the paper “solving the multi-vehicle multi-covering tour problem” Tuan Anh Pham a,b, Minh Hoàng Hà c,d, Xuan Hoai Nguyen e,n a
Military Logistics Research Center, Military Logistics Academy, Vietnam GRD Department, VNG Corporation, Vietnam University of Engineering and Technology, VNU, Vietnam d FPT Technology Research Institute, FPT University, Vietnam e IT R&D Center, Hanoi University, Vietnam b c
a r t i c l e i n f o
abstract
Article history: Received 22 July 2017 Received in revised form 18 January 2018 Accepted 21 March 2018 Available online 27 March 2018
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, mmCTP-o, mm-CTP-wo. We tested our algorithm on these data and results are shown in Pham et al. (2017) [4]. & 2018 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Specifications Table Subject area
Computer Science
More specific subject area Type of data
Operational Research Numeric contained in text files
n
DOI of original article: https://doi.org/10.1016/j.cor.2017.07.009 Corresponding author. E-mail address:
[email protected] (X.H. Nguyen).
https://doi.org/10.1016/j.dib.2018.03.106 2352-3409/& 2018 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
T.A. Pham et al. / Data in Brief 18 (2018) 1146–1148
How data was acquired Data format Experimental factors Experimental features Data source location Data accessibility
1147
Artificially generated Raw Instances of six CTP variants were generated, which can be used for testing performance of algorithms on CTP problems. 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/
Value of the data
The data is the first problem instances for m-CTP problem solved in the paper. The data is available for download and reuse without any restriction. The data is stochastic and aimed for the problem of m-CTP and the related.
1. Data 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].
2. Experimental design, materials and methods 2.1. Design We considered six variants of the 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]. 2.2. Materials All these datasets were generated from instances kroA100, kroB100, kroC100, kroD100, kroA200, and kroB200 from TSPLIB (http://elib.zib.de/pub/mp-testdata/tsp/tsplib/tsplib.html).
3. Methods To generate the datasets for the experiments in [4], we started with the data instances for the mCTP problems given in [1], but added three more constraints to ensure that the generated data instances conform with the requirements of the mm-CTP problem.
The first constraint is the maximal route length, for which we utilize the method in [2] to generate
data instances that satisfy the constraint. In particular, the maximal route length q is computed by q ¼ β þ ρ, where β ¼ 2 maxi∈V−0 c0;i and ρ ¼ 250; 500 The second constraint, which makes mm-CTP different from m-CTP, is that each vertex wk must be covered at least uk times. To satisfy that we generated uk as follows. Suppose that nbk is the maximal number of nodes in V which can cover wk , we randomly generated an integer uk in the interval from 1 to min (3, nbk ).
1148
T.A. Pham et al. / Data in Brief 18 (2018) 1146–1148
Using the graph transformation method reported in [4], we generated the data instances for mmCTP-o and mm-CTP-wo problems. Consequently, 192 data instances were obtained for the mm-CTP. Each problem data instance is labeled as X−|T | − |V | − |W | − |p| − |q|, where X is the name of the TSPLIB instance and the remaining labels are corresponding problem parameters. Some data instances do not have the q part indicating that the route length are relaxed for these instances. All of this data instances can be downloaded from public repository https://bitbucket.org/pta_vrp/ mmctp/. In this repository, we provided three data instance folders, namely mmctp, mmctp_o, mmctp_wo and with a file (data_format.txt) for describing the format of each data instance file. Instances in folder mmctp can be used for m-CTP-p, m-CTP, mm-CTP-p, mm-CTP variants. Our algorithm was coded in C þ þ and ran on a 2.4-GHz Intel Xeon computer with 10 times. For more details about results, please see our paper [4].
Acknowledgements This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number FWO.102.2013.04. This support is gratefully acknowledged.
Transparency document. Supplementary material Transparency document associated with this article can be found in the online version at http://dx. doi.org/10.1016/j.dib.2018.03.106.
References [1] M.H. Hà, N. Bostel, A. Langevin, L.M. Rousseau, An exact algorithm and a metaheuristic for the multi-vehicle covering tour problem with a constraint on the number of vertices, Eur. J. Oper. Res. 226 (2) (2013) 211–220. [2] N. Jozefowiez, A branch-and-price algorithm for the multivehicle covering tour problem, Networks 3 (2014) 160–168. [3] M. Gendreau, G. Laporte, F. Semet, The covering tour problem, Oper. Res. 45 (1997) 568–576. [4] T.A. Pham, M.H. Hà, X.H. Nguyen, Solving the multi-vehicle multi-covering tour problem, Comput. Oper. Res. (2017).
