A benchmark dataset for the multiple depot vehicle scheduling problem

Sarang Kulkarni, Mohan Krishnamoorthy, Abhiram Ranade, Andreas T. Ernst, Rahul Patil

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This data article presents a description of a benchmark dataset for the multiple depot vehicle scheduling problem (MDVSP). The MDVSP is to assign vehicles from different depots to timetabled trips to minimize the total cost of empty travel and waiting. The dataset has been developed to evaluate the heuristics of the MDVSP that are presented in “A new formulation and a column generation-based heuristic for the multiple depot vehicle scheduling problem” (Kulkarni et al., 2018). The dataset contains 60 problem instances of varying size. Researchers can use the dataset to evaluate the future algorithms for the MDVSP and compare the performance with the existing algorithms. The dataset includes a program that can be used to generate new problem instances of the MDVSP.

Original languageEnglish
Pages (from-to)484-487
Number of pages4
JournalData in Brief
Volume22
DOIs
Publication statusPublished - 1 Feb 2019

Cite this

Kulkarni, Sarang ; Krishnamoorthy, Mohan ; Ranade, Abhiram ; Ernst, Andreas T. ; Patil, Rahul. / A benchmark dataset for the multiple depot vehicle scheduling problem. In: Data in Brief. 2019 ; Vol. 22. pp. 484-487.
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A benchmark dataset for the multiple depot vehicle scheduling problem. / Kulkarni, Sarang; Krishnamoorthy, Mohan; Ranade, Abhiram; Ernst, Andreas T.; Patil, Rahul.

In: Data in Brief, Vol. 22, 01.02.2019, p. 484-487.

Research output: Contribution to journalArticleResearchpeer-review

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