A benchmark dataset for the multiple depot vehicle scheduling problem

Sarang Kulkarni; Mohan Krishnamoorthy; Abhiram Ranade; Andreas T Ernst; Rahul Patil

doi:10.1016/j.dib.2018.12.055

A benchmark dataset for the multiple depot vehicle scheduling problem

Data Brief. 2018 Dec 18:22:484-487. doi: 10.1016/j.dib.2018.12.055. eCollection 2019 Feb.

Authors

Sarang Kulkarni^{1

2

3}, Mohan Krishnamoorthy^{4

5}, Abhiram Ranade⁶, Andreas T Ernst³, Rahul Patil²

Affiliations

¹ IITB-Monash Research Academy, IIT Bombay, Powai, Mumbai 400076, India.
² SJM School of Management, IIT Bombay, Powai, Mumbai 400076, India.
³ School of Mathematical Sciences, Monash University, Clayton, VIC 3800, Australia.
⁴ Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia.
⁵ School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Australia.
⁶ Department of Computer Science and Engineering, IIT Bombay, Powai, Mumbai 400076, India.

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.