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A benchmark dataset for the multiple depot vehicle scheduling problem.

Sarang Kulkarni1,2,3, Mohan Krishnamoorthy4,5, Abhiram Ranade6

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This data article introduces a benchmark dataset for the multiple depot vehicle scheduling problem (MDVSP). The dataset aids researchers in evaluating and comparing algorithms for optimizing vehicle assignments and minimizing travel costs.

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Area of Science:

  • Operations Research
  • Transportation Science
  • Combinatorial Optimization

Background:

  • The multiple depot vehicle scheduling problem (MDVSP) involves assigning vehicles from various depots to scheduled trips.
  • The primary objective is to minimize total costs associated with empty travel and waiting times.
  • Existing research often requires standardized datasets for performance evaluation.

Purpose of the Study:

  • To present a comprehensive benchmark dataset for the MDVSP.
  • To facilitate the evaluation of new and existing MDVSP heuristics and algorithms.
  • To provide a tool for generating novel MDVSP problem instances.

Main Methods:

  • Development of a benchmark dataset comprising 60 problem instances of the MDVSP.
  • Inclusion of a program for generating new, scalable MDVSP problem instances.
  • Dataset designed to align with established MDVSP formulations and heuristics.

Main Results:

  • A structured dataset is now available for the MDVSP.
  • The dataset enables comparative analysis of different algorithmic approaches.
  • The accompanying program allows for flexible instance generation.

Conclusions:

  • The introduced dataset serves as a valuable resource for the MDVSP research community.
  • It promotes reproducible research and facilitates advancements in vehicle scheduling optimization.
  • The dataset supports the development and benchmarking of efficient MDVSP algorithms.