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Related Experiment Video

Updated: Feb 17, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
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Fairness in optimizing bus-crew scheduling process.

Jihui Ma1, Cuiying Song1, Avishai Avi Ceder1

  • 1School of Traffic and Transportation, Beijing Jiaotong University, Beijing, P. R., China.

Plos One
|December 1, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a fair crew scheduling model for bus drivers using a hybrid ant-colony optimization algorithm. The approach optimizes working and idle time fairness, proving efficient for large-scale bus scheduling problems.

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Area of Science:

  • Operations Research
  • Transportation Science
  • Artificial Intelligence

Background:

  • Crew scheduling is complex, with fairness in working and idle times being a critical, yet often overlooked, factor.
  • Existing models may not adequately address the nuanced cost structures and fairness constraints inherent in bus driver scheduling.

Purpose of the Study:

  • To develop and validate a novel model for bus driver crew scheduling (CSP-BD) that explicitly incorporates fairness considerations.
  • To enhance the hybrid ant-colony optimization (HACO) algorithm for improved efficiency and effectiveness in solving CSP-BD.

Main Methods:

  • Proposing a model for crew scheduling for bus drivers (CSP-BD) with special cost structures and fairness constraints.
  • Developing a hybrid ant-colony optimization (HACO) algorithm, enhanced with a Gamma heuristic function and specific selection rules.
  • Analyzing cost relationships and algorithm performance using data from ten Beijing Public Transport Holdings bus lines.

Main Results:

  • Demonstrated a valid approach for CSP-BD considering fairness in working and idle times.
  • Identified indirect relationships between unfair cost and common, fixed, and extra costs.
  • Showcased the HACO algorithm's efficiency, with tested bus lines completed in under 30 minutes.

Conclusions:

  • The proposed HACO-based algorithm is a feasible and efficient optimization technique for bus driver crew scheduling.
  • The model effectively addresses fairness constraints, offering a practical solution for large-scale transportation operations.
  • Fairness considerations can be integrated into crew scheduling without compromising overall efficiency.