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Related Experiment Videos

Evolutionary driver scheduling with relief chains.

R S Kwan1, A S Kwan, A Wren

  • 1School of Computing, University of Leeds, Leeds, LS2 9JT, UK. rsk@comp.leeds.ac.uk

Evolutionary Computation
|November 16, 2001
PubMed
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This study introduces a hybrid genetic algorithm (GA) for public transport driver scheduling, optimizing shift selection. The novel approach yields superior or comparable results to existing methods, even for large-scale problems.

Area of Science:

  • Operations Research
  • Computer Science
  • Transportation Science

Background:

  • Public transport driver scheduling is a complex, NP-hard problem.
  • Existing mathematical methods offer some solutions but have limitations.
  • Optimization in transport scheduling remains an active research area.

Purpose of the Study:

  • To present a novel hybrid approach for public transport driver scheduling.
  • To improve the efficiency and effectiveness of driver scheduling solutions.
  • To address limitations of current scheduling methodologies.

Main Methods:

  • A hybrid approach combining a genetic algorithm (GA) with a greedy heuristic.
  • Utilizing GA to identify optimal 'relief chains' of shifts.
  • Seeding the greedy heuristic with GA-derived shift selections for schedule construction.

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Main Results:

  • The hybrid GA approach demonstrated superior performance compared to experienced schedulers.
  • Solutions were comparable to those obtained via integer linear programming (ILP).
  • The method successfully found solutions for large-scale instances where ILP failed within practical limits.

Conclusions:

  • The hybrid GA offers a powerful and efficient method for complex driver scheduling.
  • This approach provides a viable alternative to traditional optimization techniques.
  • The findings suggest significant potential for improving public transport operations.