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Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with

Lei Yue1, Zailin Guan1, Ullah Saif2

  • 1State Key Lab of Digital Manufacturing Equipment and Technology, HUST-SANY Joint Lab of Advanced Manufacturing, Huazhong University of Science and Technology, Wuhan, 430074 People's Republic of China.

Springerplus
|September 22, 2016
PubMed
Summary

This study introduces a novel hybrid algorithm for group scheduling problems with sequence-dependent setup times and learning effects. The proposed method effectively optimizes makespan and weighted tardiness, outperforming existing algorithms.

Keywords:
Group schedulingHybrid Pareto artificial bee colony algorithmLearning effectMulti-objectivesSequence dependent setupTaguchi method

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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Artificial Intelligence

Background:

  • Group scheduling is crucial for efficient production systems but faces challenges with sequence-dependent setup times.
  • Minimizing makespan and total weighted tardiness are key objectives in production scheduling.
  • The impact of learning effects on job processing times is often overlooked in scheduling research.

Purpose of the Study:

  • To address the integrated problem of sequence-dependent group scheduling with learning effects on a single machine.
  • To develop a novel hybrid Pareto artificial bee colony algorithm (HPABC) for solving this complex scheduling problem.
  • To evaluate the performance of the proposed HPABC against established multi-objective optimization algorithms.

Main Methods:

  • A hybrid Pareto artificial bee colony algorithm (HPABC) incorporating genetic algorithm principles was developed.
  • The Taguchi method was employed to optimize the parameters of the HPABC for different problem sizes.
  • The HPABC's performance was benchmarked against SPEA2, NSGAII, and PSO using various test problem instances.

Main Results:

  • The HPABC algorithm demonstrated superior performance compared to SPEA2, NSGAII, and PSO.
  • HPABC yielded better quality and diversity in Pareto optimal solutions across various problem sizes.
  • The Taguchi method effectively tuned HPABC parameters for enhanced performance.

Conclusions:

  • The proposed HPABC is a highly effective approach for solving single-machine group scheduling problems with sequence-dependent setup times and learning effects.
  • HPABC provides a significant improvement in finding high-quality Pareto optimal solutions.
  • This research contributes a novel and efficient algorithm for a rarely considered complex scheduling scenario.