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Efficient algorithms for scheduling equal-length jobs with processing set restrictions on uniform parallel batch

Shuguang Li1

  • 1School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264005, China.

Mathematical Biosciences and Engineering : MBE
|September 20, 2022
PubMed
Summary

This study presents efficient algorithms for scheduling equal-length jobs on parallel batch machines. Exact algorithms are developed for minimizing makespan with unequal release times and for various objectives with equal release times.

Keywords:
equal job lengthsexact algorithmsprocessing set restrictionsschedulinguniform parallel batch machines

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Area of Science:

  • Operations Research
  • Computer Science

Background:

  • Job scheduling on parallel batch machines is a complex optimization problem.
  • Uniform parallel batch machines with non-identical capacities present unique challenges.
  • Job processing set restrictions add further complexity to scheduling.

Purpose of the Study:

  • To develop efficient exact algorithms for job scheduling problems.
  • To address scheduling on uniform parallel batch machines with non-identical capacities and restricted processing sets.
  • To minimize makespan and other objective functions under various release time scenarios.

Main Methods:

  • Development of exact algorithms for specific scheduling scenarios.
  • Analysis of scheduling problems with equal and unequal release times.
  • Consideration of machine capacities and job processing set constraints.

Main Results:

  • Efficient exact algorithms are provided for scheduling jobs with equal lengths on uniform parallel batch machines.
  • Algorithms effectively handle non-identical machine capacities and restricted job processing sets.
  • Optimal solutions are found for minimizing makespan with unequal release times and for various objectives with equal release times.

Conclusions:

  • The proposed algorithms offer efficient and exact solutions for complex job scheduling problems.
  • The research contributes to the field of scheduling theory and practice.
  • The findings are applicable to real-world scenarios requiring optimized batch machine scheduling.