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Related Experiment Video

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An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production
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A benchmark dataset for multi-objective flexible job shop cell scheduling.

Derya Deliktaş1,2, Ender Özcan1, Ozden Ustun3

  • 1Computational Optimisation and Learning (COL) Lab, School of Computer Science, University of Nottingham, NG8 1BB, Nottingham, UK.

Data in Brief
|December 28, 2023
PubMed
Summary

A new benchmark dataset for the flexible job shop cell scheduling problem (FJCS-SDFSTs-ITTs) is introduced. This dataset aids in evaluating multi-objective evolutionary algorithms for complex manufacturing scheduling challenges.

Keywords:
Cell schedulingExceptional and reentrant partsFlexible job shop schedulingIntercellular transportation timesMulti-objective modelSequence-dependent family setup times

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Area of Science:

  • Operations Research
  • Manufacturing Engineering
  • Computational Intelligence

Background:

  • The flexible job shop scheduling problem (FJCS) is complex, especially in cellular manufacturing.
  • Existing research often simplifies aspects like intercellular moves and sequence-dependent setup times.
  • There's a need for comprehensive datasets to test advanced scheduling algorithms.

Purpose of the Study:

  • To introduce a novel benchmark dataset for the multi-objective flexible job shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation times (FJCS-SDFSTs-ITTs).
  • To provide a standardized resource for evaluating and comparing multi-objective evolutionary algorithms (MOEAs) for this specific problem.
  • To facilitate research in optimizing makespan and total tardiness simultaneously in cellular manufacturing.

Main Methods:

  • Development of a comprehensive benchmark dataset.
  • Inclusion of realistic manufacturing constraints: intercellular moves, exceptional parts, sequence-dependent family setup times, and intercellular transportation times.
  • Generation of 43 instances ranging from small to large, including a real-world case.

Main Results:

  • A dataset containing 43 benchmark instances for the FJCS-SDFSTs-ITTs problem is now available.
  • The dataset covers a range of problem sizes, from small to large-scale instances.
  • A large, real-world problem instance is included for practical relevance.

Conclusions:

  • The presented dataset serves as a valuable resource for the research community.
  • It enables rigorous evaluation and comparison of multi-objective evolutionary algorithms for FJCS-SDFSTs-ITTs.
  • Future research can leverage this dataset to advance the field of cellular manufacturing scheduling.