Dataset benchmark job shop scheduling problem

Dataset Overview

Dataset Name

• Name: Dataset benchmark job shop scheduling problem

Dataset Description

• Purpose: Provide benchmark instances for the job shop scheduling problem, aiming to minimize makespan.

Metadata Structure

• File: instances.json
• Fields:
  • name: instance name
  • jobs: number of jobs
  • machines: number of machines
  • optimum: best makespan or null
  • bounds: when optimum is null, includes:
    • upper: makespan upper bound
    • lower: makespan lower bound
  • path: instance file path

Dataset Content

• Number of Instances and Sources:
  • ABZ5-9: 5 instances, from Adams et al. [1]
  • FT06, FT10, FT20: 3 instances, from Fisher and Thompson [2]
  • LA01-40: 40 instances, from Lawrence [3]
  • ORB01-10: 10 instances, from Applegate and Cook [4]
  • SWV01-20: 20 instances, from Storer et al. [5]
  • yn1-4: 4 instances, from Yamada and Nakano [6]
  • ta01-80: 80 instances, from Taillard [7]

References

1. Adams, J., Balas, E., & Zawack, D. (1988). The shifting bottleneck procedure for job shop scheduling. Management Science, 34(3), 391-401.
2. Muth, J.F., & Thompson, G.L. (1963). Industrial scheduling. Englewood Cliffs, NJ: Prentice-Hall.
3. Lawrence, S. (1984). Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement). Graduate School of Industrial Administration, Carnegie-Mellon University.
4. Applegate, D., & Cook, W. (1991). A computational study of job‑shop scheduling. ORSA Journal on Computing, 3(2), 149-156.
5. Storer, R.H., Wu, S.D., & Vaccari, R. (1992). New search spaces for sequencing problems with applications to job‑shop scheduling. Management Science, 38(10), 1495-1509.
6. Yamada, T., & Nakano, R. (1992). A genetic algorithm applicable to large‑scale job‑shop problems. Proceedings of the Second International Workshop on Parallel Problem Solving from Nature (PPSN2), 281-290.
7. Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278-285.