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Solving flexible job shop scheduling problems with transportation time based on improved genetic algorithm.

Guo Hui Zhang1, Jing He Sun1, Xing Liu1

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This study introduces transportation time into the flexible job shop scheduling problem (FJSP), developing a model and algorithm to minimize completion time. Results demonstrate the approach

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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Manufacturing Systems

Background:

  • In practical production, job transportation between machines is common but often overlooked in scheduling literature.
  • Transportation time can significantly impact product quality, particularly in industries like steelmaking.
  • Existing flexible job shop scheduling problem (FJSP) models typically neglect transportation durations.

Purpose of the Study:

  • To incorporate transportation time as an independent factor alongside processing time in the FJSP.
  • To develop a mathematical model for the FJSP that minimizes the makespan (maximum completion time).
  • To propose an effective algorithm for solving the complex, NP-hard FJSP with transportation times.

Main Methods:

  • Formulation of a mathematical model for the flexible job shop scheduling problem with transportation time.
  • Development of an improved genetic algorithm (GA) to address the NP-hard nature of the problem.
  • Introduction of an operation left shift insertion method for chromosome decoding to generate active schedules.

Main Results:

  • The proposed mathematical model accurately represents the FJSP considering both processing and transportation times.
  • The improved GA effectively solves the formulated problem, yielding optimal or near-optimal scheduling solutions.
  • Validation using a real-world instance in MATLAB confirms the algorithm's validity and feasibility.

Conclusions:

  • Integrating transportation time into FJSP models is crucial for realistic production scheduling.
  • The developed mathematical model and improved GA provide a viable solution for minimizing makespan.
  • The findings offer practical guidance for optimizing production processes in industries affected by transportation delays.