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This study introduces a bi-objective flexible job-shop scheduling model for discrete manufacturing, optimizing tool wear and tardiness. A novel algorithm enhances machining process decisions, outperforming traditional methods.

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Area of Science:

  • Operations Research
  • Manufacturing Engineering
  • Computational Intelligence

Background:

  • Traditional scheduling often treats resources independently, neglecting interdependencies.
  • Flexible job-shop scheduling problems (FJSP) are complex due to routing, sequencing, and resource constraints.
  • Finite tool capacity and tool wear are critical factors in discrete manufacturing optimization.

Purpose of the Study:

  • To develop a "global" optimization approach for machining processes in discrete manufacturing.
  • To propose a bi-objective flexible job-shop scheduling problem (FJSP) model incorporating tool allocation.
  • To address the strong coupling between machine routing, operation sequencing, and finite tool capacity.

Main Methods:

  • A mixed-integer programming (MIP) model was constructed to minimize tool wear cost and weighted sum of tardiness.
  • Sophisticated constraints were integrated, including tool magazine capacity, variant job releasing times, and machine/tool compatibility.
  • A knowledge-driven teaching-learning-based optimization (TLBO) algorithm with specialized strategies was designed to handle computational challenges and discrete solution spaces.

Main Results:

  • The proposed TLBO algorithm demonstrated superior performance over traditional meta-heuristics in solution quality, spread, and overall metrics.
  • Simulation experiments confirmed the algorithm's effectiveness in addressing complex constraints and preventing premature convergence.
  • The multi-objective collaborative optimization method yielded better processing decisions compared to sequential scheduling approaches.

Conclusions:

  • The developed bi-objective FJSP model and TLBO algorithm offer a robust solution for optimizing machining processes in discrete manufacturing.
  • The study highlights the importance of considering resource interdependencies, particularly tool capacity and wear, for effective scheduling.
  • The proposed approach provides a significant advancement in achieving "global" optimization for complex manufacturing environments.