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Summary
This summary is machine-generated.

This study introduces a novel discrete-time production scheduling model for continuous processes using mixed-integer linear programming (MILP). The new model reformulates time grids for improved computational efficiency and solution quality in scheduling.

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Area of Science:

  • Operations Research
  • Chemical Engineering
  • Industrial Engineering

Background:

  • Mixed-integer linear programming (MILP) is essential for production scheduling.
  • Current discrete-time models often focus on batch processes, neglecting continuous ones.
  • Uniformly spaced time grids can increase computational complexity in MILP models.

Purpose of the Study:

  • To develop a discrete-time MILP model for continuous process scheduling.
  • To reformulate existing models to incorporate multiple, nonuniformly spaced time grids.
  • To improve the efficiency and solution quality of production scheduling.

Main Methods:

  • Developed a discrete-time MILP model for continuous processes.
  • Reformulated the model to allow for nonuniform time grids for units, tasks, and materials.
  • Modified subsets, parameters, and constraints to generate unique time grids.
  • Incorporated transient operations into the scheduling model.

Main Results:

  • The proposed reformulation significantly reduces solution time for MILP production scheduling models.
  • Nonuniformly spaced time grids accurately capture key process characteristics.
  • Application in a rolling horizon setting yielded higher-quality solutions.

Conclusions:

  • Discrete-time MILP models for continuous processes benefit from nonuniform time grids.
  • The reformulation enhances computational efficiency and solution quality in production scheduling.
  • This approach offers practical advantages for industrial scheduling applications.