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Due-window assignment scheduling with past-sequence-dependent setup times.

Weiguo Liu1, Xuyin Wang1, Xiaoxiao Wang1

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Summary
This summary is machine-generated.

This study presents a polynomial time algorithm for single-machine scheduling with past-sequence-dependent setup times and due-window assignments. It minimizes weighted earliness, tardiness, and other costs, offering an efficient solution for complex scheduling problems.

Keywords:
due-window assignmentschedulingsetup timessingle-machine

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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Computer Science

Background:

  • The due-window assignment scheduling problem is critical for optimizing production processes.
  • Setup times, particularly past-sequence-dependent ones, add significant complexity to scheduling.
  • Minimizing weighted earliness, tardiness, and job counts is essential for efficiency.

Purpose of the Study:

  • To develop an optimal scheduling algorithm for a single machine with past-sequence-dependent setup times and due-window assignments.
  • To minimize a comprehensive cost function including earliness, tardiness, and due-window parameters.
  • To extend the model to incorporate learning and deterioration effects.

Main Methods:

  • Developed a polynomial time algorithm for optimal job sequencing and due-window determination.
  • Investigated common, slack, and unrestricted due-window assignment methods.
  • Analyzed the problem extension with learning and deterioration effects.

Main Results:

  • An optimal polynomial time algorithm was introduced, providing an efficient solution.
  • The algorithm effectively minimizes the defined cost function under various due-window assignment strategies.
  • The study successfully extended the problem to include learning and deterioration effects.

Conclusions:

  • The proposed algorithm offers an efficient and optimal solution for the complex due-window assignment scheduling problem.
  • The findings are applicable to single-machine environments with sequence-dependent setup times.
  • Further research can build upon the extended model incorporating learning and deterioration.