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Single-machine scheduling with periodic maintenance and learning effect.

Hui Wu1, Hongmei Zheng2

  • 1School of Science and Information Science, Qingdao Agricultural University, Qingdao, 266109, China. wuhui0925@163.com.

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|June 8, 2023
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Summary
This summary is machine-generated.

This study addresses single-machine scheduling with maintenance and learning effects to minimize makespan. Exact and heuristic algorithms were developed and tested, showing effective performance for various problem sizes.

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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Computational Optimization

Background:

  • Scheduling problems are crucial in manufacturing and operations.
  • Periodic maintenance and position-based learning significantly impact production efficiency.
  • Minimizing makespan is a key objective for optimizing production schedules.

Purpose of the Study:

  • To develop and evaluate methods for solving a single-machine scheduling problem.
  • To incorporate periodic maintenance and position-based learning effects into scheduling models.
  • To minimize the overall makespan for efficient production.

Main Methods:

  • Formulation of a two-stage binary integer programming model for exact solutions.
  • Development of a branch and bound algorithm with specialized search neighborhoods.
  • Implementation of a hybrid genetic-tabu search algorithm for larger instances.
  • Application of the Taguchi method for parameter tuning of heuristic algorithms.

Main Results:

  • The proposed integer programming model provides exact solutions for small-scale problems.
  • The branch and bound algorithm effectively solves small to medium-scale instances.
  • The hybrid genetic-tabu search algorithm demonstrates strong performance on large-scale problems.
  • Parameter tuning using the Taguchi method enhanced algorithm efficiency.

Conclusions:

  • The developed algorithms offer effective solutions for single-machine scheduling with maintenance and learning.
  • The study provides valuable insights into optimizing production schedules under complex constraints.
  • The findings contribute to the advancement of scheduling theory and practice.