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Solving multi-scenario hybrid flow shop scheduling problem based on an improved probe machine model.

Xiang Tian1, Yang Kong1, Xiyu Liu2,3

  • 1School of Health Management, Binzhou Medical University, Yantai, Shandong, China.

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This study introduces an Improved Probe Machine with Multi-Level Probe Operations (IPMMPO) for hybrid flow-shop scheduling problems. The new IPMMPO-CP model significantly outperforms existing algorithms and models.

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Area of Science:

  • Operations Research
  • Computer Science
  • Industrial Engineering

Background:

  • The hybrid flow-shop scheduling problem (HFS) is critical in manufacturing and production industries.
  • Probe machines offer advanced computing capabilities for complex optimization tasks.
  • Existing methods struggle with the diverse scenarios of HFS problems.

Purpose of the Study:

  • To develop a novel approach for solving multi-scenario hybrid flow-shop scheduling problems.
  • To introduce an Improved Probe Machine with Multi-Level Probe Operations (IPMMPO) and its constraint programming (CP) model (IPMMPO-CP).
  • To create tailored data and probe libraries for various HFS problem types.

Main Methods:

  • Designed general data and probe libraries for HFS scenarios (identical/unrelated parallel machines, no-wait/standard).
  • Developed tuple sets for constraint programming (CP) modeling as data preprocessing.
  • Proposed the IPMMPO-CP model for multi-scenario HFS problems.

Main Results:

  • The IPMMPO-CP model demonstrated superior performance across a wide range of HFS problem instances.
  • Comparative analysis showed IPMMPO-CP outperforming 9 representative algorithms and 2 recent CP models.
  • The proposed method effectively handles HFS with identical parallel machines, unrelated parallel machines, and no-wait constraints.

Conclusions:

  • The IPMMPO-CP offers a powerful and versatile solution for complex hybrid flow-shop scheduling.
  • The developed data and probe libraries enhance the applicability of probe machines to HFS.
  • This research advances the state-of-the-art in solving multi-scenario HFS problems.