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A Heuristic-Based Adaptive Iterated Greedy Algorithm for Lot-Streaming Hybrid Flow Shop Scheduling Problem with

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Intermingling sub-lots in lot-streaming hybrid flow shops offers practical advantages. A novel heuristic algorithm (HAIG) significantly improves machine utilization and reduces manufacturing cycle times.

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Area of Science:

  • Operations Research
  • Manufacturing Engineering
  • Industrial Engineering

Background:

  • Traditional lot-streaming flow shops fix sub-lot sequences.
  • This study addresses the practical need for intermingling sub-lots due to varying quantities and processing times.

Purpose of the Study:

  • To investigate the lot-streaming hybrid flow shop scheduling problem with consistent and intermingled sub-lots (LHFSP-CIS).
  • To develop an effective algorithm for optimizing scheduling in such systems.

Main Methods:

  • Formulation of a mixed integer linear programming (MILP) model.
  • Design of a heuristic-based adaptive iterated greedy algorithm (HAIG) with specific modifications.
  • Implementation of a two-layer encoding, embedded heuristics, heuristic-based initialization, adaptive local search, and improved acceptance criteria.

Main Results:

  • The HAIG algorithm demonstrated significant advantages in effectiveness and robustness over five state-of-the-art algorithms, confirmed by Kruskal-Wallis test (p=0).
  • An industrial case study validated the effectiveness of intermingling sub-lots.

Conclusions:

  • Intermingling sub-lots is a practical and effective technique for lot-streaming hybrid flow shops.
  • The proposed HAIG algorithm provides a robust solution for optimizing machine utilization and reducing manufacturing cycle times.