Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns.

Area of Science:

• Operations Research
• Industrial Engineering
• Manufacturing Systems

Background:

• Machine breakdowns are common in flow shop scheduling, impacting efficiency.
• Existing research often overlooks the critical aspects of robustness and stability in scheduling solutions.
• Blocking Lot-Streaming Flow Shop (BLSFS) problems with machine breakdowns remain understudied.

Purpose of the Study:

• To develop a multiobjective model for BLSFS scheduling problems that incorporates robustness and stability criteria.
• To address the gap in research concerning machine breakdowns in BLSFS scheduling.
• To create a scheduling solution that is practical and resilient to environmental changes.

Main Methods:

• A novel multiobjective model was formulated, integrating robustness and stability.
• An evolutionary multiobjective robust scheduling algorithm was developed.
• The algorithm incorporates heuristic-based initialization, novel crossover operators, and a local search-based rescheduling strategy.

Main Results:

• The proposed algorithm was tested on 22 datasets and compared against state-of-the-art methods.
• Empirical results demonstrate the algorithm's effectiveness in handling machine breakdowns.
• The algorithm successfully generates scheduling strategies that are both robust and stable.

Conclusions:

• The presented evolutionary algorithm effectively addresses BLSFS scheduling problems with machine breakdowns.
• The developed approach provides robust and stable scheduling solutions, enhancing practical applicability.
• This research offers a significant advancement in scheduling under unreliable manufacturing conditions.