What question did this study set out to answer?

This research aims to develop a new algorithm, t-MOHHO, to enhance scheduling efficiency while balancing productivity, cost, and reliability.

April 24, 2026ProcessesOpen Access

t-MOHHO: An Adaptive Multi-Objective Harris Hawks Optimization Algorithm for Flexible Job Shop Scheduling

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Authors

JSJunlin SuGuangdong Ocean University SMShuai MENGGuangdong Ocean University ZLZhihao LuoChongqing University of Posts and Telecommunications

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Implication

Collaboration optimization balances cost and punctuality in flexible job shop scheduling, suggesting improved decision-making for smart manufacturing.

Key Points

This research aims to develop a new algorithm, t-MOHHO, to enhance scheduling efficiency while balancing productivity, cost, and reliability.
Introduced t-MOHHO algorithm which uses a Student’s t-distribution mutation operator.
Implemented a non-linear energy escape mechanism to explore search spaces effectively.
Validated the algorithm on 10 MK benchmark instances to assess its performance against classic optimization methods.
t-MOHHO achieves a 56.2% reduction in total tardiness compared to the state-of-the-art NSGA-III.
Processing costs are lowered by 3.4% compared to NSGA-III.
Demonstrated significant advantages over classic algorithms like HHO and MOPSO across various metrics.

Cite This Study

Su et al. (2026) studied this question.

synapsesocial.com/papers/69eb099a553a5433e34b3fe3 https://doi.org/10.3390/pr14091338

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Implication

Collaboration optimization balances cost and punctuality in flexible job shop scheduling, suggesting improved decision-making for smart manufacturing.

t-MOHHO: An Adaptive Multi-Objective Harris Hawks Optimization Algorithm for Flexible Job Shop Scheduling

Discussion

Member takes

Implication

Key Points

Cite This Study

t-MOHHO: An Adaptive Multi-Objective Harris Hawks Optimization Algorithm for Flexible Job Shop Scheduling

Discussion

Member takes

Implication

Key Points

Cite This Study

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