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Related Experiment Video

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Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model

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Published on: August 29, 2013

Constructive-destructive neighbor search drives artificial bee colony algorithm for variable speed green hybrid

Danying Hu^1,2, Yali Wu^3,4, Lipeng Qiu⁵

¹Department of Information and Control Engineering, Xi'an University of Technology, Xi'an, 710048, China.

Scientific Reports

|March 21, 2025

Summary

Researchers developed a new algorithm for the variable speed hybrid flowshop scheduling problem. This method optimizes makespan and energy consumption, outperforming existing approaches in most test cases.

Keywords:

Decomposition Green scheduling Hybrid flowshop Multi-objective ABC algorithm

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Area of Science:

Operations Research
Artificial Intelligence
Manufacturing Systems

Background:

The hybrid flowshop scheduling problem (HFSP) is a complex, NP-hard problem with significant research interest.
Real-world manufacturing involves machines operating at variable speeds, adding complexity to scheduling problems.

Purpose of the Study:

To address the variable speed hybrid flowshop scheduling problem (VSHFSP).
To minimize both makespan and total energy consumption in green manufacturing environments.

Main Methods:

Proposed a novel speed optimization strategy for encoding and decoding.
Designed a constructive-destructive search driven artificial bee colony (CDSABC) algorithm.
Integrated specific operators: Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS), binary tournament selection, and global updates.

Main Results:

The CDSABC algorithm demonstrated superior performance across various metrics (best, worst, average, standard deviation of IGD index).
Outperformed other algorithms in 80% of the evaluated test cases.
Effectively balances makespan reduction and energy efficiency.

Conclusions:

The developed CDSABC algorithm is effective for solving the VSHFSP.
The approach provides a viable solution for green manufacturing by optimizing energy consumption and production time.
The method offers a robust framework for complex scheduling challenges.