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New improved hybrid genetic algorithm for optimizing facility layout design of reconfigurable manufacturing system
Xiaoxiao Wei1, Jiafan Sun1, Haojin Jiao2
1School of Modern Post, Xi 'an University of Posts and Telecommunications, Xi 'an, 710061, China.
Scientific Reports
|July 2, 2025
Summary
A novel hybrid genetic algorithm improves equipment layout design for reconfigurable manufacturing systems. This method enhances accuracy and efficiency for complex, NP-hard problems.
Area of Science:
- Industrial Engineering
- Operations Research
- Computer Science
Background:
- Equipment layout design in reconfigurable manufacturing systems is a complex, NP-hard problem.
- Existing algorithms struggle with the increasing dimensionality and complexity of these systems.
Purpose of the Study:
- To propose a new, improved hybrid genetic algorithm for optimizing equipment layout design.
- To enhance the efficiency and accuracy of solving NP-hard layout design problems.
Main Methods:
- Utilizing a chaos genetic algorithm with an improved Tent map for initial population enhancement.
- Applying association rule theory to identify dominant blocks and combine artificial chromosomes.
- Incorporating matched crossover, mutation operations, and adaptive chaotic perturbation.
Main Results:
- The proposed hybrid genetic algorithm demonstrates superior performance compared to traditional methods.
- Experimental results show significant improvements in both accuracy and efficiency.
Conclusions:
- The developed algorithm effectively addresses the complexity of reconfigurable manufacturing system layout design.
- This approach offers a more accurate and efficient solution for NP-hard optimization problems in manufacturing.
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