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Multi-objective production scheduling optimization strategy based on fuzzy mathematics theory.
1Zhengzhou Vocational College of Finance and Taxation, Zhengzhou, China.
Plos One
|July 10, 2025
Summary
Fuzzy mathematics enhances production scheduling by introducing new kernel allocation strategies. A hybrid algorithm significantly improves customer satisfaction and accelerates evolutionary equilibrium in scheduling.
Area of Science:
- Operations Research
- Fuzzy Mathematics
- Production Management
Background:
- Multi-objective production scheduling presents challenges like conflicting objectives and uncertainty.
- Traditional optimization algorithms struggle with complexity and ambiguity in scheduling.
- Fuzzy mathematics offers a potential solution to improve scheduling efficiency and optimization.
Purpose of the Study:
- To introduce and evaluate novel kernel allocation strategies for fuzzy mathematical scheduling.
- To analyze the relationship between fuzzy mathematical scheduling solutions and kernel allocation.
- To compare fuzzy scheduling with other existing scheduling approaches.
Main Methods:
- Proposed proportional gain, weighted marginal, and average cost-saving kernel allocation methods.
- Analysis of fuzzy mathematical scheduling solutions and their connection to kernel allocation.
- Comparative study of fuzzy mathematical scheduling with other scheduling paradigms.
Main Results:
- Fuzzy mathematics theory reached equilibrium in 22 generations with a maximum satisfaction of 2.345.
- The proposed hybrid algorithm achieved equilibrium in just 3 generations.
- The hybrid algorithm increased maximum customer satisfaction to 2.445.
Conclusions:
- Novel kernel allocation strategies are effective for fuzzy mathematical scheduling.
- A hybrid algorithm significantly enhances customer satisfaction and speeds up evolutionary equilibrium.
- Fuzzy mathematics, particularly with hybrid approaches, offers a superior solution for complex production scheduling.
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