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Research on solving multi-preference intelligent scheduling problem based on improved genetic algorithm
- 1 NanTong University
- 2 NanTong University
- 3 NanTong University
- 4 NanTong University
- 5 NanTong University
- 6 NanTong University
- 7 NanTong University
- 8 Guanghua Cambridge international school
- 9 Nantong University
* Author to whom correspondence should be addressed.
Abstract
The paper will use the improved genetic algorithm that adds Gaussian perturbation based on the standard deviation of the population fitness and increases the variance probability operation to optimize the traditional genetic algorithm, and after iterating until the optimal scheduling strategy is found, we combine this algorithm with a mathematical model, and adopt a variety of variations to improve the efficiency of the algorithm. Among them, we take into account the customer flow, area of the store, employee work preference and other related factors to maximize its adaptability. We use real store employee data for simulation example experimental evidence, and compared with other algorithms, the results show that the study of the scheduling optimization ideas and algorithms are practical and feasible.
Keywords
Employee preferences, intelligent scheduling, genetic algorithms
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Cite this article
Wang,R.;Cao,Z.;Chen,X.;Wan,C.;Yuan,Z.;Shi,L.;Yang,W.;Cao,Z.;Wang,H. (2024). Research on solving multi-preference intelligent scheduling problem based on improved genetic algorithm. Theoretical and Natural Science,56,1-11.
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Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling
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