Application of Improved Genetic Algorithm Based on Multi-objective Optimization in the Layout of Intelligent Logistics Park
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v6i4.4240
Abstract
This paper constructs a layout model for intelligent logistics parks through literature research, breaking the traditional limitation of focusing only on fixed costs. It innovatively integrates a multi-objective optimization mechanism that considers logistics distribution and operating costs. The fitness function, encoding and decoding schemes, genetic algorithm operators, and parameter configurations are carefully designed. By introducing and improving the genetic algorithm, the planning and layout problems of the park are effectively solved, significantly reducing redundant encoding and greatly improving computational efficiency. The experimental results show that this method can obtain the optimal layout solution in a short time, providing strong support for the scientific planning and efficient operation of intelligent logistics parks, achieving dual optimization of cost and efficiency.
Keywords
Intelligent logistics; Multi-objective optimization; Fitness function; Genetic algorithm
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[2] ZHAO Quanwu, YANG Qian. The location of city professional logistics centers on consideration of CO2 emissions [J]. Chinese Journal of Management Science, 2014, 22(7): 124−130.
[3] LIN D S, ZHANG Z Y, WANG J X, et al. Lowcarbon logistics distribution center location with uncertain demand [J]. Control and Decisions,2020,35(2):492−500.
[4] 100−108. ZHAO Jingyuan, LYU Nan. Layout of logistics park based on improved SLP method [J]. Journal of Chang’an University (Natural Science Edition),2020,40(3): 100−108.
[5] TAO Jinghui, GUO Xiaowei. The Co-location between logistics park and industrial park based on the total cost and carbon emission reduction [J]. Chinese Journal of Management Science,2018,26(12):124−134.
[6] SUN Liucheng, SUN Yan, ZHENG Wenjia. Model and algorithm on the layout problem of functional areas in irregular logistics parks [J]. Journal of Transportation Systems Engineering and Information Technology,2017,17(2):168−175.
[7] SUN W,HUANG C C. Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency [J]. Journal of Cleaner Production,2022,338:130414.
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