机坪货运保障人员配置计划优化方法

毕军; 回晶; 成沛璇; 王永兴

doi:10.19818/j.cnki.1671-1637.2025.04.018

机坪货运保障人员配置计划优化方法

doi: 10.19818/j.cnki.1671-1637.2025.04.018

毕军^{1, 2, ,},
回晶¹,
成沛璇¹,
王永兴¹

1.
北京交通大学交通运输学院，北京 100044
2.
北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室，北京 100044

基金项目:

国家自然科学基金项目 72171019

详细信息

作者简介:
毕军(1973-)，男，山东济宁人，北京交通大学教授，工学博士，从事大数据智能交通决策优化研究

通讯作者:
BI Jun (1973-), male, professor, PhD, jbi@bjtu.edu.cn

中图分类号: U695
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- 文章访问数: 445
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- 被引次数: 0
出版历程
- 收稿日期: 2024-11-20
- 录用日期: 2025-04-02
- 修回日期: 2025-02-05
- 刊出日期: 2025-08-28

Optimization method of personnel allocation plan for apron cargo support

1.
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2.
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China

Funds:

National Natural Science Foundation of China 72171019

Article Text (Baidu Translation)

摘要

摘要: 为了提高航空货运保障效率，优化机坪货运保障人员的配置方案，确保航空货运的高效运转与时效性，提出了一种兼顾人力资源优化与任务均衡性的科学优化方法；基于航空货运业务的需求特性，设计了机坪货运保障人员的编组构成；在充分考虑各项机坪货运保障流程相关约束的基础上，建立以最小化机坪货运保障总人力资源数与机坪货运保障人员任务量均衡为目标的优化模型；结合模型特点，提出了一种改进染色体搜索方向的任务分配优化策略，进而设计改进遗传算法实现对模型的高效求解；基于国内某机场的实际运行数据，设计了数值案例以验证模型和算法的可行性和有效性。研究结果表明：与传统遗传算法相比，设计的改进遗传算法在资源利用率方面有显著提升，所求解的机坪货运保障人力资源总数由44人减少至28人，人力资源利用率提高了36.36%；相比于Gurobi求解器直接计算所得结果，改进遗传算法在求解精度上与其保持高度一致，但在求解效率上展现出明显优势，Gurobi求解器的求解时间为112.28 s，而改进遗传算法的求解时间仅为其11.17%。提出的优化方法能够在多任务、多约束的动态环境中减少冗余配置和优化人员任务负荷分布，为优化航空货运保障人员配置问题提供了一种科学、高效的解决方案。
- 航空货运 /
- 机坪货运保障人员 /
- 配置计划 /
- 改进遗传算法 /
- 任务分配优化 /
- 人力资源 /
- 任务均衡
Abstract: To enhance the efficiency of air cargo support operations, optimize the allocation of apron cargo support personnel, and ensure the efficient operation and timeliness of air cargo services, a scientifically optimized method that balances human resource optimization and task distribution was proposed. Based on the demand characteristics of the air cargo business, the composition of the apron cargo support personnel groupings was designed. By fully considering the constraints related to the apron cargo support processes, an optimization model for the apron cargo support personnel allocation plan was built. The objective was to minimize the total number of human resources and balance the task load. In line with the characteristics of the model, a task allocation optimization strategy was proposed to enhance the chromosome search direction, and an improved genetic algorithm was subsequently developed to solve the model efficiently. A numerical case study based on actual operational data from a domestic airport was conducted to verify the feasibility and effectiveness of the proposed model and algorithm. The results show that, compared with the traditional genetic algorithm, the proposed improved genetic algorithm significantly enhances the resource utilization rate. The total number of apron cargo support personnel is reduced from 44 to 28, and the human resource utilization rate is increased by 36.36%. In addition, compared with the results obtained directly from the Gurobi solver, the improved genetic algorithm maintains a high level of accuracy while demonstrating a clear advantage in computational efficiency. The Gurobi solver takes 112.28 s to obtain a solution, whereas the improved genetic algorithm requires only 11.17% of that time. The proposed optimization method can reduce redundant configurations and optimize the distribution of personnel task load in a multi-task and multi-constraint dynamic environment. It provides a scientific and efficient solution for the problem of the allocation of air cargo support personnel.
- air cargo /
- apron cargo support personnel /
- allocation plan /
- improved genetic algorithm /
- task allocation optimization /
- human resource /
- task balancing

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图 1 机坪货运保障人员编组构成

Figure 1. Composition of apron cargo support personnel grouping

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图 2 机坪货运保障人员协同优化模型算法流程

Figure 2. Algorithm process of apron cargo support personnel collaborative optimization model

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图 3 染色体编码

Figure 3. Chromosome coding

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图 4 染色体交叉操作

Figure 4. Chromosome crossing operation

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图 5 装卸任务开始时间

Figure 5. Start time of loading and unloading tasks

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图 6 装卸任务所用时长情况

Figure 6. Time spent on loading and unloading tasks

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图 7 改进遗传算法收敛曲线

Figure 7. Convergence curves of improved genetic algorithm

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图 8 指挥员最优调度方案

Figure 8. Optimal scheduling scheme of commander

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图 9 装卸小组最优调度方案

Figure 9. Optimal scheduling scheme for support groups

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图 10 两种算法性能对比

Figure 10. Performance comparison of two algorithms

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图 11 Gurobi求解指挥员最优调度方案

Figure 11. Gurobi solves optimal scheduling scheme of commanders

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图 12 Gurobi求解装卸小组最优调度方案

Figure 12. Gurobi solves optimal scheduling scheme of support groups

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图 13 不同权重系数分配结果

Figure 13. Assignment results of different weight coefficient

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图 14 在λ₁=1.0、λ₂=0时指挥员最优调度方案

Figure 14. Commanders' optimal scheduling scheme under conditions of λ₁=1.0 and λ₂=0

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图 15 在λ₁=1.0、λ₂=0时装卸小组最优调度方案

Figure 15. Optimal scheduling scheme for support groups under conditions of λ₁=1.0 and λ₂=0

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图 16 在λ₁=0、λ₂=1.0时指挥员最优调度方案

Figure 16. Commanders' optimal scheduling scheme under conditions of λ₁=0 and λ₂=1.0

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图 17 在λ₁=0、λ₂=1.0时装卸小组最优调度方案

Figure 17. Optimal scheduling scheme for support groups under conditions of λ₁=0 and λ₂=1.0

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表 1 部分航班基本信息

Table 1. Basic information of some flights

航班编号	进港航班实际落地时间	进港航班总卸货质量/kg	出港航班计划起飞时间	停机位	出港航班总装机质量/kg	装卸可用时间/min
1	8:10	2 980	8:45	321	2 869	30
2	8:25	720	9:00	313	914	30
3	8:35	1 850	9:10	381	678	30
4	8:40	2 030	9:15	325	550	30
5	8:45	1 272	9:20	403	520	30
6	8:45	570	9:20	331	164	30
7	8:55	543	9:30	401	607	30
8	9:05	2 693	9:40	336	505	30
9	9:10	408	9:45	417	280	30
10	9:15	2 366	9:50	342	1 024	30

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表 2 遗传算法参数设置

Table 2. Setting of genetic algorithm parameters

染色体数目	最大迭代数	交叉概率	变异概率	预估指挥员数量	预估装卸小组数量	λ₁	λ₂
20	100	0.8	0.05	10	20	0.5	0.5

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表 3 Gurobi求解器与改进遗传算法求解结果

Table 3. Results of Gurobi solver and improved genetic algorithm

任务规模	Gurobi求解器			改进遗传算法
任务规模	P₁	P₂	t/s	P₁	P₂	t/s
204	28	6.28	112.28	28	6.28	12.55

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