基于多目标狼群算法的机场行李导入系统仿真优化研究

doi:10.16182/j.issn1004731x.joss.23-0437

摘要/Abstract

摘要：

针对民航机场行李导入系统运行过程中旅客行李注入等待时间长、系统能耗高等问题，综合考虑虚拟视窗控制方式、收集带式输送机运行速度、虚拟视窗长度及同时开放值机柜台数量等关键控制参数对机场行李导入系统运行效率的影响，提出一种求解该问题的仿真优化框架。通过分析机场行李导入系统实际运行工况，建立参数化仿真优化模型。以最小化旅客行李注入平均等待时间和系统能耗为优化目标，结合系统设计和运行过程中的实际约束条件，建立该问题的数学模型，并设计了一种多目标自适应并行狼群算法进行求解。该算法针对所提问题特性及经典狼群算法易陷入局部最优和收敛速度慢等不足，提出一种混合整实数单链编码方式，融合反向学习策略生成初始种群，引入自适应游走概率机制和智能行为并行机制，采用局部和全局自适应邻域搜索及启发式保优策略实现狼群算法智能行为搜索，使用Pareto非支配排序进行寻优迭代并获得最优解集。以国内某大型国际航空枢纽机场行李导入系统为例设计不同规模多种算法对比实验，验证了所提方法的有效性和优越性。

关键词: 机场行李导入系统, 关键控制参数, 仿真优化, 多目标自适应并行狼群算法, Pareto非支配排序

Abstract:

Aiming at the problems of long waiting time for passenger baggage import and high system energy consumption during the operation of the baggage import system in civil aviation airports, a simulation optimization framework for solving this problem is proposed by comprehensively considering the influence of key control parameters on the operation efficiency of the baggage import system in airports, including the virtual window control mode, the operation speed of the collection belt conveyor, the length of the virtual window and the number of check-in counters opened at the same time. By analyzing the actual operating conditions of the airport baggage import system, a parametric simulation optimization model is established. To minimize the average waiting time for passenger baggage import and system energy consumption, combined with the actual constraints in the process of system design and operation, a mathematical model of the problem is established, and a multi-objective adaptive parallel wolf pack algorithm is designed to solve it. Aiming at the characteristics of the proposed problem and the shortcomings of the classical wolf pack algorithm, such as easy falling into the local optimum and slowing convergence speed, the algorithm proposes a single chain coding method of mixed integer and real numbers, which combines the opposition-based learning strategy to generate the initial population, introduces the adaptive walk probability mechanism and the parallel mechanism of intelligent behavior, adopts the local and global adaptive neighborhood search and heuristic optimization strategy to realize the intelligent behavior search of the wolf pack algorithm, and employs Pareto non-dominated sorting method for optimization iteration and obtains the optimal solution set. Taking the baggage import system of a large international aviation hub airport in China as an example, the comparative experiments of different scales and algorithms are designed to verify the effectiveness and superiority of the method proposed.

Key words: airport baggage import system, key control parameters, simulation optimization, multi-objective adaptive parallel wolf pack algorithm, Pareto non-dominated sorting method

中图分类号:

TP391.9

陶翼飞,丁小鹏,罗俊斌等 . 基于多目标狼群算法的机场行李导入系统仿真优化研究[J]. 系统仿真学报, 2024, 36(7): 1655-1669.

Tao Yifei,Ding Xiaopeng,Luo Junbin,et al . Simulation Optimization of Airport Baggage Import System Based on Multi-objective Wolf Pack Algorithm[J]. Journal of System Simulation, 2024, 36(7): 1655-1669.

图/表 19

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图9

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图10

图11

图12

表7

熵权TOPSIS法评价计算结果

序号	$S i +$	$S i -$	η_i	排序
1	0.009 7	0.030 1	0.757 0	1
2	0.010 3	0.029 9	0.744 1	2
3	0.013 7	0.028 3	0.674 1	3
4	0.020 9	0.026 5	0.559 5	4
5	0.022 9	0.026 4	0.535 2	5
6	0.024 4	0.026 3	0.518 5	6
7	0.026 2	0.026 2	0.500 0	7

表7

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参数	水平
参数	1	2	3
y	50	60	70
β	1	2	3
α₁	0.7	0.8	0.9
α₂	0.3	0.4	0.5

组合	水平				IGD均值
组合	y	β	α₁	α₂	IGD均值
1	1	1	1	1	1.224
2	1	2	2	2	0.539
3	1	3	3	3	0.695
4	2	1	2	3	0.697
5	2	2	3	1	1.202
6	2	3	1	2	0.500
7	3	1	3	2	0.639
8	3	2	1	3	0.213
9	3	3	2	1	0.150

参数	1	2	3	极差	排序
y	0.819	0.800	0.334	0.485	1
β	0.853	0.651	0.448	0.405	2
α₁	0.646	0.462	0.845	0.383	3
α₂	0.859	0.559	0.535	0.324	4

算法	种群规模	交叉概率	变异概率
NSGA-Ⅱ	70	0.9	0.050 0
MOPSO	70	1.2	0.753 2

规模	NSGA-Ⅱ				MOPSO				MOAPWPA
规模	IGD	SP	NS	RT/s	IGD	SP	NS	RT/s	IGD	SP	NS	RT/s
均值	3.300	49.401	8	82.465	1.119	38.788	6	84.198	1.074	33.386	11	80.334
1 d	1.649	6.574	13	41.328	0.589	5.406	8	43.596	1.148	22.437	15	37.856
2 d	0.344	49.942	6	56.256	0.453	16.986	6	58.394	0.735	20.134	10	54.234
3 d	9.022	27.625	12	71.413	0.000	24.230	5	72.981	0.494	47.945	13	68.951
4 d	10.725	20.133	6	83.165	3.815	80.183	5	85.023	3.192	3.639	11	82.025
5 d	0.557	54.027	4	95.781	1.648	40.170	6	97.287	0.000	94.398	7	94.765
6 d	0.167	125.172	10	108.956	0.193	57.806	5	110.354	1.563	14.848	13	106.986
7 d	0.635	62.336	5	120.354	1.138	46.734	7	121.753	0.385	30.303	8	117.523