Simulation Optimization of Airport Baggage Import System Based on Multi-objective Wolf Pack Algorithm

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

Abstract

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

CLC Number:

TP391.9

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

Figures/Tables 19

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Table 7

Entropy weight TOPSIS method to evaluate calculation results

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

Table 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