Research On Airport Baggage Import System Optimization Based On Adaptive Parallel Wolf Pack Algorithm

As the input part of the airport baggage handling system,the service efficiency of the airport baggage import system(BIS)will directly affect the operation efficiency of its front-end check-in system,back-end sorting system and baggage handling system,and its service efficiency is closely related to key control parameters such as 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.Therefore,taking the airport BIS as the research object,comprehensively considering the influence of the above four key control parameters on the service efficiency of the airport BIS,and minimizing the performance indicators such as average waiting time of passenger baggage import and system energy consumption as the optimization goal,this paper studies the optimization problems of single-objective and multi-objective airport BIS respectively,and the main research content is as follows.Firstly,aiming at the problem of long waiting time for passenger baggage import during the operation of airport BIS,in order to minimize the optimization goal of the average waiting time of passenger baggage import,combined with the actual constraints in the process of system design and operation,a mathematical model of this problem is established.And a simulation optimization framework to solve this problem is proposed.And an 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 that are easy to fall into local optimum and slow convergence speed,this algorithm proposes a single-stranded coding method of mixed integer and real numbers,which combines the opposition-based learning strategy to generate the initial population,and introduces the walk probability mechanism and the parallel mechanism of intelligent behavior,and adopts the local and global adaptive neighborhood search and heuristic optimization strategy to realize the intelligent behavior search of the wolf pack algorithm.Taking the BIS of a large international aviation hub airport in China as an example,the comparative experiments of different scales and algorithms are designed,which verifies that the adaptive parallel wolf pack algorithm has stronger search performance and optimization efficiency.Secondly,based on the research on the optimization problem of single-objective airport BIS,considering the importance of energy saving and consumption reduction,taking minimizing the average waiting time of passenger baggage import and system energy consumption as the optimization goal,a simulation optimization model of multiobjective airport BIS is established,and a multi-objective adaptive parallel wolf pack algorithm is designed to solve it.Based on the adaptive parallel wolf pack algorithm proposed in this paper,the algorithm combines the theories and methods of multiobjective optimization and congestion distance calculation,and Pareto non-dominated sorting method is used to perform optimization iteration and obtain the optimal solution set,which provides a variety of optimization schemes for decision makers.And an entropy-weighted TOPSIS method is introduced to select the best of the Pareto frontier solution set obtained from the algorithm search to assist the dispatcher to make better decisions on the scheduling task.Finally,by designing different scale examples and comparing it with the widely used multi-objective optimization algorithm in the field of logistics scheduling,it is verified that the multi-objective adaptive parallel wolf pack algorithm has better convergence,diversity and stability.Finally,on the basis of the above research results,based on the B/S system architecture,using the Flask development framework,a set of airport BIS control parameter optimization system is designed and developed by Python language,and the system is tested and verified.In summary,this paper establishes single-objective and multi-objective simulation optimization models and mathematical models for airport BIS optimization problems,proposes a simulation optimization framework for solving such problems,designs and improves wolf pack algorithms to solve them,and designs comparison experiments to verify the comprehensive performance of the algorithm with actual airport data.The experimental results show that the proposed method can effectively solve the airport BIS optimization problem.