基于交互式细菌觅食优化算法的飞机基地维修人员调度

Interactive bacterial foraging optimization algorithm for aircraft base maintenance technician scheduling

随着行业细分的逐步深入,航空公司将检修任务外包给基地维修服务提供商,以降低运营成本。针对外包模式下飞机基地维修人员调度问题,本文首先基于维修任务的分布式特征,结合维修服务提供商对成本控制的需求与维修人员对工作量分配公平的需求,构建调度模型。其次,提出与模型约束条件相结合的编码转换机制,基于结构重组思想以及群体内部信息交流方式设计交互式细菌觅食优化算法,并对模型进行求解。最后,基于分布式任务分配特点设计对比实验。实验结果表明,相较于原始细菌觅食优化算法、菌群优化算法、改进细菌觅食优化算法、粒子群算法、综合学习粒子群算法以及遗传算法,交互式细菌觅食优化算法具有更强的寻优能力、更高的搜索精度,以及良好的稳健性与收敛性能,能有效解决手动排班引起的耗时长、费力多等问题,提升飞机维修基地的运营效率。

In recent years,with the reform of industry segmentation,more and more airlines outsource maintenance tasks to aircraft base maintenance service providers to reduce operational costs.As the most important part of aircraft base maintenance service providers,the scheduling scheme of the aircraft maintenance technicians determines whether the maintenance service could be completed on time.However,aircraft maintenance technicians often face a tense and difficult working environment.The shortage of the rest time and huge psychological pressure decrease working efficiency and quality,which is not conducive to the timely delivery of maintenance orders.In addition,at present,the complex and distributed maintenance technician scheduling mainly depends on the project managers according to personal experience.Therefore,for a maintenance technician scheduling scheme,both efficiency and fairness cannot be guaranteed.To solve the above problems,this paper studies the distributed aircraft base maintenance technician scheduling problem considering the fairness of workload distribution and designs an interactive bacterial foraging optimization algorithm according to the characteristics of the considered NP-hard problem.First,different from the traditional aircraft maintenance technician scheduling model,this paper distinguishes the maintenance tasks on different aircrafts and considers the maintenance task-technician assignment problem from four dimensions,including aircraft,maintenance task,maintenance technician,and maintenance shift.Instead of simplifying the maintenance tasks of all aircrafts into a single task sequence,the task-technician assignment for maintenance aircrafts in the hangar is abstracted as a distributed maintenance technician scheduling problem.Moreover,since multiple aircrafts in the hangar need to share the same group of maintenance technicians at the same time,the distributed technician scheduling is carried out by accumulating the maintenance time of each aircraft to ensure that maintenance tasks on multiple aircrafts can be performed synchronously.Based on the above analysis,both optimization objective and constraints are designed from the aspects of on-time delivery of the whole maintenance work,cost control,and reasonable and fair workload distribution,so as to make the scheduling scheme of maintenance technicians more specific and meet the requirements of actual maintenance scenarios.Then,according to the distributed,nonlinear,discrete,and multi-dimensional characteristics of the considered NP-hard problem,this paper proposes the interactive bacterial foraging optimization(IBFO)algorithm based on bacterial foraging optimization(BFO)algorithm to obtain excellent characteristics,such as strong adaptability to the environment and outstanding parallelism.Compared with BFO,IBFO improves both structure and information interaction mode.In terms of algorithm structure,the three-tier nested loop in the original BFO is disassembled.Chemotaxis,replication,and dispersion are regarded as three parallel operations.In terms of information interaction mode,the bacterial swarm is divided into multiple sub-swarms.By designing the information interaction mode of bacterial individuals among each sub swarm,the effective communication and learning of the whole group are realized,including information interaction within its historical position and information interaction with other individuals.Through the above improvements,bacterial individuals can obtain information from more individuals to enhance the overall optimization ability and search efficiency.Next,this paper depicts a unique encoding mechanism to connect the IBFO algorithm with the built model,and four groups of comparative experiments are designed to verify the effectiveness of IBFO in solving the above model.The BFO,swarm intelligence bacterial foraging optimization(SiBFO),bacterial colony optimization(BCO),particle swarm optimization(PSO),comprehensive learning particle swarm optimizer(CLPSO),and genetic algorithm(GA)are chosen as comparison algorithms.The experimental results show that compared with the other six algorithms,the interactive bacterial foraging optimization algorithm owns stronger search ability,higher search accuracy,better robustness as well as convergence performance.Additionally,with the expansion of the problem scales and the prominence of the distributed mode,compared with other algorithms,IBFO has more obvious advantages in cost-saving and fairness improvement than the other listed intelligent algorithms.In summary,different from the previous simplified aircraft base maintenance technician scheduling model,this paper constructs a distributed one based on the maintenance technician′s concern about the fairness of workload distribution and the aircraft base maintenance service provider′s consideration of cost control,which is more in line with the actual scenario requirements of the synchronous execution of maintenance tasks on multiple aircrafts in the maintenance hangar.This paper also devises an interactive bacterial foraging optimization algorithm suitable for solving the above model.The improvement of the algorithm structure,the division within the swarm,and the communication and learning mechanism between different sub-swarms enhance the optimizing performance and solving ability of the algorithm.The proposed method not only provides a more reasonable and humanized technician scheduling scheme for aircraft maintenance base service providers but also effectively solves the problems of time-consuming,poor fairness,and laborious manual scheduling and enhances the operational efficiency of aircraft maintenance bases.