知识化制造环境下航空发动机装配线调度及自重构

航空发动机装配工序数量多、工序间装配约束复杂. 当产品需求变化时, 人工调整存在响应速度慢、装配效率低等问题. 以最小化产品完工成本、工序提前期惩罚成本及班组重构成本加权和为目标, 建立了航空发动机装配线调度和装配班组自重构优化模型. 提出一种新的基于工序局部最优排序的分解算法, 将调度问题分解为单个装配组上工序顺序优化问题. 设计了一种工序后向插入搜索策略. 最后提出装配线调度及自重构集成优化算法. 通过数值试验,验证了模型与算法的有效性.     

面向航空发动机装配线的知识化制造系统重调度和自重构

为了解决航空发动机装配过程中存在的不确定返工问题,建立了面向航空发动机装配线的知识化制造系统(knowledgeable manufacturing system,KMS)重调度和班组自重构优化模型,提出装配线重调度及自重构集成优化算法.在算法调度层,证明了以加权完工成本为优化目标的工序排序性质,并对工序进行初始排序.定义了3种邻域结构,用变邻域搜索(variable neighborhood search,VNS)对工序在并行装配组上的指派问题和调度问题进行优化.在重构层,在不违背装配组装配技能约束的前提下利用装配线负载平衡原则对装配班组进行配置,并采用禁忌搜索(tabu search,TS)对班组配置进行优化.仿真实验结果表明了模型与算法的有效性.     

不确定环境下的航空发动机装配线适应性调度方法

航空发动机装配是航空发动机制造过程的关键环节,其工序多,流程复杂,生产过程中扰动频发,如装配时间波动、不合格返工等.针对不确定环境下的航空发动机装配线的调度问题,提出一种基于门控循环神经网络(GRU)的适应性调度方法.该调度方法包含扰动识别和调度规则调整两个部分:扰动识别模块以滑动时间窗口为周期,利用GRU神经网络进行渐近型扰动的识别;调度规则调整模块以扰动识别的结果为触发,通过构建基于GRU神经网络的调度规则决策模型,输出适配当前生产状态的新的调度规则,用以指导生成更新的调度方案.最后,以某航空发动机装配线为研究案例,对所提出适应性调度方法进行验证分析.对比实验结果表明,所提出方法能够有效提升装配线的设备利用率、日均生产率等性能.     

面向虚拟装配的VRML模型优化重构

对基于Web的虚拟装配中VRML模型优化重构进行了研究.提出了虚拟装配环境中的五层结构信息模型;在此基础上,分析VRML模型的语法结构和语法解析过程,建立了节点重构算法和优化处理算法;实现了VRML模型组织结构和信息描述的优化重构,很好地满足了虚拟装配快速设计与分析的需求.开发了基于Web的交互式虚拟装配原型系统,验证了模型优化重构算法的可行性.     

基于窗口的某航空发动机装配车间动态调度

针对航空发动机装配车间因急件插入造成静态调度结果失效的问题,文中在考虑机器能力约束的基础上,建立了重入式混合Flow-shop动态调度模型;提出了基于窗口的动态调度策略,根据急件插入时刻,将已生成的预调度方案中的发动机分成三部分:正在装配发动机、装配完成发动机、未装配发动机。再调度窗口的大小包含正在装配发动机的未装配工序和插入的急件。该动态调度策略不仅保证了事件响应的快速性,而且尽量减少对预调度方案的变更。实验表明该动态调度方法可行有效。     

基于反向差分算法的航空发动机装配车间优化调度

航空发动机装配车间的各个生产线的优化调度问题是一个典型的具有可重入特性的生产调度问题,从调度优化难易的角度来看,属于NP难题。针对这个问题,在分析航空发动机的工艺流程与特点以及航空发动机的数学模型之后,以最小化最大完工时间为调度优化问题的目标函数,提出了基于反向差分进化算法的优化调度方案。最后,仿真实验表明,该方案对于解决优化调度问题是行之有效的。     

基于窗口的某航空发动机装配车间动态调度

针对航空发动机装配车间因急件插入造成静态调度结果失效的问题,文中在考虑机器能力约束的基础上,建立了重入式混合Flow-shop动态调度模型;提出了基于窗口的动态调度策略,根据急件插入时刻,将已生成的预调度方案中的发动机分成三部分：正在装配发动机、装配完成发动机、未装配发动机。再调度窗口的大小包含正在装配发动机的未装配工序和插入的急件。该动态调度策略不仅保证了事件响应的快速性,而且尽量减少对预调度方案的变更。实验表明该动态调度方法可行有效。     

工序加工时间不确定的可重入调度问题的求解

针对一类工序加工时间不确定的可重入调度问题,以提前/拖期惩罚区间大小为目标构建调度模型,并设计一种兼顾区间极值和区间跨度的方法对惩罚区间进行定量比较,同时可根据生产的实际需求或调度人员的倾向灵活调节区间极值和区间跨度所占的比重.根据调度模型,提出一种变邻域化学反应优化算法(VNCRO),并设计4种化学反应.其中:碰壁反...     

混合流水车间提前/拖期调度问题的DE优化解

针对JIT生产模式下的混合流水车间调度问题特点,提出了采用DE算法与指派规则联合调度策略求解流水车间提前/拖期调度问题。构建了混合流水车间的提前/拖期调度模型。详细论述了DE算法的实施流程和关键问题。在算法实施过程中,首先,采用DE算法进行全局寻优,完成生产任务指派,确定某个工件在某个工序在哪个工位加工;然后采用局部指派规则来确定工件在该工序的开工时间。在满足目标完成时间(交货期)的前提下,使提前惩罚费用与拖期惩罚费用之和最小。数值计算结果证明了该算法的有效性。     

带有交货期的比例流水车间调度问题的合作博弈

针对具有多个客户订单的比例流水车间调度问题,在考虑有交货期及提前和拖期惩罚下,以客户支出成本为优化指标,在客户通过合作结成联盟的方式下,以联盟内成员进行重新调度所获得的最大成本节省为联盟的价值,建立合作博弈模型.该合作博弈是具有无外部性的平衡博弈,从而有非空核.考虑到客户对提前加工和延迟加工的迫切程度不同,提出基于提前及拖期惩罚的β规则分配方法,该方法能得到带有交货期的比例流水车间调度合作博弈的一个核分配.通过混合差分进化算法求解最优调度顺序,实验结果验证了基于合作博弈模型的调度方法及成本分配方法的有效性.     

A problem space genetic algorithm in multiobjective optimization

In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in flexible manufacturing systems. The PSGA is used to generate approximately efficient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the first implementation of PSGA to solve a multiobjective optimization problem (MOP). In multiobjective search, the key issues are guiding the search towards the global Pareto-optimal set and maintaining diversity. A new fitness assignment method, which is used in PSGA, is proposed to find a well-diversified, uniformly distributed set of solutions that are close to the global Pareto set. The proposed fitness assignment method is a combination of a nondominated sorting based method which is most commonly used in multiobjective optimization literature and aggregation of objectives method which is popular in the operations research literature. The quality of the Pareto-optimal set is evaluated by using the performance measures developed for multiobjective optimization problems.     

An adaptive genetic algorithm for demand-driven and resource-constrained project scheduling in aircraft assembly

Scheduling of aircraft assembling activities is proven as a non-deterministic polynomial-time hard problem; which is also known as a typical resource-constrained project scheduling problem (RCPSP). Not saying the scheduling of the complex assemblies of an aircraft, even for a simple product requiring a limited number of assembling operations, it is difficult or even infeasible to obtain the best solution for its RCPSP. To obtain a high quality solution in a short time frame, resource constraints are treated as the objective function of an RCPSP, and an adaptive genetic algorithm (GA) is proposed to solve demand-driven scheduling problems of aircraft assembly. In contrast to other GA-based heuristic algorithms, the proposed algorithm is innovative in sense that: (1) it executes a procedure with two crossovers and three mutations; (2) its fitness function is demand-driven. In the formulation of RCPSP for aircraft assembly, the optimizing criteria are the utilizations of working time, space, and operators. To validate the effectiveness of the proposed algorithm, two encoding approaches have been tested with the real data of demand.     

Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: A case study based on the apparel industry

An effective job shop scheduling (JSS) in the manufacturing industry is helpful to meet the production demand and reduce the production cost, and to improve the ability to compete in the ever increasing volatile market demanding multiple products. In this paper, a universal mathematical model of the JSS problem for apparel assembly process is constructed. The objective of this model is to minimize the total penalties of earliness and tardiness by deciding when to start each order’s production and how to assign the operations to machines (operators). A genetic optimization process is then presented to solve this model, in which a new chromosome representation, a heuristic initialization process and modified crossover and mutation operators are proposed. Three experiments using industrial data are illustrated to evaluate the performance of the proposed method. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the JSS problem in a mixed- and multi-product assembly environment.     

基于遗传算法的某航空发动机装配车间优化调度

航空发动机装配车间装配生产线的调度问题,是一类比较典型的混合Flowshop问题,同时还带有工件可重人等特点,这就区别于一般的Flowshop和Jobshop调度问题,因此,将可重入混合车间调度问题划为第三类调度问题。关于重入式混合车间生产调度的优化问题通常来说都是属于NP难问题。文中通过某航空发动机装配车间生产线的研究,以最小化最大完工时间为目标函数,借助随机矩阵的编码方式和改进的交叉方法与变异方法,提出了基于遗传算法的调度优化方法。最后实验结果表明,文中提出的改进算法能够有效地实现装配车间调度的优化。     

改进离散粒子群算法及其在飞机装配任务调度中的应用研究

论文阐述了飞机装配过程中任务调度的重要意义,介绍了当前调度算法的 研究现状,对离散粒子群优化算法进行研究并在此基础上提出一种基于激励原则的改进离散 粒子群优化算法。最后以某型飞机尾段装配流程为对象对改进后的算法进行验证,得到良好 效果。     

基于滚动时域优化的机场起降动态调度

为了减少作战时军用飞机实际起降时刻与计划起降时刻产生的偏差,提出一种基于滚动时域优化原理的动态机场起降调度策略,分析军用飞机起降过程中的时间窗口约束和尾流间隔约束。对进入每个预测窗口内的飞机队列,以加权提前/延误时间和最小作为目标函数,将军用飞机起降调度模型转换为带到达时间的单机加权E/T调度模型。设计一种针对自然数域的离散粒子群算法,并应用该算法进行模型求解。通过仿真实验确定了算法的参数,仿真结果说明了模型和算法的有效性。     

Hybrid flow shop scheduling with assembly operations and key objectives: A novel neighborhood search

This paper addresses hybrid flow shop scheduling problem (HFSP) with assembly operations, in which parts of each product are produced in a hybrid flow shop and then assembled at an assembly stage. The goal is to minimize total tardiness, maximum tardiness and makespan simultaneously. Tardiness objectives are regarded as key ones because of their relative importance and this situation is seldom considered. A simple strategy is applied to handle the optimization with key objectives. A novel neighborhood search with global exchange (NSG) is proposed, in which a part-based coding method is adopted and global exchange is cooperated with neighborhood search to produce high quality solution. Extensive experiments are conducted and the results show that the strategy on key objectives is reasonable and effective and NSG is a very competitive method for the considered HFSP.     

Solving push-out conflicts in apron taxiways of airports by a network-based simulation

At busy airports, push-out conflicts occur simultaneously in apron taxiways and affect gate assignment operations in apron control. In current manual operations, it is not easy for apron controllers to consider and solve such conflicts in an exact and efficient manner, due to the uncertainties involved in the operations, the difficulties in efficiently controlling and solving push-out conflicts, and also the fact that too many more important factors should be considered in these real-time operations. At most airports today, a buffer time between the use of gates is introduced in gate assignment operations to reduce the impact of push-out conflicts to the delay of aircraft getting into, or leaving from, a gate. From the standpoint of gate assignment operations in apron control, this study analyzes aircraft ground movements and conflicts in apron taxiways. For coping with increasing air traffic demand and minimizing delays caused by inefficient scheduling of aircraft ground movements, and their effect on the gate assignment operations, a network-based model is proposed to perform simulation to support the analysis of the effect of aircraft ground movements in apron taxiways to gate assignment operations. By the proposed method, the delay characteristics of aircraft ground movements in apron taxiways and the activities on gates can be analyzed and studied in detail. Furthermore, the results of the simulation are applied to solve push-out conflicts and minimize delays to aircraft on gates in apron control. Computational results of experiments are also presented and discussed.