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。     

基于工件窗口的发动机装配车间滚动调度

针对航空发动机装配车间因订单变更,机器故障等随机扰动造成静态调度结果失效问题,建立了重入式混合车间FIow－shop动态调度模型,综合考虑了机器,班组能力等多种资源约束;提出了一种基于工件窗口的滚动调度方法,从滚动窗口的确定,滚动机制,滚动窗口内部算法三个方面提高动态调度性能,实验表明该方法可行有效。     

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。     

应用Agent理论的生产调度系统研究

生产调度问题,一般可根据生产流程的不同分为Job-shop调度和Flowshop调度两大类(也有学者认为,存在两者相结合的第三类—混合调度)。该文研究以最小化Makespan为目标的Flowshop调度问题。基于Agent理论,提出采用Flowshop复合代理体(Flowshop-Compound-Agent,FSCA)求解Flowshop调度问题的方法。在给出FSCA的结构及其实现的基础上,通过毛纺企业制条车间的实例说明了使用FSCA解决Flowshop调度问题的有效性。     

区间数可重入混合流水车间调度与预维护协同优化

针对加工时间不确定的可重入混合流水车间调度与预维护协同优化问题,构建以区间最大完工时间、区间总碳排放和区间总预维护费用为优化目标的集成调度模型.针对问题特性,通过设计改进的可能度计算方法,定义区间意义下解的Pareto占优关系.提出一种改进的离散鲸鱼群算法,通过同步调度与维护策略,实现制造与维护的联合优化;设计个体间距离计算策略,寻找“最近较优个体”;设计个体位置移动策略以及多邻域搜索策略,有效地平衡全局搜索和局部搜索,提高收敛精度.通过大量的仿真实验和结果对比分析,表明了所提出的算法对于求解区间数可重入混合流水车间调度和预维护协同优化问题的有效性和可行性.     

应用Agent理论的生产调度系数研究

生产调度问题，一般可根据生产流程的不同分为Job-shop调度和Flowshop调度两大类（也有学者认为，存在两者相结合的第三类－混合调度）。该文研究以最小化Makespan为目标的Flowshop调度问题。基于Agent理论，提出采用Flowshop复合代理体（Flowshop-Compond-Agent,FSCA)求解Flowshop调度问题的方法，在给出FSCA的结构及其实现的基础上，通过毛纺企业制度车间的实例说明了使用FSCA解决Flowhop调度问题的有效性。     

基于Petri网仿真的航空发动机装配车间调度

针对含有并行机器组、可进行多品种小批量生产的可重入航空发动机装配车间,其调度问题的研究属于NP难问题。文中首先采用分层有色赋时Petri网进行系统的逻辑分析;然后给出了三种调度规则:①FCFS+;②SPT+CR+返工工件优先加工;③返工工件优先加工+ATC来实现生产调度;最后采用离散事件系统仿真方法,研究了上述3种调度算法对于最小化系统总的拖期时间的性能影响。实验结果表明,第二种调度最能有效的改善系统性能。     

基于遗传分散搜索算法的装配排序优化

航空发动机装配车间调度问题包括部件组装调度和试车台排序。其中试车台排序是一个关键问题,它直接影响生产效率和生产成本。为了提高生产效率、降低生产成本,文中针对航空发动机装配车间调度问题,依据实际情况,对其中关键的试车台排序问题进行深入研究。在分析试车台排序特征的基础上,建立了试车台最优排序的旅行商模型,基于遗传分散搜索算法对该模型进行求解,为装配排序优化问题提供了一种科学的方法。最后通过算例比较表明该算法的适用性和优越性。     

基于遗传分散搜索算法的装配排序优化

航空发动机装配车间调度问题包括部件组装调度和试车台排序。其中试车台排序是一个关键问题,它直接影响生产效率和生产成本。为了提高生产效率、降低生产成本,文中针对航空发动机装配车间调度问题,依据实际情况,对其中关键的试车台排序问题进行深入研究。在分析试车台排序特征的基础上,建立了试车台最优排序的旅行商模型,基于遗传分散搜索算法对该模型进行求解,为装配排序优化问题提供了一种科学的方法。最后通过算例比较表明该算法的适用性和优越性。     

Hybrid flowshop with unrelated machines, sequence-dependent setup time, availability constraints and limited buffers

This paper presents a genetic algorithm for an important production scheduling problem. Since the problem is NP-hard, we focus on suboptimal scheduling solutions for the hybrid flowshop with unrelated machines, sequence-dependent setup time, availability constraints, and limited buffers. The production environment of a television assembly line for inserting electronic components is considered. The proposed genetic algorithm is a modified and extended version of the algorithm for a problem without limited buffers. It takes into account additional limited buffer constraints and uses a new crossover operator and stopping criteria. Experimental results carried out on real production settings show an improvement in scheduling when the proposed algorithm is used.     

求解区间数分布式流水线调度的混合离散果蝇优化算法

分布式调度是制造系统领域的前沿研究,而不确定调度问题的研究更具现实意义.针对不确定分布式置换流水线调度问题,采用区间数表示工序加工时间,以最小化区间最大完工时间为目标,利用问题特性在果蝇优化框架内提出一种混合离散果蝇优化算法.首先,通过改进启发式方法和随机方法混合初始化种群;然后,基于概率协同多搜索操作执行嗅觉搜索.为了平衡算法的全局探索与局部开发能力,设计基于学习机制的双种群协同搜索环节.为了进一步提升种群性能,针对优良解设计基于切换机制的双模式局部搜索.基于大量算例的仿真结果与统计对比,表明所提出算法能更有效求解区间数分布式流水线调度问题.     

An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan

This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.     

A memetic algorithm for the re-entrant permutation flowshop scheduling problem to minimize the makespan

A common assumption in the classical permutation flowshop scheduling model is that each job is processed on each machine at most once. However, this assumption does not hold for a re-entrant flowshop in which a job may be operated by one or more machines many times. Given that the re-entrant permutation flowshop scheduling problem to minimize the makespan is very complex, we adopt the CPLEX solver and develop a memetic algorithm (MA) to tackle the problem. We conduct computational experiments to test the effectiveness of the proposed algorithm and compare it with two existing heuristics. The results show that CPLEX can solve mid-size problem instances in a reasonable computing time, and the proposed MA is effective in treating the problem and outperforms the two existing heuristics.     

基于混合遗传算法的重入式流水线调度研究

针对可重入式生产流水线的大规模调度优化问题,提出了一种改进的混合遗传算法。在计算适应值的过程中利用信息素算法的正反馈机制,解决工件在重入环节的竞争问题。并将禁忌搜索算法中独有的记忆功能引入到遗传算法的交叉与变异的进化过程中,可以有效的避免算法早熟和迂回搜索。通过实例仿真,表明改进后的算法相比于普通的遗传算法不仅有较强的收敛性,并且具有更快的寻优功能,是解决复杂调度问题的有效算法。     

A discrete whale swarm algorithm for hybrid flow-shop scheduling problem with limited buffers

This paper studies a hybrid flow-shop scheduling problem with limited buffers and two process routes that comes from an engine hot-test production line in a diesel engine assembly plant. It extends the classical hybrid flow-shop scheduling problem by considering practical constraints on buffer area resources and alternative process routes. Because of its NP-hardness and large scale, traditional optimization methods and heuristic rules cannot obtain satisfactory solutions. A discrete whale swarm algorithm (DWSA) is proposed to identify near-optimal solutions efficiently. The proposed algorithm adopts an encoding method based on the problem characteristic and a greedy delayed decoding strategy to avoid infeasible solutions. A hybrid initialization is used to ensure the quality of the initial population and diversity. A new way of computing distances and a movement rule between individuals are designed. Five mutation operators and a deduplication strategy are proposed to improve the population diversity. The effectiveness of the proposed DWSA is validated on three groups of instances and a real-world industrial case.     

改进鲸鱼算法求解分布式装配柔性作业车间生产与配送联合调度问题

针对一类先加工后装配的离散生产模式,研究分布式制造环境下的装配柔性作业车间生产与配送两阶段联合调度问题。结合实际的生产情况,考虑供应链下生产与配送过程所产生的库存成本,以最小化生产和配送的总成本为联合调度优化目标,提出一种改进鲸鱼算法。针对联合调度的多阶段调度过程,设计了一种基于工序、产品、工厂、机器和车辆的五层编码策略;根据各阶段的特点提出了相应的混合种群初始化策略,以提高解的质量;以加强种群中领头鲸鱼个体与普通鲸鱼个体的联系为导向,改进了鲸鱼觅食的搜索操作并提出四种邻域结构,以增强算法的全局探索和局部搜索能力。最后,通过仿真实验,对比相关研究领域的多种算法来验证所提算法在收敛速度和求解质量等方面的优势,并且将联合调度与分阶段调度进行实验对比,验证了联合调度的优越性。     

Genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line

Re-entrant production lines, such as those which occur in micro-electronic wafer fabrication, are characterized by a product routing that consists of multiple visits to a facility during the manufacturing process. With the development of micro-electronic technology, the research on the scheduling and control problem of re-entrant micro-electronic production line has attracted more and more people from both academia and industry to study and has become a challenging research subject. Some results of the scheduling of re-entrant micro-electronic production line based on heuristic sequence rules have been obtained. However, performances of these sequence rules are not good enough in re-entrant micro-electronic production line because of their sensitivity to the variation of types of production line. A genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line is proposed in this paper. Comparisons between the proposed algorithm and some other typical sequence rules have been made through the simulations of a practical micro-electronic production line. The static and dynamic simulation results show that the algorithm has considerable improvements on performances of the micro-electronic production such as mean cycle time, mean number of work-in-process, production rate.     

Production scheduling for blocking flowshop in distributed environment using effective heuristics and iterated greedy algorithm

Production scheduling plays an important role in the intelligent decision support system and intelligent optimization decision technology. In the context of the globalization trend, the current production and management may extend from a single factory to a distributed production network. In this paper, we study the distributed blocking flowshop scheduling problem (DBFSP) that is an important generalization of the traditional blocking flowshop scheduling problem in the distributed environment. Six constructive heuristics and an iterated greedy (IG) algorithm are proposed to minimize the makespan, which provides procedures for obtaining efficient and effective solutions to make decision-making sounder. The first five heuristics are developed based on the well-known NEH2 heuristic [B. Naderi, R. Ruiz, The distributed permutation flowshop scheduling problem, Computers & Operations Research, 37 (4) (2010) 754–768.] and the last heuristic is presented by extending the PW heuristic [Q.K. Pan, L. Wang, Effective heuristics for the blocking flowshop scheduling problem with makespan minimization, Omega, 40 (2) (2012) 218–229.] to DBFSP in an effective way. The composite heuristics that combining constructive heuristics and local searches are also studied. The proposed composite heuristics are chosen to generate an initial solution with a high level of quality. Keeping the simplicity of the IG algorithm, three local search procedures, two destruction procedures, an improved reconstruction procedure, and a simulated annealing-like acceptance criterion are well designed based on the problem-specific knowledge to enhance the IG algorithm. The computational experiments are carried out based on the 720 benchmark instances from the literature. The results show that the proposed heuristics are very effective for solving the problem under consideration and the presented IG algorithm performs significantly better than the other state-of-the-art metaheuristics from the literature.     

Multi-objective colonial competitive algorithm for hybrid flowshop problem

This study analyses the multi-objective optimization in hybrid flowshop problem, in which two conflicting objectives, makespan and total weighted tardiness, are considered to be minimized simultaneously. The multi-objective version of Colonial Competitive Algorithm (CCA) for real world optimization problem is introduced and investigated. In contrast to multi-objective problems solved by CCA, presented in the literature, which used the combination of the objectives as single objective, the proposed algorithm is established on Pareto solutions concepts. Another novelty of this paper is estimating the power of each imperialist by a probabilistic criterion for this multi objective algorithm. Besides that, the variable neighborhood search is implemented as an assimilation strategy. Performance of the algorithm is finally compared with a famous algorithm for scheduling problem, NSGA-II, and the multi-objective form of CCA [28].