基于双层蚁群算法的云制造服务组合研究

为解决在确定的子任务加工顺序条件下进行服务组合时,云服务组合的灵活性降低、解空间被限制等问题,提出了用于描述加工顺序的工艺逻辑矩阵,建立了基于双层蚁群算法的,以时间最短、成本最低、能耗最少、可靠性最高为目标的服务组合优选模型。针对加工顺序与任务工艺逻辑要求不一致的情况,在算法中加入了基于工艺逻辑的加工顺序交换机制。通过仿真实验验证了该算法的有效性,并研究了在不同物流条件和工艺逻辑要求下,算法收敛速度的变化规律。     

基于混合粒子群算法的混流装配线投产顺序研究及仿真

针对混流装配线投产顺序的优化问题,提出两个目标函数.针于这种多目标优化问题,提出了一种混合粒子群算法.该算法结合最优解评估选取方法和遗传算法编码和交叉的思想,对粒子的全局极值和个体极值的选取做了改进.给出发动机混流装配线的一个实例,用该算法求投产顺序,按照求得的投产顺序在AutoMod仿真平台上建立仿真模型.通过仿真运行,证明了该算法求解混流装配线投产顺序的有效性以及AutoMod仿真混流装配线投产顺序的可行性.     

岸桥故障下考虑邻近岸桥共享作业的集装箱码头调度多目标优化

针对岸桥故障等原因引起的码头调度方案重新调整的实际问题，对邻近船舶采用岸桥共享作业的方法，以船舶作业时间最短和码头作业成本最低为目标建立多目标优化模型，利用混合式多点交叉和混合式单点变异方式改进NSGA-Ⅱ算法，并融入岸桥共享原则的邻域搜索策略。为寻找满足决策者偏好的调度方案，在兼顾船公司和港口方利益基础上，利用Pareto前沿分布信息，采用Pareto非劣解相对于各优化目标的偏向量化方法，得到了适合决策者偏好的最终方案。最后，以我国某集装箱码头为例验证了所提模型和算法的可行性；通过算法对比，显示改进后的算法更为有效。该研究成果可为提高港口管理水平提供决策依据。     

母舱推力偏差对分导导弹落点精度的影响分析

建立了母舱推力对落点影响的运动学模型,提出了分导调整时间的计算方法,仿真结果表明,分导顺序首先考虑最远目标点时,推力偏差作用的时间最短,对精度影响最小。对战场决策具有一定的辅助作用。     

基于自适应遗传算法立体库货位优化调整

针对单元货格式自动化立体仓库货位优化调整再分配问题,以堆垛机的立体库货架重心最低和总取货时间最短为目标,同时保证调整时间最短,采用改进自适应遗传算法进行求解,对交叉概率和变异概率进行非线性改进,并根据结果误差情况调整适应度加权系数,避免早熟和局部收敛。实验结果表明,会明显缩短之后的出库时间,并使重心下移,达到了提高出库速度和运行稳定性的目标。     

优化加权平均流程时间的平行机调度

平行机调度问题在工业界有着广泛应用,实际生产中瓶颈工序的调度很多属于这类问题.运用增强学习算法来研究以最小化作业的加权平均滞留时间为目标的动态平行机调度问题Qm I rj,sjk,Mj I ∑wjfj,考虑与作业顺序相关的转换时间和机器-作业资格约束.为了把调度问题转化为增强学习问题,定义了系统状态的表示方式,利用加权最短加工时间优先(WSPT)规则、Weng算法、排名(RA)算法和LFJ-RA(Least Flexible Job-Ranking Algorithm)算法构造行为,定义了与调度目标函数等价的报酬函数,并采用结合函数泛化器的Q学习算法来解决.实验表明Q学习算法对每个测试问题的调度结果都优于WSPT规则、排名算法、LFJ-RA算法和Weng算法.     

模拟退火算法在混流装配投产中的应用

对混流装配投产顺序的不同调度目标进行分析,建立混流装配投产的目标函数,着重讨论了模拟退火算法的原理及在混流装配投产中的应用流程,最后通过实例对算法进行验证.验证结果表明,模拟退火算法比常规的目标追迹法更有效,可以在生产中推广应用.     

基于遗传蚁群混合算法的激光切割机路径优化

激光切割机的路径优化问题是激光切割行业的一个关键问题,针对其特点将其归纳为广义旅行商问题,利用改进的遗传蚁群算法来求解该问题。算法以时间距离最短为目标函数,对轮廓切割顺序及轮廓切割起始点同时进行优化。为了让算法所得解能够快速聚集在最优解附近而又不至于陷入局部最优解,利用遗传算法快速随机的全局搜索能力来产生蚁群算法初期的信息素分布,蚁群算法采用最大最小蚂蚁算法同时在其加入变异因子。仿真结果表明取得了非常好的效果。     

调整时间与顺序相关的能耗优化调度问题

以陶瓷行业车间生产计划为背景,将其抽象为调整时间与顺序相关的能耗优化调度问题进行研究。以最少化总能耗为目标,建立数学模型;提出了基于NEH算法的混合遗传算法,其中初始化种群中,定义了虚拟工件概念,采用了改进NEH规则对种群初始化;为了对上述算法进行验证,提出了该问题的两个下界,设计了仿真试验,进行下界背离程度分析和CPU运行时间分析。计算结果表明：所设计的混合遗传算法能够在可接受的计算时间内获得合理的解。     

基于改进遗传算法的多目标柔性车间调度问题研究

通过对车间生产调度的特点和算法的研究,建立了离散车间多目标动态调度的数学模型及设计了改进的遗传算法。以最大完工时间最短、总延期时间最小和设备总负荷最小这3个目标作为车间生产优化目标调度模型。利用改进的遗传算法确定车间调度的最优解。最后通过实例分析,验证了算法的可行性。     

Considering the conveyer stoppages in sequencing mixed-model assembly lines by a new fuzzy programming approach

Mixed-model production is the practice of assembling different and distinct models in a line without changeovers with responding to sudden demand changes for a variety of models. In this paper, we specify sequence of models to minimize conveyer stoppages. We assume that our lines are fixed and we cannot change the balance of the lines. When the condition of lines like setup cost and demand of each model change, it is important to specify the sequence for minimizing the conveyer stoppages without balancing the line again because the main lines are fixed. We consider three objective functions simultaneously: minimizing the variation in the actual and required production capacity of the line and minimizing the objectives which increase the chance of conveyer stoppage, including: (a) minimizing the total setup time, (b) minimizing the total production variation cost, and (c) minimizing the total utility work cost. Because of conflicting objectives, we propose the fuzzy goal programming-based approach to solve the model. Finally, we present an estimator for nearness of conveyer stoppages and study about affecting of sub-lines and changing the conveyer velocity in a station for reducing stoppages.     

单机成组调度问题的约束满足建模与求解方法

为适应多品种小批量生产需求,企业普遍采用基于成组技术的混流生产,由此产生的成组调度需要平衡安装时间减少与满足交期之间的冲突关系。在分析安装时间是否依赖工件组排序、工件组能否分割加工等成组特征的基础上,以最小化加权流程时间与加权拖期为目标,构建了单机成组调度问题的约束满足模型,提出了以变量排序启发式搜索和前向约束传播相结合的求解方法。典型生产数据的实证分析表明,所提出的方法建模能力强,解的适应性好。     

基于混沌粒子群算法的单台批加工设备调度

提出了一种基于混沌粒子群算法的双层调度方法。双层调度的外层基于加工时间最小的目标构建组批方法集,然后将其作为内层算法的搜索空间;双层调度的内层以加工时间最小为适应值函数,采用混沌粒子群算法求解批次排序的最优解,得到最优的组批方法及其排序。通过仿真验证了该算法在搜索时间和搜索精度的可行性。     

Robust production control policies considering WIP balance and setup time in a semiconductor fabrication line

This paper discusses the problem of determining workload in a semiconductor fabrication line for providing robust production control. A mathematical model is proposed to determine the amount of wafers to be processed on equipment in a photolithography process where the setup time is incurred if the type of wafers is changed on the equipment. The objective of the model is to control the fabrication line by maintaining the target work-in-process (WIP) level as close as possible for the purpose of short cycle time and by minimizing the setup time loss for maximal throughput. The proposed model is formulated using mixed integer programming (MIP) to minimize the weighted sum of two objective functions. A heuristic approach is suggested using linear relaxation and its adjustment. Performances are evaluated for the optimal solution with computational cost and for the heuristics. It is shown that the heuristics give good solutions which are 10% away, on average, from the optimal solution, but which can be obtained in a few seconds.     

Due-date scheduling on parallel machines with job splitting and sequence-dependent major/minor setup times

This paper addresses job scheduling problems with parallel machines. To satisfy customers better in a manufacturing company, meeting due dates has been an important performance metric. Besides the numerous other factors affecting due date satisfaction, the splitting of a job through parallel machines can contribute to the reduction of production lead time, resulting in less job tardiness against their due dates. Thus, this paper presents heuristic algorithms for minimizing total tardiness of jobs to meet their due dates in a manufacturing shop with identically functioning machines. The algorithms take into account job splitting and sequence-dependent major/minor setup times. The performance of the proposed heuristics is compared with that of past three algorithms in the literature.     

ACO-based multi-objective scheduling of parallel batch processing machines with advanced process control constraints

This research was motivated by a scheduling problem in the dry strip operations of a semiconductor wafer fabrication facility. The machines were modeled as parallel batch processing machines with incompatible job families and dynamic job arrivals, and constraints on the sequence-dependent setup time and the qual-run requirements of advanced process control. The optimization had multiple objectives, the total weighted tardiness (TWT) and makespan, to consider simultaneously. Since the problem is NP-hard, we used an Ant Colony Optimization (ACO) algorithm to achieve a satisfactory solution in a reasonable computation time. A variety of simulation experiments were run to choose ACO parameter values and to demonstrate the performance of the proposed method. The simulation results showed that the proposed ACO algorithm is superior to the common Apparent Tardiness Cost-Batched Apparent Tardiness Cost rule for minimizing the TWT and makespan. The arrival time distribution and the number of jobs strongly affected the ACO algorithm’s performance.     

可重构装配线多目标优化调度研究

针对可重构装配线调度存在的问题,综合考虑影响可重构装配线调度的三个主要因素,即最小化空闲和未完工作业量、均衡零部件的使用速率、最小化装配线重构成本,建立了可重构装配线多目标优化调度的数学模型。提出了一种基于Pareto多目标遗传算法的可重构装配线优化调度方法,该算法综合运用了群体排序技术、小生境技术、Pareto解集过滤及精英保留策略,并采用了交叉概率和变异概率的自适应重构策略。实例仿真表明该算法具有比其他遗传算法更高的求解质量。

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No-wait flowshop with separate setup times to minimize maximum lateness

This paper considers the m-machine no-wait flowshop problem with the objective of minimizing the maximum lateness where setup times are considered as separate from processing times and treated as sequence-independent. A dominance relation is developed for the case of three machines and several heuristics and four new effective and efficient genetic algorithms are proposed. The genetic algorithms make use of advanced concepts like steady-state and elitist generational scheme as well as new fast-selection operators. Extensive experimentation is conducted to evaluate the performance of the dominance rule, the proposed heuristics, and the genetic algorithms. The computational and statistical analyses by means of experimental designs show that the genetic algorithms provide better results than the existing literature under the same conditions. Additionally, the proposed dominance rule shows great potential for instances where the processing and setup times are tightly distributed.     

Scheduling identical parallel machines with machine eligibility restrictions to minimize total weighted flowtime in automobile gear manufacturing

In this paper, we address a scheduling problem for minimizing total weighted flowtime, observed in automobile gear manufacturing. Specifically, the bottleneck operation of the pre-heat treatment stage of gear manufacturing process has been dealt with in scheduling. Many real-life scenarios like unequal release times, sequence dependent setup times, and machine eligibility restrictions have been considered. A mathematical model taking into account dynamic starting conditions has been proposed. The problem is derived to be NP-hard. To approach the problem, a few heuristic algorithms have been proposed. Based on planned computational experiments, the performance of the proposed heuristic algorithms is evaluated: (a) in comparison with optimal solution for small-size problem instances and (b) in comparison with the estimated optimal solution for large-size problem instances. Extensive computational analyses reveal that the proposed heuristic algorithms are capable of consistently yielding near-statistically estimated optimal solutions in a reasonable computational time.     

ANALYSIS AND IMPROVEMENT OF LEAD TIME FOR JOB SHOP UNDER MIXED PRODUCTION SYSTEM

Firstly an overview of the potential impact on work-in-process (WIP) and lead time is provided when transfer lot sizes are undifferentiated from processing lot sizes. Simple performance examples are compared to those from a shop with one-piece transfer lots. Next, a mathematical programming model for minimizing lead time in the mixed-model job shop is presented, in which one-piece transfer lots are used. Key factors affecting lead time are found by analyzing the sum of the longest setup time of individual items among the shared processes (SLST) and the longest processing time of individual items among processes (LPT). And lead time can be minimized by cutting down the SLST and LPT. Reduction of the SLST is described as a traveling salesman problem (TSP), and the minimum of the SLST is solved through job shop scheduling. Removing the bottleneck and leveling the production line optimize the LPT. If the number of items produced is small, the routings are relatively short, and items and facilities are changed infrequently, the optimal schedule will remain valid. Finally a brief example serves to illustrate the method.