Hybrid Flow Shop with Setup Times Scheduling Problem

The two-stage hybridflow shop problem under setup times is addressed in this paper.This problem is NP-Hard.on the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high performance-computing.Tackling this kind of problem requires the development of adapted algorithms.In this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this paper.These approximate solutions are using the optimal solution of the parallel machines under release and delivery times.Indeed,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be solved.The general solution is then a concatenation of all the solutions in each stage.In addition,three lower bounds based on the relaxation method are provided.These lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative gap.An experimental result is discussed to evaluate the performance of the developed algorithms.In total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and heuristics.Several indicators are given to compare between algorithms.The results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.

Single Machine Scheduling with Time-Dependent Learning Effect and Non-Linear Past-Sequence-Dependent Setup Times

This paper studies a single machine scheduling problem with time-dependent learning and setup times. Time-dependent learning means that the actual processing time of a job is a function of the sum of the normal processing times of the jobs already scheduled. The setup time of a job is proportional to the length of the already processed jobs, that is, past-sequence-dependent (psd) setup time. We show that the addressed problem remains polynomially solvable for the objectives, i.e., minimization of the total completion time and minimization of the total weighted completion time. We also show that the smallest processing time (SPT) rule provides the optimum sequence for the addressed problem.

No-Wait Flowshops to Minimize Total Tardiness with Setup Times

The m-machine no-wait flowshop scheduling problem is addressed where setup times are treated as separate from processing times. The objective is to minimize total tardiness. Different dispatching rules have been investigated and three were found to be superior. Two heuristics, a simulated annealing (SA) and a genetic algorithm (GA), have been proposed by using the best performing dispatching rule as the initial solution for SA, and the three superior dispatching rules as part of the initial population for GA. Moreover, improved versions of SA and GA are proposed using an insertion algorithm. Extensive computational experiments reveal that the improved versions of SA and GA perform about 95% better than SA and GA. The improved version of GA outperforms the improved version of SA by about 3.5%.

A Hybrid Estimation of Distribution Algorithm for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times

A hybrid estimation of distribution algorithm (EDA) with iterated greedy (IG) search (EDA-IG) is proposed for solving the unrelated parallel machine scheduling problem with sequence-dependent setup times (UPMSP-SDST). For makespan criterion, some properties about neighborhood search operators to avoid invalid search are derived. A probability model based on neighbor relations of jobs is built in the EDA-based exploration phase to generate new solutions by sampling the promising search region. Two types of deconstruction and reconstruction as well as an IG search are designed in the IG-based exploitation phase. Computational complexity of the algorithm is analyzed, and the effect of parameters is investigated by using the Taguchi method of design-of-experiment. Numerical tests on 1640 benchmark instances are carried out. The results and comparisons demonstrate the effectiveness of the EDA-IG. Especially, the bestknown solutions of 531 instances are updated. In addition, the effectiveness of the properties is also demonstrated by numerical comparisons. © 2014 Chinese Association of Automation.

An Improved Ant Colony Algorithm for a Single-machine Scheduling Problem with Setup Times

Motivated by industrial applications we study a single-machine scheduling problem in which all the jobs are mutu- ally independent and available at time zero.The machine processes the jobs sequentially and it is not idle if there is any job to be pro- cessed.The operation of each job cannot be interrupted.The machine cannot process more than one job at a time.A setup time is needed if the machine switches from one type of job to another.The objective is to find an optimal schedule with the minimal total jobs’completion time.While the sum of jobs’processing time is always a constant,the objective is to minimize the sum of setup times.Ant colony optimization(ACO)is a meta-heuristic that has recently been applied to scheduling problem.In this paper we propose an improved ACO-Branching Ant Colony with Dynamic Perturbation(DPBAC)algorithm for the single-machine schedul- ing problem.DPBAC improves traditional ACO in following aspects:introducing Branching Method to choose starting points;im- proving state transition rules;introducing Mutation Method to shorten tours;improving pheromone updating rules and introduc- ing Conditional Dynamic Perturbation Strategy.Computational results show that DPBAC algorithm is superior to the traditional ACO algorithm.

Identical Machine Scheduling Problem with Sequence-Dependent Setup Times: MILP Formulations Computational Study

This work aims to give a systematic construction of the two families of mixed-integer-linear-programming (MILP) formulations, which are graph- based and sequence-based, of the well-known scheduling problem. Two upper bounds of job completion times are introduced. A numerical test result analysis is conducted with a two-fold objective 1) testing the performance of each solving methods, and 2) identifying and analyzing the tractability of an instance according to the instance structure in terms of the number of machines, of the jobs setup time lengths and of the jobs release date distribution over the scheduling horizon.

A Linear Programming Approach for Parallel Cell Scheduling with Sequence-Dependent Setup Times

In this study, we consider the problem of scheduling a set of jobs with sequence-dependent setup times on a set of parallel production cells. The objective of this study is to minimize the total completion time. We note that total customer demands for each type should be satisfied, and total required production time in each cell cannot exceed the capacity of the cell. This problem is formulated as an integer programming model and an interface is designed to provide integrity between data and software. Mathematical model is tested by both randomly generated data set and real-world data set from a factory that produce automotive components. As a result of this study, the solution which gives the best alternative production schedule is obtained.

设备故障下的零空闲多设备工作中心调度

设备故障是车间调度中最为常见的动态事件,易对原生产调度计划造成影响。为保证加工生产在遇到设备故障后能够快速地对原调度方案做出调整,降低设备故障所带来的负面影响,对设备故障下的零空闲多设备工作中心调度问题进行研究。建立了设备故障下考虑调整时间和搬运时间的零空闲多设备工作中心调度模型,优化目标为最小化最大完工时间、搬运次数和系统稳定性;设计NSGA-Ⅱ-V算法用于原调度和重调度的求解;基于NSGA-Ⅱ-V获得的静态调度结果与部分重调度策略的思想,进行设备故障算例测试。测试结果表明,所提模型和算法可较好地处理动态事件为设备故障的零空闲多设备工作中心调度问题。

考虑序列相关准备时间的分布式柔性作业车间调度研究

针对考虑序列相关准备时间的分布式柔性作业车间调度问题,提出以最小化最大完工时间为优化目标的混合整数线性规划模型,并提出一种改进遗传算法。采用基于负荷均衡的种群初始化方法提高初始种群质量,根据问题特性构造6个局部扰动算子,设计多重局部扰动策略提高算法的局部搜索能力。通过扩展柔性作业车间调度基准生成测试算例,使用正交实验确定算法参数。实验结果表明,所提改进策略能够有效提高算法性能,求解结果优于对比算法,验证了调度模型和所提算法的可行性和有效性。

多目标协同正弦优化算法求解分布式流水车间调度问题

针对最小化最大完工时间(makespan)、总拖期以及平均空闲时间的多目标序列相关准备时间分布式流水车间调度问题,本文提出一种多目标协同正弦优化算法(MCSOA).算法主要包括4个核心阶段:在多邻域搜索阶段,提出了基于关键工厂的搜索策略,并通过正弦优化算法控制搜索范围;在破坏重构阶段,设计了一种迭代搜索策略引导个体的进化方向,同时使用正弦优化算法平衡全局开发与局部搜索;在选择阶段,使用非支配排序与参考点的方法筛选优质解,外部档案集用于存储所有非支配解;在协同阶段,设计种群间共享与竞争机制,平衡3个目标的优化.本文通过多目标优化的均匀性、反世代距离和覆盖率3项性能指标验证算法的有效性,并使用非参数检验证明所提出的算法具有显著性优势.

Scheduling a three-machine no-wait flowshop with separated setup time

In many practical flowshop production environments, there is no intermediate storage space available to keep partially completed jobs between any two machines. The workflow has to be continuous, implying that the no-wait conditions must be abided, which is typical in steel and plastic production. We discuss the three-machine no-wait flowshop scheduling problem where the setup times are considered as separated from processing times and sequence independent. The scheduling goal is to minimize the total flowtime. An optimal property and two heuristic algorithms for this problem are proposed. Evaluated over a large number of problems, the proposed heuristics are found that they can yield good solutions effectively with low computational complexity, and have more obvious advantage for the large size problem compared with the existing one.

交换突变策略改进萤火虫算法的异构并行机调度

序列相关设置时间的异构并行机调度问题是个NP(non-deterministic polynomial)问题,在高纬度情况下难以求解。选取任务完工时间为优化目标建立数学模型,提出一种基于交换突变策略改进的萤火虫算法,并应用该算法进行求解,以期在可接受的时间内提供近似最优解的可行方案。实验结果表明,所提出的算法在处理异构并行机调度问题时具有较突出的全局搜索优势,收敛速度较快,搜索精度高,测算了36个算例,其中的28个取得最优平均解,并且较萤火虫算法、模拟退火算法和遗传算法分别减少了11.12%、7.36%和1.43%的平均任务完工时间。

考虑多时间因素的绿色可重入混合流水车间调度问题

针对同时考虑顺序相关调整时间和运输时间等多时间因素的绿色可重入混合流水车间调度问题(GRHFSP-MTF),以最小化最大完工时间和总能耗为目标建立双目标优化模型。针对GRHFSP-MTF的特点,提出一种混合文化基因算法(HMA)。首先,提出了基于工序、机器和转速的三层编码策略;然后,设计了基于贪婪机器选择和完全随机的种群初始化方法、交叉和变异算子以及5种邻域搜索算子;最后,在不改变机器分配和工件排列的前提下,基于降低机器转速手段设计了节能算子。大量仿真实验表明,HMA可以有效地求解考虑多时间因素的绿色可重入混合流水车间调度问题,并具有较强的优越性。

改进混合多目标蚁群算法求解带运输时间和调整时间的柔性作业车间调度问题

柔性作业车间调度问题是一类重要的组合优化问题,实际生产过程中,产品搬运、机床换模、更换刀具等间接加工活动中存在运输时间和调整时间,会对生产周期产生影响。研究了同时考虑运输时间和调整时间的柔性作业车间调度问题,建立以最小化最大完工时间、机器总负载、机器关键负载和工件的交货期惩罚值为目标的数学模型,并提出一种改进的混合多目标蚁群算法。结合问题特征和算法特点设计了一种分布式编码方式,采用改进蚁群算法分别搜索各优化目标的最优调度方案,针对调度方案集进行非支配排序选择,为了提高算法的搜索精度,提出了突变和靠拢操作。最后通过基准实例和生产实例进行仿真实验,并与改进遗传算法、MOGATS算法进行对比,实验结果表明提出的改进混合多目标蚁群算法是有效和可行的。

基于多动作深度强化学习的纺机制造车间调度方法

纺机制造车间调度问题是一种具有复杂工艺约束和序列相关设置时间的柔性作业车间调度问题,为了保证调度方案的质量,提升企业的订单准时交付能力,提出了一种以最小化最大完工期为优化目标的多动作深度强化学习算法。首先,将调度问题建模为多马尔可夫决策过程。然后,针对纺机制造车间调度的工件选择和机器选择两个子问题,分别设计了用于定义工序选择策略和机器选择策略的两个编码器,以预测选择不同工序和机器的概率分布。其中,在工序选择编码器中,采用图神经网络对析取图进行编码,以降低问题规模对解的质量的影响。其次,提出了一种具有多动作空间的强化学习训练算法,用于学习两个子策略。最后,经某纺机制造企业的实际生产案例验证,该方法的性能受问题规模影响较小,与其他对比算法相比,能够获得较高质量的调度方案,训练的模型具有较好的泛化能力和稳定性。

基于时间窃取的数字电路时序优化方法

在大规模数字集成电路设计中,时序分析是签核(Signoff)的关键一环,目前电路设计中主要通过关键路径优化使电路时序达到要求,但这类方法可能会使电路结构发生改变,电路版图也要进行大量更改,延长了芯片设计周期.为能快速解决电路时序修正问题,提出了一种基于动态电路设计思想的时移触发器,此触发器去除了建立(Setup)时间,基于SMIC40 nm工艺完成电路设计和仿真,进行了触发器标准单元版图绘制,通过合理分配参数,时序参数优于标准单元库中的D触发器.不同工艺角(Process,Voltage,Temperature,PVT)仿真表明,在典型情况下,时移触发器相比于SMIC40 nm标准单元库中相同驱动能力的D触发器输出响应时间加速比达到188.6%.结合所设计的时移触发器和时间窃取(Timing Borrow)方法,分析了数字电路中时序分配情况,所设计的触发器可应用于工程更改计划(Engineering Change Order,ECO)阶段进行数字电路时序修复和优化,可减少时钟树和逻辑电路调整,有效缩短数字电路芯片设计周期.

Performance Assessment and Configuration Analysis in the Study of SCADA System (Supervisory Control and Data Acquisition)

Queuing models are used to assess the functionality and aesthetics of SCADA systems for supervisory control and data collection.Here,the main emphasis is on how the queuing theory can be used in the system’s design and analysis.The analysis’s findings indicate that by using queuing models,cost-performance ratios close to the ideal might be attained.This article discusses a novel methodology for evaluating the service-oriented survivability of SCADA systems.In order to evaluate the state of service performance and the system’s overall resilience,the framework applies queuing theory to an analytical model.As a result,the SCADA process is translated using the M^(X)/G/1 queuing model,and the queueing theory is used to evaluate this design’s strategy.The supplemental variable technique solves the queuing problem that comes with the subsequent results.The queue size,server idle time,utilization,and probabilistic generating factors of the distinct operating strategies are estimated.Notable examples were examined via numerical analysis using mathematical software.Because it is used frequently and uses a statistical demarcation method,this tactic is completely acceptable.The graphical representation of this perspective offers a thorough analysis of the alleged limits.

一种基于PI控制的数字低压差稳压器高速可调节模型

数字低压差线性稳压器由于可以在低电源电压下工作而被广泛使用。在数字低压差线性稳压器中,其利用模数转换器和积分器进行稳压操作。但是当负载出现瞬态电压变化,其稳定时间将会很长。在PI控制系统中,积分系数大的电路建立时间很短,会产生过冲,然后输出才会稳定。积分系数小的模型输出可以直接稳定,但是建立时间太长。提出了一种高速可调节电路模型,目的是利用电压传感器和时间数字转换器(TDC),并在电路中加入2种不同积分系数的积分器。首先利用电压传感器和时间数字转换技术(TDCT)实现模数转换以得到数字信号。随后判断数字信号与基准电压,在误差很大时,控制电路选择大积分系数,输出到PI控制;误差小时,控制电路选择小的积分系数,这样可以使电路结合不同积分系数电路的优点,从而达到同时缩短电路建立时间和稳定时间的目的。

Neighborhood Combination Search for Single-Machine Scheduling with Sequence-Dependent Setup Time

In a local search algorithm,one of its most important features is the definition of its neighborhood which is crucial to the algorithm's performance.In this paper,we present an analysis of neighborhood combination search for solv-ing the single-machine scheduling problem with sequence-dependent setup time with the objective of minimizing total weighted tardiness(SMSWT).First,We propose a new neighborhood structure named Block Swap(B1)which can be con-sidered as an extension of the previously widely used Block Move(B2)neighborhood,and a fast incremental evaluation technique to enhance its evaluation efficiency.Second,based on the Block Swap and Block Move neighborhoods,we present two kinds of neighborhood structures:neighborhood union(denoted by B1UB2)and token-ring search(denoted by B1→B2),both of which are combinations of B1 and B2.Third,we incorporate the neighborhood union and token-ring search into two representative metaheuristic algorithms:the Iterated Local Search Algorithm(ILSnew)and the Hybrid Evolutionary Algorithm(HEA_(new))to investigate the performance of the neighborhood union and token-ring search.Exten-sive experiments show the competitiveness of the token-ring search combination mechanism of the two neighborhoods.Tested on the 120 public benchmark instances,our HEA_(new)has a highly competitive performance in solution quality and computational time compared with both the exact algorithms and recent metaheuristics.We have also tested the HEA,new algorithm with the selected neighborhood combination search to deal with the 64 public benchmark instances of the single-machine scheduling problem with sequence-dependent setup time.HEAnew is able to match the optimal or the best known results for all the 64 instances.In particular,the computational time for reaching the best well-known results for five chal-lenging instances is reduced by at least 61.25%.

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time(EADHFSP-ST)that simultaneously optimizes the makespan and the energy consumption.We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm(TAMA)with a surprisingly popular mechanism.First,a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions.Second,multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation.Third,considering that the memetic algorithm(MA)framework is less efficient due to the randomness in the selection of local search operators,TAMA is proposed to balance the local and global searches.The first stage accumulates more experience for updating the surprisingly popular algorithm(SPA)model to guide the second stage operator selection and ensures population convergence.The second stage gets rid of local optimization and designs an elite archive to ensure population diversity.Fourth,five problem-specific operators are designed,and non-critical path deceleration and right-shift strategies are designed for energy efficiency.Finally,to evaluate the performance of the proposed algorithm,multiple experiments are performed on a benchmark with 45 instances.The experimental results show that the proposed TAMA can solve the problem effectively.