基于总加权完成时间的可重入混合流水车间调度问题

研究以最小化总加权完成时间为目标的可重入混合流水车间调度问题(RHFS-TWC),并构建问题的整数规划模型.根据模型的特点,设计基于二维矩阵组的调度解编码方案,结合NEH启发式算法确定工件初始加工顺序,生成高质量初始调度解群.为避免算法陷入早熟及扩大解的搜索空间,给出IGA的遗传参数自适应调整策略,最终形成NEH-IGA融合求解策略.针对不同规模问题分别用传统GA、基于遗传参数自适应调整的IGA、NEH启发式、NEH-IGA算法进行仿真测试,仿真结果表明NEH启发式和遗传参数自适应动态调整策略的引入有效改善了原有GA的求解能力,NEH-IGA算法在求解RHFS-TWC问题方面优势明显.     

差异工件流水车间批调度问题的求解

针对流水车间批调度问题,提出一种基于群智能算法的求解思路。结合问题具体特点,给出工件集合的分批策略,设计了将Palmer和Best Fit（BF）分批规则相结合的分批方法;在批排序阶段,提出了一种改进的微粒群算法;在粒子初始生成阶段,通过引入NEH启发式算法改进了粒子的初始化质量;在全局最佳位置更新前,通过变邻域搜索优化了算法的局部搜索能力,避免了算法陷入局部最优。仿真实验表明,改进后的算法优于传统的微粒群算法和NEH启发式算法。     

离散和声求解带启动时间批量流水线调度问题

分别在有等待和无等待的情况下,深入分析了带有启动时间的批量调度问题,以最小化最大完成时间为目标,提出了两种离散和声搜索算法。针对算法本质连续而问题离散的矛盾,对和声搜索算法进行改进。首先提出了基于工序的编码方式,采用inver-over和重组两种离散算子产生候选解的进化机制;并利用改进的NEH(Nawaz-Enscore-Ham)方法进行初始化,产生的高质量和多样化的初始种群有效地指导了算法的进化方向,提高收敛速度;最后将一种简单而有效的局部邻域搜索方法嵌入到和声搜索算法中以增强其局部搜索能力。仿真实验和比较结果表明了所提算法的有效性。     

An improved heuristic to minimize total flow time for scheduling in the m-machine no-wait flow shop

In this paper, we present a constructive heuristic to minimize total flow time criterion for the well-known NP-hard no-wait flow shop scheduling problem. It is based on the assumption that the priority of a job in the initial sequence is given by the sum of its processing times on the bottleneck machines. The initial sequence of jobs thus generated is further improved using a new job insertion technique. We show, through computational experimentation, that the proposed method significantly outperforms the best-known heuristics while retaining its time complexity of O(n²). Statistical tests of significance are used to confirm the improvement in solution quality.     

瓶颈指向的启发式算法求解混合流水车间调度问题

针对以最小化时间表长为目标的复杂混合流水车间调度问题,提出了一种将机器布局和工件加工时间特征紧密结合的启发式算法.首先,充分利用各阶段平均机器负荷一般不相等的特点确定瓶颈阶段,构建初始工件排序.其次,针对在瓶颈阶段前加工时间较短而瓶颈阶段后加工时间相对较长的工件,在第1阶段优先开始加工.同时,在瓶颈阶段前的每一个阶段,每当有工件等待加工或同时完工时,优先选择瓶颈阶段前剩余加工时间最短的工件加工;在瓶颈阶段以及瓶颈阶段之后,则优先选择这台机器后剩余加工时间最长的工件加工.最后,采用工件交换和插入操作改进初始调度.用Carlier和Neron的Benchmark算例测试提出的启发式算法.将计算结果与NEH启发式算法进行了比较,平均偏差降低了0.0555%,表明这个启发式算法是有效的.     

An artificial neural network based heuristic for flow shop scheduling problems

The objective of this paper is to find a sequence of jobs in the flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the problem. The network is trained with the optimal sequences of completely enumerated five, six and seven jobs, ten machine problem and this trained network is then used to solve the problem with greater number of jobs. The sequence obtained using artificial neural network (ANN) is given as the initial sequence to a heuristic proposed by Suliman and also to genetic algorithm (GA) as one of the sequences of the population for further improvement. The approaches are referred as ANN-Suliman heuristic and ANN-GA heuristic respectively. Makespan of the sequences obtained by these heuristics are compared with the makespan of the sequences obtained using the heuristic proposed by Nawaz, Enscore and Ham (NEH) and Suliman Heuristic initialized with Campbell Dudek and Smith (CDS) heuristic called as CDS-Suliman approach. It is found that the ANN-GA and ANN-Suliman heuristic approaches perform better than NEH and CDS-Suliman heuristics for the problems considered.     

基于改进离散和声算法的批量流水线调度研究

针对批量流水线调度问题,提出了以总流经时间为目标的改进离散和声算法。与基本的和声算法相比,该算法首先采用了基于工件序列的编码方式,使其直接应用于调度问题,同时运用NEH和SWAP方法产生初始和声库,保证了初始种群具有较高的质量和多样性。使用自适应和声微调概率参数和INSERT方法产生新解,提高了算法的优化性能。为了提高算法的局部搜索能力,结合交换扰动策略和插入邻域搜索算法给出了两种混合求解策略。仿真实验表明所提算法的有效性。     

An improved NEH heuristic to minimize makespan in permutation flow shops

For over 20 years the NEH heuristic of Nawaz, Enscore, and Ham [A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, The International Journal of Management Science 1983;11:91–5] has been commonly regarded as the best heuristic for solving the NP-hard problem of minimizing the makespan in permutation flow shops. The strength of NEH lies mainly in its priority order according to which jobs are selected to be scheduled during the insertion phase. Framinan et al. [Different initial sequences for the heuristic of Nawaz, Enscore and Ham to minimize makespan, idle time or flowtime in the static permutation flowshop problem. International Journal of Production Research 2003;41:121–48] presented the results of an extensive study to conclude that the NEH priority order is superior to 136 different orders examined. Based upon the concept of Johnson's algorithm, we propose a new priority order combined with a simple tie-breaking method that leads to a heuristic that outperforms NEH for all problem sizes.     

On insertion tie-breaking rules in heuristics for the permutation flowshop scheduling problem

The most efficient approximate procedures so far for the flowshop scheduling problem with makespan objective – i.e. the NEH heuristic and the iterated greedy algorithm – are based on constructing a sequence by iteratively inserting, one by one, the non-scheduled jobs into all positions of an existing subsequence, and then, among the so obtained subsequences, selecting the one yielding the lowest (partial) makespan. This procedure usually causes a high number of ties (different subsequences with the same best partial makespan) that must be broken via a tie-breaking mechanism. The particular tie-breaking mechanism employed is known to have a great influence in the performance of the NEH, therefore different procedures have been proposed in the literature. However, to the best of our knowledge, no tie-breaking mechanism has been proposed for the iterated greedy. In our paper, we present a new tie-breaking mechanism based on an estimation of the idle times of the different subsequences in order to pick the one with the lowest value of the estimation. The computational experiments carried out show that this mechanism outperforms the existing ones both for the NEH and the iterated greedy for different CPU times. Furthermore, embedding the proposed tie-breaking mechanism into the iterated greedy provides the most efficient heuristic for the problem so far.     

A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid

Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.     

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on m machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard’s well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling problems.     

Completion time variance minimization in single machine and multi-machine systems

In this paper we consider the problem of minimizing the completion time variance of n jobs on a single machine with deterministic processing times. We propose a new heuristic and compare the results with existing popular heuristics for the problem. We also propose a method based on genetic algorithms to solve the problem. We present the worst case performance analysis of the proposed heuristic. We also consider the problem of minimizing the completion time variance of n jobs on m identical parallel machines in both restricted and unrestricted versions. A heuristic method and a method based on genetic algorithms are presented for both the cases and results of computational testing are provided. It is concluded that the proposed methods provide better results compared to existing methods for the single machine case as well as for the multi-machine case.     

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.     

改进离散粒子群算法求解柔性流水车间调度问题

针对以最小化完工时间为目标的柔性流水车间调度问题(FFSP),提出了一种改进离散粒子群(DPSO)算法.所提算法重新定义粒子速度和位置的相关算子,并引入编码矩阵和解码矩阵来表示工件、机器以及调度之间的关系.为了提高柔性流水车间调度问题求解的改进离散粒子群算法的初始群体质量,通过分析初始机器选择与调度总完工时间的关系,首次提出一种基于NEH算法的最短用时分解策略算法.仿真实验结果表明,该算法在求解柔性流水车间调度问题上有很好的性能,是一种有效的调度算法.     

求解置换流水车间调度问题的改进遗传算法

针对置换流水车间调度问题的基本特征和传统遗传算法易早熟的缺陷,设计了改进遗传算法来求解此问题。采用NEH和Palmer启发式算法进行种群初始化,以提高初始解的质量;根据Metropolis准则对染色体进行选择操作,避免陷入局部最优;在变异过程中引入禁忌算法,避免迂回搜索;在算法迭代过程中引入了保优机制,避免丢失优秀染色体的基因信息;采用自适应终止准则,以保证解的质量。基于典型Benchmark算例的仿真实验结果表明,算法在求解质量和收敛速度方面明显优于NEH算法和种群经过初始优化的传统遗传算法。     

基于延迟函数次梯度启发式道路交通补偿策略

拥挤收费被认为是解决交通拥挤的有效方法,解决道路交通拥堵的主要想法是,对于有些容易造成拥堵的道路进行收费,而对于其他未充分利用的道路进行适当补偿,对此提出一种基于延迟函数的次梯度启发式道路交通补偿策略。首先,给出道路集的收费/补贴的非线性规划模型,主要是基于Beckmann最小化目标函数实现,然后利用库恩-希尔斯条件和拉格朗日乘子建立模型的条件约束;其次,基于启发式算法建立道路交通的定价补偿策略,利用边际成本建立延迟函数分析模型,然后基于次梯度法进行模型的优化;最后,通过在真实道路网络上的仿真实验,显示所提算法在旅行时间、交通流量、收敛性等指标上均具有较好的性能,验证了算法的有效性。     

基于深度邻域搜索PSO 算法的装配序列优化问题

针对产品结构特征建立几何约束矩阵, 以最大化满足几何约束条件装配次数和最小化装配方向改变次数为目标, 研究产品装配序列优化问题. 利用值变换的粒子位置和速度更新规则, 基于具有随机性启发式算法产生初始种群, 提出一种带有深度邻域搜索改进策略的粒子群算法解决装配序列问题. 通过装配实例验证了所提出算法的性能并对装配序列质量进行了评价, 所得结果表明了该算法在解决装配序列优化问题上的有效性与稳定性.

     

一种用于PFSP节能优化的混合禁忌搜索算法

为了追求节能减排与净利润最大化,建立一种置换流水车间订单接受与调度模型。禁忌搜索是一类启发式全局搜索算法,传统禁忌搜索对初始解依赖较大,没有对考虑能效的置换流水车间调度问题进行更深入的优化。鉴于问题的复杂性,提出了一种节能混合禁忌搜索算法,结合了NEH构造启发式算法的优势,并在该算法中设计了订单接受与拒绝编码方式、能耗调整与交货期配置策略。最后采用大量随机实例对性能进行分析。实验结果表明,通过上述改进,改善了算法的全局搜索能力与解决复杂模型的寻优能力,节能混合禁忌搜索较单一算法而言性能更优,可以有效增加企业总净利润,降低能源消耗。     

A generalized constructive algorithm using insertion-based heuristics

In this paper, a generalized constructive algorithm referred to as GCA is presented which makes it possible to select a wide variety of heuristics just by the selection of its arguments values. A general framework for generating permutations of integers is presented. This framework, referred to as PERMGEN, forms a link between the numbering of permutations and steps in the insertion-based heuristics. A number of arguments controlling the operation of GCA are identified. Features and benefits of the generalized algorithm are presented through the extension of the NEH heuristic, a successful heuristic solution approach of Nawaz, Enscore, and Ham for the permutation flowshop problem (PFSP). The goal of the experimental study is to improve the performance of the NEH heuristic on the PFSP. To achieve this goal, the space of algorithmic control arguments is searched for a combination of values that define an algorithm providing lower makespan solutions than NEH, in a linear increase of CPU time. Computational experiments on a set of 120 benchmark problem instances, originally proposed by Taillard, are performed to establish a more robust version of the original NEH constructive heuristic. The proposed procedures outperform NEH, preserving its efficiency and simplicity.     

基于TDMA方式WMN中一种链路调度机制研究

基于TDMA方式的无线网状网中,链路调度对网络性能起着重要作用.针对固定顺序的待调度链路集,提出求解最优调度周期的启发式算法;基于链路顺序对算法性能的影响,从全局优化的角度对全网链路进行排序,提出基于遗传算法的最优链路调度机制.仿真结果表明,该算法能快速收敛于全网链路的最小调度周期,具有比现有算法更高的传输效率和更低的实施复杂度.