用约束满足自适应神经网络和有效的启发式算法解Job—shop调度问题

提出一种用约束满足自适应神经网络结合有效的启发式算法求解Ｊｏｂ－ｓｈｏｐ调度问题,在混合算法中,自适应神经网络具有在网络运行过程中神经元的偏置和连接权值自适应取值的特性,被用来求得调度问题的可行解,启发式算法分别和来增强神经网络的性能,获得确定排序下最优解和提高可行解的质量。仿整表明了本文提出的混合算法的快速有效性。     

用约束满足自适应神经网络和有效的启发式算法解Job-shop调度问题

提出一种用约束满足自适应神经网络结合有效的启发式算法求解Ｊｏｂ－ｓｈｏｐ调度问题．在混合算法中,自适应神经网络具有在网络运行过程中神经元的偏置和连接权值自适应取值的特性,被用来求得调度问题的可行解,启发式算法分别被用来增强神经网络的性能、获得确定排序下最优解和提高可行解的质量．仿真表明了本文提出的混合算法的快速有效性．     

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.     

树型网格计算环境下的自适应任务调度算法

提出一种基于树型计算网格的自适应调度算法,实现对小粒度独立任务和用户大作业的自适应最优调度。通过对网格环境的实时检测,给出了基于节点负载状况、节点任务执行时间、任务传输时间和任务特性的自适应调度算法,即基于最优任务分配方案的启发式任务调度算法。通过实验与其他调度算法的比较,证明了所提出的任务调度算法在负载平衡和最优跨度方面具有明显的优越性。     

基于遗传算法车间流控制中调度问题的研究

提出了实现车间调度的混合遗传算法的设计方案，把经典的启发式算法、自适应算法与遗传算法相结合，将启发式搜索运用于初始种群的生成，充分发挥遗传算法良好的全局搜索能力和启发式搜索结构简单搜索速度快的特性，采用自适应方法改进交叉概率与变异概率，并通过实验验证了算法的有效性、     

网络编码调度算法的研究

近年来,网络编码作为提高通信系统吞吐量一种手段。在多播的通信网络,网络中各个传送节点结合动态变化的网络情况,对不同信息流的数据包进行编码处理,从而减轻局部节点的阻塞,提高了整个通信系统的性能。文章主要工作要体现在典型无线通信网络中,引入动态网络编码调度算法,提高无线通信系统的网络编码增益和系统吞吐量;探讨在自适应无线通信系统下,如何适当的使用自适应技术,使得动态网络编码调度算法的作用发挥到最大。各个节点间的发送端更应选取适合的自适应技术,来提高无线通信系统性能。通过MATLAB仿真显示,带有自适应技术的无线网络,在动态网络编码调度算法作用下,对改进系统性能有着更加重要的现实意义。     

异构传感器网络中基于差分进化的调度算法

为提高异构有向传感器网络的节点调度效率,基于学习自动机提出一种参数自适应的差分进化算法。将节点调度问题转化为集合覆盖问题,利用学习自动机与环境的交互实现差分算法控制参数的自适应选择,同时采用自适应的变异策略增强算法解决集合覆盖问题时的寻优能力。仿真结果表明,相比原始差分进化算法,该算法拓展了参数自适应性,优化能力更强,并且能够延长异构有向传感器网络的生存时间。     

混合流水车间插单重调度的超启发式遗传算法

针对带有紧急订单的混合流水车间插单重调度问题,提出了一种双层编码的超启发式遗传算法。针对混合流水车间具有的订单排序和机器选择的双决策特征,在算法低层设计双层编码方案,在个体中表示订单排序和机器选择两类信息,对应一个唯一调度解,进而提出了12种排序和选择启发式对个体进行迭代优化;在算法高层采用自适应遗传算法,用来确定订单排序启发式和机器选择启发式的操作组合以及各组合执行的次序,并设计了自适应变异算子来优化算法的有效性。大规模数据实验的结果表明,所提算法具有很好的求解质量和求解效率。     

WSN中一种自适应功率控制及调度算法

在低功耗自适应分簇算法中,无线传感网(WSN)的簇成员节点由于簇首时分复用调度的相互独立性会产生簇间干扰,并且还存在发射功率浪费的现象.为此,提出一种自适应功率控制及调度算法,在基站的辅助下,簇首通过监听邻居簇首调度及功率等级表来自适应地安排簇内节点的发射功率级和时隙.仿真结果表明,该算法能降低50％的簇间干扰,减小功率浪费.     

多构造蚁群优化求解置换流水车间调度问题

针对置换流水车间调度问题,提出了一种多构造蚁群优化求解算法。在该算法中,蚁群采用两种方式构造解,分别是基于NEH(Nawaz-Enscore-Ham,NEH)启发式算法和Rajendran启发式算法,并根据解的质量,自适应地调整两种构造方式在蚁群中所占的比例。对置换流水车间调度问题的基准问题测试表明,提出的算法是有效的。     

Minimizing total earliness and tardiness on a single machine using a hybrid heuristic

This paper focuses on scheduling jobs with different processing times and distinct due dates on a single machine with no inserted idle time as to minimize the sum of total earliness and tardiness. This scheduling problem is a very important and frequent industrial problem that is common to most just-in-time production environments. This NP hard scheduling problem is herein solved using a hybrid heuristic which combines local search heuristics (dispatching rules, hill climbing and simulated annealing) and an evolutionary algorithm based on genetic algorithms. The heuristic involves low and high, relay and teamwork hybridization. Computational results reflect the sizeable solution quality improvement induced by hybridization, and assess the impact of each type of hybridization on the efficiency of the hybrid heuristic.     

Improved methods for scheduling flexible manufacturing systems based on Petri nets and heuristic search

This paper proposes and evaluates two improved Petri net （PN）-based hybrid search strategies and their applications to flexible manufacturing system （FMS） scheduling. The algorithms proposed in some previous papers, which combine PN simulation capabilities with A＊ heuristic search within the PN reachability graph,may not find an optimum solution even with an admissible heuristic function. To remedy the defects an improved heuristic search strategy is proposed, which adopts a different method for selecting the promising markings and reserves the admissibility of the algorithm. To speed up the search process, another algorithm is also proposed which invokes faster termination conditions and still guarantees that the solution found is optimum. The scheduling results are compared through a simple FMS between our algorithms and the previous methods. They are also applied and evaluated in a set of randomly-generated FMSs with such characteristics as multiple resources and alternative routes.     

Improved methods f or scheduling flexible manufacturing systems based on Pet ri nets and heuristic search

This paper proposes and evaluates two improved Petri net (PN) - based hybrid search strategies and their applications to flexible manufacturing system (FMS) scheduling. The algorithms proposed in some previous papers ,which combine PN simulation capabilities with A 3 heuristic search within the PN reachability graph ,may not find an optimum solution even with an admissible heuristic function. To remedy the defects an improved heuristic search strategy is proposed ,which adopts a different method for selecting the promising markings and reserves the admissibility of the algorithm. To speed up the search process ,another algorithm is also proposed which invokes faster termination conditions and still guarantees that the solution found is optimum. The scheduling results are compared through a simple FMS between our algorithms and the previous methods. They are also applied and evaluated in a set of randomly- generated FMSs with such characteristics as multiple resources and alternative routes.     

Flow shop scheduling with blocking using modified harmony search algorithm with neighboring heuristics methods

The flow shop scheduling with blocking is considered an important scheduling problem which has many real-world applications. This paper proposes a new algorithm which applies heuristic techniques in harmony search algorithm (HSA) to minimize the total flow time. The proposed method is called modified harmony search algorithm with neighboring heuristics methods (MHSNH). To improve the initial harmony memory, we apply two heuristic techniques: nearest neighbor (NN) and constructive modified NEH (MNEH). A modified version of harmony search algorithm evolves to explore and generates a new solution. The newly generated solution is then enhanced by using neighboring heuristics. Lastly, another neighboring heuristic is applied to improve the obtained solution. The proposed algorithm is evaluated using 12 real-world problem instances each with 10 samples. The experimental evaluation is accomplished using two factors: CPU computational time and the number of iterations. For the first factor, comparative evaluation against six well-established methods shows that the proposed method achieves almost the best overall results in six problem instances out of the twelve and yields fruitful results for others. For the second factor, comparative evaluation against twelve well-regarded methods shows that the proposed method achieves the best overall results in three problem instances and obtains very good results in other instances. In a nutshell, the proposed MHSNH is an effective strategy for solving the job shop scheduling problem.     

Heuristic programming: A survey

This survey of heuristic programming starts by considering the nature of an algorithm as an automatic and precise process. A heuristic, by contrast, in an approximate and uncertain process, although algorithms and heuristics should be regarded as lying on a continuum. An examination of various applications of heuristics follows, showing the wide range of their use from game playing and logic theorem proving to shop floor scheduling and natural language programming.     

A hierarchical reliability-driven scheduling algorithm in grid systems

In a Grid computing system, many distributed scientific and engineering applications often require multi-institutional collaboration, large-scale resource sharing, wide-area communication, etc. Applications executing in such systems inevitably encounter different types of failures such as hardware failure, program failure, and storage failure. One way of taking failures into account is to employ a reliable scheduling algorithm. However, most existing Grid scheduling algorithms do not adequately consider the reliability requirements of an application. In recognition of this problem, we design a hierarchical reliability-driven scheduling architecture that includes both a local scheduler and a global scheduler. The local scheduler aims to effectively measure task reliability of an application in a Grid virtual node and incorporate the precedence constrained tasks’ reliability overhead into a heuristic scheduling algorithm. In the global scheduler, we propose a hierarchical reliability-driven scheduling algorithm based on quantitative evaluation of independent application reliability. Our experiments, based on both randomly generated graphs and the graphs of some real applications, show that our hierarchical scheduling algorithm performs much better than the existing scheduling algorithms in terms of system reliability, schedule length, and speedup.     

Minimizing total tardiness in a two-machine re-entrant flowshop with sequence-dependent setup times

We consider a two-machine re-entrant flowshop scheduling problem in which all jobs must be processed twice on each machine and there are sequence-dependent setup times on the second machine. For the problem with the objective of minimizing total tardiness, we develop dominance properties and a lower bound by extending those for a two-machine re-entrant flowshop problem (without sequence-dependent setup times) as well as heuristic algorithms, and present a branch and bound algorithm in which these dominance properties, lower bound, and heuristics are used. For evaluation of the performance of the branch and bound algorithm and heuristics, computational experiments are performed on randomly generated instances, and results are reported.     

Efficient constructive and composite heuristics for the Permutation Flowshop to minimise total earliness and tardiness

In this paper we address the problem of scheduling jobs in a permutation flowshop with a just-in-time objective, i.e. the minimisation of the sum of total tardiness and total earliness. Since the problem is NP-hard, there are several approximate procedures available for the problem, although their performance largely depends on the due dates of the specific instance to be solved. After an in-depth analysis of the problem, different cases or sub-problems are identified and, by incorporating this knowledge, four heuristics are proposed: a fast constructive heuristic, and three different local search procedures that use the proposed constructive heuristic as initial solution.The proposed Prod. Type: FLPheuristics have been compared on an extensive set of instances with the best-so-far heuristic for the problem, as well as with adaptations of efficient heuristics for similar scheduling problems. The computational results show the excellent performance of the proposed algorithms. Finally, the positive impact of the efficient heuristics is evaluated by including them as seed sequences for one of the best metaheuristic for the problem.     

Evolutionary algorithms for scheduling m-machine flow shop with lot streaming

This paper addresses the problem of making sequencing and scheduling decisions for n jobs–m-machines flow shops under lot sizing environment. Lot streaming (Lot sizing) is the process of creating sub lots to move the completed portion of a production sub lots to down stream machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. Evolutionary algorithms that belong to search heuristics find more applications in recent research. Genetic algorithm (GA) and hybrid genetic algorithm (HEA) also known as hybrid evolutionary algorithm fall under evolutionary heuristics. On this concern this paper proposes two evolutionary algorithms namely, GA and HEA to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set-up time. The following two algorithms are used to evaluate the performance of the proposed GA and HEA: (i) Baker's algorithm (BA), an optimal solution procedure for two-machine flow shop problem with lot streaming and makespan objective criterion and (ii) simulated annealing algorithm (SA) for m-machine flow shop problem with lot streaming and makespan and total flow time criteria.     

Several flow shop scheduling problems with truncated position-based learning effect

The concept of truncated position-based learning process plays a key role in production environments. However, it is relatively unexplored in the flow shop setting. In this paper, we consider the flow shop scheduling with truncated position-based learning effect, i.e., the actual processing time of a job is a function of its position and a control parameter in a processing permutation. The objective is to minimize one of the six regular performance criteria, namely, the total completion time, the makespan, the total weighted completion time, the discounted total weighted completion time, the sum of the quadratic job completion times, and the maximum lateness. We present heuristic algorithms and analyze the worst-case bound of these heuristic algorithms. We also provide the computational results to evaluate the performance of the heuristics.