基于效率函数求解单件车间调度问题算法的完善

单件小批量生产形式下的单件车间（Job-shop)调度是生产计划中的一个重要问题，本文在文献[1]提出的求解Job-shop调度问题的动排算法及调解算法的基础上，做出了进一步的修改和完善，在调解算法中引入了交换与移动相结合的机制以提高调解效率，并在VBA For Projcet2000中实现了该算法，经分析及实验验证，利用这种算法求解Job-shop调度问题，可得到十分满意的结果。     

基于效率函数求解单件车间调度问题算法的软件实现和实例分析

单件小批量生产形式下的单件车间（Job-shop）调度是生产计划中的一个重要问题。西方在文献[1]提出的求解Job-shop调度问题的初排算法及调解算法的基础上，做出了进一步的修改和完善，在调解算法中引入了交换与移动相结合的机制以提高调解效率；在软件实现中引入了“虚工序”的概念，并在“VBA ForProject2000中实现了该算法。经分析及实验验证，利用这种算法求解Job-shop调度问题可得到十分满意的结果。     

A genetic algorithm for the Flexible Job-shop Scheduling Problem

In this paper, we present a genetic algorithm for the Flexible Job-shop Scheduling Problem (FJSP). The algorithm integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Computational result shows that the integration of more strategies in a genetic framework leads to better results, with respect to other genetic algorithms. Moreover, results are quite comparable to those obtained by the best-known algorithm, based on tabu search. These two results, together with the flexibility of genetic paradigm, prove that genetic algorithms are effective for solving FJSP.     

Job-shop调度问题的一种遗传算法表示方法

提出了一种新的染色体表示方法以及相应的遗传操作算子,它们与少许的调整工作相结合,既使得在每次操作算子作用之后产生的新的个体是合法的,也使染色体与时间表产生一一对应的关系,完满地解决了Job-shop问题中关键的表示和操作问题。     

A memetic algorithm for the job-shop with time-lags

The job-shop with time-lags (JS|t)$(\text{<italic>JS</italic>}|t)$ is defined as a job-shop problem with minimal and maximal delays between starting times of operations. In this article, time-lags between successive operations of the same job (JS|_ti,si)$(\text{<italic>JS</italic>}|t_{i,s_i})$ are studied. This problem is a generalization of the job-shop problem (null minimal time-lags and infinite maximal time-lags) and the no-wait job-shop problem (null minimal and maximal time-lags). This article introduced a framework based on a disjunctive graph to modelize the problem and on a memetic algorithm for job sequence generation on machines.     

基于遗传算法的Job-Shop调度问题求解方法

调度问题是许多计算机应用领域的重要问题,Job-Shop调度是其中的一类典型的困难问题,它通常包含多个可并行实现的目标以及实现这些目标的多种方法与资源.本文以一类实用的Job-Shop问题模型为基础,给出了用遗传算法求解调度问题应采用的染色体表示方法,并针对问题的特点,给出了面向资源空间与面向规划空间的遗传操作的设计思想与方法.实验结果表明,基于遗传算法的Job-Shop调度问题求解方法具有较好的性能,同时也表明,对于求解过程中可能出现的提前收敛问题可通过改变遗传操作概率及调节适应度等方法予以解决.     

一种动态分布式约束优化问题协同求解算法

多Agent协作过程中的许多问题都可在分布式约束优化问题(DCOP)框架下建模,但多局限于规划问题,且一般需Agent具有完全、准确收益函数.针对DCOP局限性,定义动态分布式约束优化问题(DDCOP),分析求解它的两个关键操作:Exploration和Exploitation,提出基于混沌蚂蚁的DDCOP协同求解算法(CA-DDCOP).该算法借鉴单只蚂蚁的混沌行为和蚁群的自组织行为,实现Exploration和Exploitation,根据玻尔兹曼分布,建立平衡Exploration和Exploitation的协同方法.通过多射频多信道无线AdHoc网络的信道分配验证该算法的有效性.     

一种基于图分解的几何约束求解方法

为了提高几何约束求解的效率和鲁棒性 ,对基于图的构造方法进行了改进 ,即加入虚约束进行扩展和过约束问题的一致性判定 ,提出了一种基于图分解的方法 ,用此方法可以处理包括完全约束、过约束和欠约束等多种情况的约束求解问题 ,另外 ,在该方法中还通过引入分解树将约束求解的范围由整体下降到局部 ,使大部分求解过程能够采用几何求解实现 ,提高了求解和后续修改的效率 ,通过实验数据测试证明 ,该方法对于大型约束求解问题可以达到实时处理的效果 ,具有较强的实用性     

一种求解置换Flow Shop调度问题的DRPFSP算法

针对置换Flow Shop调度问题,在对经典启发式算法进行研究的基础上,提出了一种用于求解此类问题的DRPFSP算法。算法首先对加工时间矩阵A进行数据标准化处理;然后通过引入一个概率矩阵P2×m和相应的降维函数f_p(A)=PA,将含有m台机器的原问题转化为含2台机器的新问题;再运用Johnson算法对新问题进行求解得到一个调度序列π⁰;最后结合插入邻域快速评价法对π⁰进行处理以获得原问题的一个调度方案π。实验结果表明,相对于经典的启发式算法,DRPFSP算法能更有效地对置换Flow Shop调度问题进行求解。     

求解总拖期时间最小化流水车间调度问题的多智能体进化算法

针对总拖期时间最小化的置换流水车间调度问题（Total tardiness permutation flow-shop scheduling problem） 提出了一种基于多智能体的进化搜索算法. 在该算法中,采用基于延迟时间排序的学习搜索策略（Tardiness rank based learning）,快速产生高质量的新个体,并根据概率更新模型进行智能体网格的更新进化. 同时通过实验设计的方法探讨了算法参数设置对算法性能的影响. 为了验证算法的性能,求解了Vallada标准测试集中540个测试问题,并将测试结果与一些代表算法进行比较,验证了该算法的有效性.     

一种求解多处理机调度问题的a-平坦化调度算法

在分析多处理机调度问题的基础上,提出了α-平坦的概念,并将其引入到多处理机调度问题中;基于此,提出了一种新的基于α-平坦的求解多处理机调度问题的算法。算法首先对作业集合做平坦化处理,然后再对处理后所得的新问题进行求解,最终获得原调度问题的一个近似解。实验结果表明,通过该算法可以求得较好的结果,相对于其它启发式算法,该算法具有较好的稳定性。     

A semi-Automatic Approach for Parallel Problem Solving using the Multi-BSP Model

Programming and Computer Software - The Multi-Bulk Synchronous Parallel (Multi-BSP) model is a recently proposed parallel programming model for multicore machines that extends the classic Bulk...     

A Fast and Accurate Technique for Mapping Parallel Applications on Stream-Oriented MPSoC Platforms with Communication Awareness

The problem of allocating and scheduling precedence-constrained tasks on the processors of a distributed real-time system is NP-hard. As such, it has been traditionally tackled by means of heuristics, which provide only approximate or near-optimal solutions. This paper proposes a complete allocation and scheduling framework, and deploys an MPSoC virtual platform to validate the accuracy of modelling assumptions. The optimizer implements an efficient and exact approach to the mapping problem based on a decomposition strategy. The allocation subproblem is solved through Integer Programming (IP) while the scheduling one through Constraint Programming (CP). The two solvers interact by means of an iterative procedure which has been proven to converge to the optimal solution. Experimental results show significant speed-ups w.r.t. pure IP and CP exact solution strategies as well as high accuracy with respect to cycle-accurate functional simulation. Two case studies further demonstrate the practical viability of our framework for real-life applications.     

用并行遗传算法解决带约束并行多机调度问题

遗传算法是一种全局优化的数值计算方法,它存在自然并行性,本文提出了一种解带约束并行多机调度问题的主从式控制网络并行遗传算法,并在PVM环境下实现。计算结果表明,并行遗传算法是有效的,且能适用于大规模并行多机调度问题。     

用遗传算法求解可变机器约束的Job-shop调度问题

描述了可变机器约束的Job—shop调度问题模型，并提出了一种基于遗传算法的调度算法进行求解．采用了一种新的基于操作的染色体编码方式，用二维矩阵的形式在机器的表达形式上扩展了传统基于操作的编码方式．在进化过程中设计了一种改变算子附加信息方法的操作，用于扩展种群的变化方式和算法的搜索范围．最后，分别以满足交货期和总加工时间最小为调度目标进行了数值计算，表明了该方法的有效性．     

约束满足自适应神经网络求解车间调度问题

提出一种基于约求满足的自适应神经网络方法求解车间作业调度问题。在该算法中,神经网络在运行过程中能够根据问题的约束类型、约束满足情况、启发式规则的选择来自适应调节神经元之间的连接权值,从而求得问题的可行解。仿真实验证明了算法的有效性。     

A new constraint programming approach for the orthogonal packing problem

The two-dimensional orthogonal packing problem (2OPP) consists in determining if a set of rectangles can be packed in a larger rectangle of fixed size. We propose an exact method for 2OPP, based on a new constraint-based scheduling model. We provide a generalization of energetic reasoning techniques for the problem under investigation. Feasibility tests requiring the solution of subset-sum problems are described. Computational results confirm the efficiency of our method compared to others in the literature.     

A hybrid approach to the single line scheduling problem with multiple products and sequence-dependent time

The present paper presents a hybrid approach for solving manufacturing scheduling problems, based on the integration between Constraint Logic Programming (CLP) and Genetic Algorithm (GA) approaches. The proposed methodology is applied to a single line with multiple products and sequence-dependent time. This system model derives from a real case of a company producing sheets for catalytic converters. A sensitivity analysis of the hybrid methodology is carried out to compare the performance of the CLP, GA and integrated CLP–GA approaches.     

A constraint programming approach to the multiple-venue,sport-scheduling problem

In this paper, we consider the problem of scheduling sports competitions over several venues which are not associated with any of the competitors. A two-phase, constraint programming approach is developed, first identifying a solution that designates the participants and schedules each of the competitions, then assigning each competitor as the “home” or the “away” team. Computational experiments are conducted and the results are compared with an integer goal programming approach. The constraint programming approach achieves optimal solutions for problems with up to sixteen teams, and near-optimal solutions for problems with up to thirty teams.     

A new ant colony algorithm for makespan minimization in permutation flow shops

The problem of scheduling in permutation flow shop with the objective of minimizing the maximum completion time, or makespan, is considered. A new ant colony optimization algorithm is developed for solving the problem. A novel mechanism is employed in initializing the pheromone trails based on an initial sequence. Moreover, the pheromone trail intensities are limited between lower and upper bounds which change dynamically. When a complete sequence of jobs is constructed by an artificial ant, a local search is performed to improve the performance quality of the solution. The proposed ant colony algorithm is applied to Taillard’s benchmark problems. Computational experiments suggest that the algorithm yields better results than well-known ant colony optimization algorithms available in the literature.