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1.
具有优先关系的累积调度问题的约束传播算法   总被引:2,自引:0,他引:2  
约束传播是约束规划成功应用的关键技术之一. 针对累积调度问题提出一种结合工作间优先关系和工作最早开始/最晚完成时间约束的约束传播算法, 给出了算法的理论依据. 引用资源受限项目调度问题库PSPLIB中的典型问题对算法进行了测试, 结果表明: 针对测试问题新的约束传播算法在总体约减效果上优于现有约束传播算法, 新算法与基于能量推理的约束传播算法可以互补, 两者结合推理效果更好.  相似文献   

2.
多执行模式项目调度问题的克隆选择优化   总被引:1,自引:0,他引:1  
针对多执行模式资源受限项目调度问题的具体特点,采用基于调度的编码方式、半随机的初始种群产生方式、受限变异等策略,提出一种克隆选择优化算法用于求解项目活动的最优调度以使整个工程工期最短.该方法将问题特性与免疫克隆选择算法所具有的全局搜索特性、解的多样性和不易早熟的特点相融合,在能获得最优解的前提下,使得所花费的代价大为降低.通过对标准测试库PSPLIB中调度问题的仿真实验表明,该算法具有良好的性能,对于各组测试集中的大部分问题都能在合理的时间内寻找到最优解.与其它启发式算法相比,该方法具有更优的性能.  相似文献   

3.
研究多次抢占式资源受限的项目调度问题,假设任意时间点可作为资源抢占节点且抢占次数不受限制,建立满足多次资源抢占的线性整数规划模型并提出改进遗传算法对其进行求解。为克服遗传算法(GA)局部搜索能力缺陷,在算法中引入禁忌搜索(TS)进一步优化子代。针对性地设计了允许多次抢占的基于工作优先级编码策略以及串行调度方案生成机制。通过测试算例集实验调试算法参数,并以标准算例集(Project Scheduling Problem Library,PSPLIB)对算法进行可行性检验。实验结果表明,资源受限项目调度问题中引入多次抢占机制能有效缩减项目工期,设计的算法对问题求解效果良好。  相似文献   

4.
陈旺  史彦军  滕弘飞 《计算机工程》2011,37(14):134-136
针对大规模资源受限项目调度问题计算复杂的特点,提出一种合作式协同进化分布估计算法(CCEDA)。将合作式协同进化框架与分布估计算法相结合,将复杂问题分解为子问题,利用改进的分布估计算法对每个子问题进行协同优化求解。为提高分布估计算法的局部搜索能力,给出一种对解进行局部搜索的方法。将CCEDA用于求解标准问题库PSPLIB,并与GAPS、GA-DBH、GA-hybrid与GA-FBI算法进行比较,结果证明CCEDA拥有更好的求解性能。  相似文献   

5.
通过分析多模式项目调度问题的特点,提出一种主、从递阶结构的蚁群粒子群求解算法。算法中,主级为蚁群算法,完成任务模式选择;从级为粒子群算法,完成主级约束下的任务调度。然后,以工期最小和资源均衡分配为目标设计蚂蚁转移概率、模式优选概率和任务优选概率。最后,针对PSPLIB中的测试集对算法主要参数进行优化,并通过与其他算法比较验证了算法的有效性。  相似文献   

6.
蜂群算法作为一种较为新颖的启发式算法已经在多种类型的优化问题求解过程中表现了优秀的性能.针对蜂群算法在项目调度问题中的模型求解资源受限的问题,提出对求解方法进行改进,采用人工蜂群算法和蜂群优化算法两类典型的蜂群算法,对资源受限项目调度问题进行优化设计,并在benchmark上进行仿真并与传统的调度优化算法进行比较.实验结果表明,新设计的两类蜂群算法在调度成功率和收敛速度方面均有更好表现,人工蜂群算法求解的质量方面更优,蜂群算法在收敛速度上更具有优势.  相似文献   

7.
资源约束项目的改进差分进化参数控制及双向调度算法   总被引:1,自引:0,他引:1  
针对资源约束项目调度组合优化难题,提出一种改进的动态差分进化参数控制及双向调度算法.通过参数时变衰减与个体优劣评价,自适应控制个体进化参数,提高算法的收敛性能、勘探与开发最优解的能力;基于动态差分进化(Dynamic differential evolution, DDE),提出一种双向调度算法,使用满足任务时序约束的优先数编码、交替正向反向调度,结合标准化编码调整与精英保留的种群随机重建策略,建立了一种高效稳健的双向编码调整机制.通过著名的项目调度问题库(Project scheduling problem library, PSPLIB)中实例集测试,并与其他文献算法比较最优解平均偏差率,验证了所提算法的有效性与优越性.  相似文献   

8.
为了更好测试和比较项目调度问题求解算法的性能,通常需要利用测试问题集对相关算法进行测试和比较。对现有测试问题集的研究进行综述,并重点介绍国际上常用的两套标准问题集(Patterson问题集和PSPLIB标准问题库)和两款用于生成问题集的软件(单项目调度问题集生成器RanGen和多项目调度问题集生成器RCMPSP),最后,提出项目调度问题中选取问题集的一般流程以及构建问题集的一般方法,并通过实例说明该问题集选取方法的有效性及应用前景。  相似文献   

9.
采用优先规则的粒子群算法求解RCPSP   总被引:1,自引:0,他引:1       下载免费PDF全文
优先规则是解决大规模资源受限的项目调度问题(Resource-Constrained Project Scheduling Problem,RCPSP)强有力的方法,但是单一的优先规则的往往仅在某些特定的问题上表现出良好的性能。以粒子群算法为基础,提出了基于优先规则编码的粒子群算法(Priority Rule based Particle Swarm Optimization,PRPSO),求解资源受限的项目调度问题。该方法能够通过粒子群算法搜索优先规则和调度生成方案的组合。分别对PRPSO采用串行调度方案、并行调度方案和混合调度方案时,不同任务数和资源强度的问题实例进行了分析。通过对PSPLIB进行测试,结果表明该方法与其它基于优先规则的启发式方法相比有较低的偏差率,因而有较好的性能。  相似文献   

10.
基于关键链的资源受限项目调度新方法   总被引:25,自引:0,他引:25  
针对资源受限项目调度问题(RCPSPs)的实际需求建立了多目标优化调度模型,综合运用现有研究成果,设计了基于关键链的项目调度方法。该方法首先采用基于优先规则的启发式算法生成工期最小的近优项目计划,再在该计划中嵌入输入缓冲和项目缓冲,保证项目计划在非确定环境下的稳定执行。论文引用RCPSPs的标准问题库PSPLIB中大量案例对算法进行了的仿真试验,结果表明本文方法较传统项目调度方法有很大改进,论文最后对仿真结果进行了深入讨论,并指出了未来的研究方向。  相似文献   

11.
Ant colony optimization for resource-constrained project scheduling   总被引:8,自引:0,他引:8  
An ant colony optimization (ACO) approach for the resource-constrained project scheduling problem (RCPSP) is presented. Several new features that are interesting for ACO in general are proposed and evaluated. In particular, the use of a combination of two pheromone evaluation methods by the ants to find new solutions, a change of the influence of the heuristic on the decisions of the ants during the run of the algorithm, and the option that an elitist ant forgets the best-found solution are studied. We tested the ACO algorithm on a set of large benchmark problems from the Project Scheduling Library. Compared to several other heuristics for the RCPSP, including genetic algorithms, simulated annealing, tabu search, and different sampling methods, our algorithm performed best on average. For nearly one-third of all benchmark problems, which were not known to be solved optimally before, the algorithm was able to find new best solutions  相似文献   

12.
Constraint Satisfaction Problems (CSP) belong to a kind of traditional NP-hard problems with a high impact on both research and industrial domains. The goal of these problems is to find a feasible assignment for a group of variables where a set of imposed restrictions is satisfied. This family of NP-hard problems demands a huge amount of computational resources even for their simplest cases. For this reason, different heuristic methods have been studied so far in order to discover feasible solutions at an affordable complexity level. This paper elaborates on the application of Ant Colony Optimization (ACO) algorithms with a novel CSP-graph based model to solve Resource-Constrained Project Scheduling Problems (RCPSP). The main drawback of this ACO-based model is related to the high number of pheromones created in the system. To overcome this issue we propose two adaptive Oblivion Rate heuristics to control the number of pheromones: the first one, called Dynamic Oblivion Rate, takes into account the overall number of pheromones produced in the system, whereas the second one inspires from the recently contributed Coral Reef Optimization (CRO) solver. A thorough experimental analysis has been carried out using the public PSPLIB library, and the obtained results have been compared to those of the most relevant contributions from the related literature. The performed experiments reveal that the Oblivion Rate heuristic removes at least 79% of the pheromones in the system, whereas the ACO algorithm renders statistically better results than other algorithmic counterparts from the literature.  相似文献   

13.
We propose an efficient hybrid algorithm, known as ACOSS, for solving resource-constrained project scheduling problems (RCPSP) in real-time. The ACOSS algorithm combines a local search strategy, ant colony optimization (ACO), and a scatter search (SS) in an iterative process. In this process, ACO first searches the solution space and generates activity lists to provide the initial population for the SS algorithm. Then, the SS algorithm builds a reference set from the pheromone trails of the ACO, and improves these to obtain better solutions. Thereafter, the ACO uses the improved solutions to update the pheromone set. Finally in this iteration, the ACO searches the solution set using the new pheromone trails after the SS has terminated. In ACOSS, ACO and the SS share the solution space for efficient exchange of the solution set. The ACOSS algorithm is compared with state-of-the-art algorithms using a set of standard problems available in the literature. The experimental results validate the efficiency of the proposed algorithm.  相似文献   

14.
利用约束规划(constraintprogramming,CP)与数学规划(mathematicalprogramming,MP)结合的方法求解调度问题已经获得了一些较好的研究成果,正成为调度问题研究领域的一个新的热点研究方向.本文针对求解资源受限项目调度问题(RCPSP)的整数规划模型,设计了基于CP技术的问题和模型预处理方法,证明了整数规划模型的有效不等式定理,提出了通过将项目子网络图转化为加权最大团问题求解后获得有效不等式的方法.引用标准问题库PSPLIB中的一组典型问题进行求解实验,结果表明本文提出的有效不等式可以明显改进模型的求解质量和时间性能.论文最后对实验结果进行了深入讨论,讨论了未来的研究方向.  相似文献   

15.
The resource-constrained project scheduling problem (RCPSP) is an NP-hard optimization problem. RCPSP is one of the most important and challenging problems in the project management field. In the past few years, many researches have been proposed for solving the RCPSP. The objective of this problem is to schedule the activities under limited resources so that the project makespan is minimized. This paper proposes a new algorithm for solving RCPSP that combines the concepts of negative selection mechanism of the biologic immune system, simulated annealing algorithm (SA), tabu search algorithm (TS) and genetic algorithm (GA) together. The performance of the proposed algorithm is evaluated and compared to current state-of-the-art metaheuristic algorithms. In this study, the benchmark data sets used in testing the performance of the proposed algorithm are obtained from the project scheduling problem library. The performance is measured in terms of the average percentage deviation from the critical path lower bound. The experimental results show that the proposed algorithm outperforms the state-of-the-art metaheuristic algorithms on all standard benchmark data sets.  相似文献   

16.
Ant Colony Optimization (ACO) is a kind of metaheuristic that has become very popular for solving problems from combinatorial optimization. Solutions for a given problem are constructed by a random walk on a so-called construction graph. This random walk can be influenced by heuristic information about the problem. In contrast to many successful applications, the theoretical foundation of this kind of metaheuristic is rather weak. Theoretical investigations with respect to the runtime behavior of ACO algorithms have been started only recently for the optimization of pseudo-Boolean functions.We present the first comprehensive rigorous analysis of a simple ACO algorithm for a combinatorial optimization problem. In our investigations, we consider the minimum spanning tree (MST) problem and examine the effect of two construction graphs with respect to the runtime behavior. The choice of the construction graph in an ACO algorithm seems to be crucial for the success of such an algorithm. First, we take the input graph itself as the construction graph and analyze the use of a construction procedure that is similar to Broder’s algorithm for choosing a spanning tree uniformly at random. After that, a more incremental construction procedure is analyzed. It turns out that this procedure is superior to the Broder-based algorithm and produces additionally in a constant number of iterations an MST, if the influence of the heuristic information is large enough.  相似文献   

17.
A variety of metaheuristic approaches have emerged in recent years for solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling. In this paper, we propose a Neurogenetic approach which is a hybrid of genetic algorithms (GA) and neural-network (NN) approaches. In this hybrid approach the search process relies on GA iterations for global search and on NN iterations for local search. The GA and NN search iterations are interleaved in a manner that allows NN to pick the best solution thus far from the GA pool and perform an intensification search in the solution's local neighborhood. Similarly, good solutions obtained by NN search are included in the GA population for further search using the GA iterations. Although both GA and NN approaches, independently give good solutions, we found that the hybrid approach gives better solutions than either approach independently for the same number of shared iterations. We demonstrate the effectiveness of this approach empirically on the standard benchmark problems of size J30, J60, J90 and J120 from PSPLIB.  相似文献   

18.
The multi-satellite control resource scheduling problem (MSCRSP) is a kind of large-scale combinatorial optimization problem. As the solution space of the problem is sparse, the optimization process is very complicated. Ant colony optimization as one of heuristic method is wildly used by other researchers to solve many practical problems. An algorithm of multi-satellite control resource scheduling problem based on ant colony optimization (MSCRSP–ACO) is presented in this paper. The main idea of MSCRSP–ACO is that pheromone trail update by two stages to avoid algorithm trapping into local optima. The main procedures of this algorithm contain three processes. Firstly, the data get by satellite control center should be preprocessed according to visible arcs. Secondly, aiming to minimize the working burden as optimization objective, the optimization model of MSCRSP, called complex independent set model (CISM), is developed based on visible arcs and working periods. Ant colony algorithm can be used directly to solve CISM. Lastly, a novel ant colony algorithm, called MSCRSP–ACO, is applied to CISM. From the definition of pheromone and heuristic information to the updating strategy of pheromone is described detailed. The effect of parameters on the algorithm performance is also studied by experimental method. The experiment results demonstrate that the global exploration ability and solution quality of the MSCRSP–ACO is superior to existed algorithms such as genetic algorithm, iterative repair algorithm and max–min ant system.  相似文献   

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