Experimental Evaluation of Agent-based Approaches to Solving Multi-mode Resource-Constrained Project Scheduling Problem

ABSTRACT

In this paper, a rank-based nonparametric statistical test for measuring the effect of cooperation between optimization agents solving the multi-mode resource-constrained project scheduling problem is presented. To solve this NP-hard optimization problem, different methods are applied including population- and agent-based approaches. One of them is a team of asynchronous agents composed of multiple optimization agents, management agents, and common memories, which through interactions produce solutions of hard optimization problems. Optimization agents represent different methods including local search, path relinking, or tabu search. Interactions are managed through various cooperation strategies based on applying heuristics, reinforcement learning, or population learning.     

求解资源受限项目调度问题的约束规划/数学规划混合算法

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．     

资源约束系统的信息调度及其渐近稳定性

对于通信约束的网络控制系统,应用通信序列的概念和混合逻辑动态的构架,将其建模成一类集成控 制和调度的资源约束离散线性周期系统．给出了这类系统网络信息的可调度条件,并基于线性矩阵不等式设计动态 反馈控制律．仿真表明,该方法能够在网络信息的周期调度下使闭环系统渐近稳定．     

面向资源受限用户的高效动态数据审计方案

物联网(IoT)设备推动着云存储外包数据服务的快速发展,从而使云存储外包数据服务得到越来越多终端用户的青睐,因此如何确保云服务器中用户数据的完整性验证成为一个亟待解决的热点问题.针对资源受限的用户,目前的云数据审计方案存在运算复杂、开销高和效率低等问题.为了解决这些问题,提出一个面向资源受限用户的高效动态数据审计方案....     

Hybrid genetic algorithm for bi-objective resource-constrained project scheduling

In this study, we considered a bi-objective, multi-project, multi-mode resource-constrained project scheduling problem. We adopted three objective pairs as combinations of the net present value (NPV) as a financial performance measure with one of the time-based performance measures, namely, makespan (C_max), mean completion time (MCT), and mean flow time (MFT) (i.e., minC_max/maxNPV, minMCT/maxNPV, and minMFT/maxNPV). We developed a hybrid non-dominated sorting genetic algorithm II (hybrid-NSGA-II) as a solution method by introducing a backward–forward pass (BFP) procedure and an injection procedure into NSGA-II. The BFP was proposed for new population generation and post-processing. Then, an injection procedure was introduced to increase diversity. The BFP and injection procedures led to improved objective functional values. The injection procedure generated a significantly high number of non-dominated solutions, thereby resulting in great diversity. An extensive computational study was performed. Results showed that hybrid-NSGA-II surpassed NSGA-II in terms of the performance metrics hypervolume, maximum spread, and the number of non-dominated solutions. Solutions were obtained for the objective pairs using hybrid-NSGA-II and three different test problem sets with specific properties. Further analysis was performed by employing cash balance, which was another financial performance measure of practical importance. Several managerial insights and extensions for further research were presented.     

A scatter search heuristic for maximising the net present value of a resource-constrained project with fixed activity cash flows

In this paper, we present a meta-heuristic algorithm for the resource-constrained project scheduling problem with discounted cash flows. We assume fixed payments associated with the execution of project activities and develop a heuristic optimisation procedure to maximise the net present value of a project subject to the precedence and renewable resource constraints. We investigate the use of a bi-directional generation scheme and a recursive forward/backward improvement method from literature and embed them in a meta-heuristic scatter search framework. We generate a large dataset of project instances under a controlled design and report detailed computational results. The solutions and project instances can be downloaded from a website in order to facilitate comparison with future research attempts.     

采用改进的细菌觅食优化算法求解 RCPSP

针对细菌觅食优化算法全局搜索能力较弱和收敛速度慢的问题,对算法的更新方式进行改进,在算法的初期通过粒子群算法进行全局搜索,使细菌在更新时感知周围环境,再由细菌觅食算法的趋向操作进行局部搜索,提高算法的计算精度和搜索能力。最后运用实例对算法进行验证,实验结果验证了此算法在求解资源受限的项目调度问题时的可行性和优越性。     

人工蜂群算法求解资源受限项目调度问题

针对资源受限项目调度问题,提出了一种基于人工蜂群算法的优化方法。人工蜂群算法中每个食物源的位置代表一种项目任务的优先权序列,每个食物源的位置通过扩展串行调度机制转换成可行的调度方案,迭代中由三种人工蜂执行不同的操作来实现全局最优解的更新。实验结果表明,人工蜂群算法是求解资源受限项目调度问题的有效方法,同时扩展调度机制的引入可以加速迭代收敛的进程。     

基于BIM的资源受限项目调度集成优化

建筑信息模型（BIM）发展迅速,已成为工程管理与项目管理领域研究的热点。为了实现项目调度信息集成化管理与共享,将项目调度优化与BIM技术相结合,提出了基于BIM的资源受限项目调度集成优化方法。利用BIM技术、项目调度理论以及优化算法,设计了基于BIM的资源受限项目调度集成优化框架。该框架将BIM三维模型扩展到项目调度维,采用遗传算法进行项目工期优化,利用BIM进行项目调度优化的动态模拟。通过案例验证了集成优化方法的有效性,实现了调度过程的4D动态模拟,为现场施工调度管理提供指导。     

一种求解资源受限项目调度问题的差分进化-布谷鸟搜索算法

提出一种新颖的求解资源受限项目调度问题的差分进化-布谷鸟搜索(DE-CS)算法。DE-CS算法以全局搜索能力强的布谷鸟搜索算法为总框架,通过嵌入具有记忆搜索功能的差分进化算法,结合不同的个体更新操作,增加种群的多样性以改善算法收敛性。采用任务优先级编码和串行进度生成机制来求解PSPLIB问题库中J30、J60、J120的全部480、480和600个问题,以验证算法的有效性,并与多种算法调度结果进行对比。数值试验和算法比较验证了DE-CS算法求解RCPSP问题的有效性。