考虑柔性资源能力差异的项目调度问题

在资源受限项目调度问题中,将可再生资源进一步拓展为具有能力差异的柔性资源,建立考虑能力差异的柔性资源受限项目调度问题模型,该模型是对传统资源约束项目调度问题（RCPSP）更接近实际的拓展。为了求解该模型,提出一种基于活动序列表示的粒子群算法,在粒子解码过程中运用了基于优先规则的柔性资源-能力分配算法,在此基础上详细介绍了改进的串行调度生成方案与改进的并行调度生成方案、算法框架、选择性粒子更新方法。通过在改造的项目调度测试问题集上进行数值实验,证明了算法的可行性和有效性,其中使用改进串行调度生成方案与最匹配资源优先规则的粒子群算法具有较好的求解性能。     

多启发式规则融合粒子群算法的受限项目调度

在船舶生产的现实背景上,对船舶生产过程中如何利用总装平台这一瓶颈资源建立空间资源受限项目调度的问题模型。利用空间资源和分段任务对象的特性,在最大面积优先、最长边优先、BL（Bottom-Left,一种解决布局问题的启发式规则）规则等启发式规则的基础上,提出多启发式规则融合粒子群算法的空间资源受限项目调度算法。将分段任务对象根据几何特性和拖延惩罚因子赋予不同的权值,确定其实际开始时间,再通过最长边优先和BL规则确定其空间位置。设计了具有初始解集并且能够自动识别的粒子群算法,加速其收敛以更快更优地获取分段任务对象序列。通过和其他几种主流的空间调度方法（分支界定和遗传算法）进行不同规模的实验对比,得出该算法在时间复杂度和平均资源利用率方面都有所提高。     

柔性资源受限多项目调度的混沌粒子群算法研究

为解决柔性资源受限多项目的调度问题,有效实行资源分配和工作时间安排,采用混沌粒子群算法结合混合优先规则,形成优先规则序列。针对多项目问题,避免了传统方法将多个项目合并为一个项目,而是形成一个链表在项目的各工序间进行选择调度,并在初始化中嵌入混沌理论,在迭代过程中使用并行算法,有效避免了算法易陷入局部最优解的可能。改编标准库的多模式算例,对比多种算法下的结果,验证了该方法在求解该问题的可行性和有效性,对于项目管理中柔性资源受限问题具有实际应用价值。     

基于混沌粒子群算法的多目标多执行模式项目调度问题研究

在工程项目调度中保持工期、成本、质量以及资源的均衡控制是构成项目建设总目标的关键因素,关系到整个工程的成败。同时,鉴于基本粒子群算法容易陷入局部最优,提出一种将混沌算法嵌入基本粒子群的新算法,并将其用于求解多目标项目调度问题,通过建立工期、费用、资源和质量多目标综合优化模型,再运用基于优先规则的混沌粒子群算法解决该模型问题。最终通过实例计算表明:相对于基本的粒子群算法,混沌粒子群算法可以更为准确快速地解决该模型下的项目多目标多执行模式优化调度问题。     

粒子群优化算法在柔性资源受限项目调度中的研究

为了更有效地解决柔性资源受限项目调度问题,建立了速熟练度的技能供给矩阵,并应用混沌粒子群优化算 法来满足工序的先后约束关系,以在技能供给受限的情况下形成优先规则序列,根据串行进度生成机制形成该序列下 的最优解,运用嵌入混沌理论的粒子群优化算法更新种群,寻得全局最优解。实验结果验证了混沌粒子群优化算法求 解该问题的可行性和有效性,对于项目管理中柔性资源受限问题具有实际应用价值。     

基于优先规则的空间资源约束项目调度算法

任务组占用空间资源项目调度问题需满足组内任务的序关系和人力、设备等常规资源约束,以及空间资源这一特殊资源的约束,同时任务组之间也需满足上述约束,使得该调度问题异常复杂。以船舶建造分段制造问题为背景,建立任务组占用空间资源受限的项目调度问题数学模型,基于并行调度生成方案提出基于优先规则的启发式调度算法,实现对该调度问题的综合求解。实例测试结果表明了该算法的正解性和有效性。     

基于关键链的资源受限项目调度新方法

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。     

双目标无等待流水线调度的加权混合算法

针对最小化“总完工时间”和“最大完工时间”的双目标无等待流水线作业调度问题提出了一种粒子群加权混合优化算法,通过随机加权的方式将其转换成单目标问题,并应用基于升序排列的ROV（ranked-order-value）编码规则,将粒子群优化算法应用于无等待流水线作业调度问题。为了提高算法的性能,增强算法的搜索能力,提出的混合算法应用了NEH方法构造初始种群,在一个较好的初始值上进行粒子群优化,为防止种群陷入局部最优造成早熟,在粒子群每次迭代之后对全局最优解加入扰动并进行变邻域搜索。仿真实验结果表明该混合调度算法具有良好的性能。     

协同震荡搜索混沌粒子群求解资源受限项目调度问题

针对求解资源受限项目调度问题(RCPSP),提出了协同震荡搜索混沌粒子群(CSCPSO)算法。算法围绕种群粒子吸引子建立双向协同震荡搜索机制,该机制一方面使粒子向吸引子收敛,另一方面使粒子震荡调整自身与吸引子相邻维度大小关系不一致的维度,提升算法的搜索精度和种群的多样性。项目调度采用基于粒子的拓扑排序和串行项目进度生成机制,保证项目调度解决方案满足资源约束和紧前约束。采用具体算例对算法进行检验,结果表明该算法在求解RCPSP的精度和稳定性方面表现更优。     

基于GA-PSO的天基预警系统资源调度方法

为解决天基预警系统中的卫星资源调度问题,从预警任务特点出发,在对预警任务进行分解的基础上,建立了资源调度模型．结合传统遗传算法（GA）和粒子群算法（PSO）的优点,采用一种混合遗传粒子群（GA-PSO）算法来求解资源调度问题．该算法在解决粒子编解码问题的前提下,将遗传算法的遗传算子应用于粒子群算法,改善了粒子群算法的寻优能力．实验结果表明,提出的算法能有效解决多目标探测时天基预警系统的资源调度问题,调度结果优于传统粒子群算法和遗传算法．     

Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem

The studied resource-constrained project scheduling problem (RCPSP) is a classical well-known problem which involves resource, precedence, and temporal constraints and has been applied to many applications. However, the RCPSP is confirmed to be an NP-hard combinatorial problem. Restated, it is hard to be solved in a reasonable time. Therefore, there are many metaheuristics-based schemes for finding near optima of RCPSP were proposed. The particle swarm optimization (PSO) is one of the metaheuristics, and has been verified being an efficient nature-inspired algorithm for many optimization problems. For enhancing the PSO efficiency in solving RCPSP, an effective scheme is suggested. The justification technique is combined with PSO as the proposed justification particle swarm optimization (JPSO), which includes other designed mechanisms. The justification technique adjusts the start time of each activity of the yielded schedule to further shorten the makespan. Moreover, schedules are generated by both forward scheduling particle swarm and backward scheduling particle swarm in this work. Additionally, a mapping scheme and a modified communication mechanism among particles with a designed gbest ratio (GR) are also proposed to further improve the efficiency of the proposed JPSO. Simulation results demonstrate that the proposed JPSO provides an effective and efficient approach for solving RCPSP.     

基于改进粒子群算法的资源受限项目进度研究

资源受限的项目进度问题是经典的NP-hard问题,在研究以往求解方法的基础上,应用一种新的群智能算法——粒子群算法,对粒子群优化算法的搜索能力进行改进,结合Gbest模型与Pbest模型的优点,提出使粒子在搜索的前期有较强的全局搜索能力,尽可能多地发现可能全局最优的种子,而在搜索的后期则具有较强的局部搜索能力,用提高算法的收敛速度和精度的复合最优模型粒子群算法对RCPSP问题进行了求解,最后用文献[8]中的算例进行了仿真实验,实验结果验证了此算法的可行性与优越性。     

差分进化混合粒子群算法求解项目调度问题*

针对求解资源受限项目调度问题（RCPSP）,提出了基于差分进化（DE）的混合粒子群算法（PSODE）。通过在PSO种群和DE种群之间建立一种信息交流机制,使信息能够在两个种群中传递,以避免个体因错误的信息判断而陷入局部最优点。采用标准测试函数和具体算例进行检验,结果表明PSODE算法可以较好地解决RCPS问题。     

Fuzzy resource‐constrained project scheduling using taboo search algorithm

This paper proposes a mathematical model to deal with project scheduling problem under vagueness and a framework of a heuristic approach to fuzzy resource‐constrained project scheduling problem (F‐RCPSP) using heuristic and metaheuristic scheduling methods. Our approach is very simple to apply, and it does not require knowing the explicit form of the membership functions of the fuzzy activity times. We first identify two typical activity priority rules, namely, resource over time and minimum slack priority rules. They are used in the F‐RCPS problem and in the initial solution of Taboo search (TS) method. We improved the TS algorithm method for the solution of F‐RCPSP. Our objective is to check the performance of these rules and metaheuristic method in minimizing the project completion time for the F‐RCPS problems. In our study, we use trapezoidal fuzzy numbers (TraFNs) for activity times and activity‐on‐nodes (AON) representation and compute several project characteristics such as earliest, latest, and slack times in terms of TraFNs. The computational experiment shows that the performance of the proposed TS is better than the evaluation and light beam search algorithms in the literature. © 2012 Wiley Periodicals, Inc.     

Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem

This paper presents two solution representations and the corresponding decoding methods for solving the capacitated vehicle routing problem (CVRP) using particle swarm optimization (PSO). The first solution representation (SR-1) is a (n + 2m)-dimensional particle for CVRP with n customers and m vehicles. The decoding method for this representation starts with the transformation of particle into a priority list of customer to enter route and a priority matrix of vehicle to serve each customer. The vehicle routes are then constructed based on the customer priority list and vehicle priority matrix. The second representation (SR-2) is a 3m-dimensional particle. The decoding method for this representation starts with the transformation of particle into the vehicle orientation points and the vehicle coverage radius. The vehicle routes are constructed based on these points and radius. The proposed representations are applied using GLNPSO, a PSO algorithm with multiple social learning structures, and tested using some benchmark problems. The computational result shows that representation SR-2 is better than representation SR-1 and also competitive with other methods for solving CVRP.     

基于量子行为的粒子群优化算法分类规则获取*

介绍了基本的粒子群算法,并针对基本的粒子群算法在收敛性能上的缺陷,提出将具有量子行为的粒子群优化算法应用于数据挖掘学科中的分类规则获取。对加州大学厄文分校的若干数据集模式分类规则进行提取,与其他规则提取方法相比,证明该算法提高了分类规则的正确率以及全局寻优能力。     

A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery

This paper proposes a formulation of the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) and a particle swarm optimization (PSO) algorithm for solving it. The formulation is a generalization of three existing VRPSPD formulations. The main PSO algorithm is developed based on GLNPSO, a PSO algorithm with multiple social structures. A random key-based solution representation and decoding method is proposed for implementing PSO for VRPSPD. The solution representation for VRPSPD with n customers and m   vehicles is a (n+2m)$(n+2m)$-dimensional particle. The decoding method starts by transforming the particle to a priority list of customers to enter the route and a priority matrix of vehicles to serve each customer. The vehicle routes are constructed based on the customer priority list and vehicle priority matrix. The proposed algorithm is tested using three benchmark data sets available from the literature. The computational result shows that the proposed method is competitive with other published results for solving VRPSPD. Some new best known solutions of the benchmark problem are also found by the proposed method.     

自动变速器换档规则的粒子群优化提取方法

针对神经网络“黑箱”模型的缺陷,利用粒子群优化的换档规则提取算法,将规则编码为粒子的方法,通过粒子群优化算法的“位置-速度”搜索模型生成换档规则集。实验分析了标准粒子群与惯性递减粒子群在最优解搜索过程中的性能差异,并验证了该方法的有效性。     

一种求解TSP问题的粒子群算法改进设计

采用权重编码方案,将面向连续优化的粒子群优化算法应用于旅行商问题的求解,保留了粒子群算法的易操作性和高效性。针对粒子群算法易陷入局部最优的缺陷,提出了适合旅行商问题的基于k-means的改进措施。采用k-means对粒子群进行聚类分析,实现了粒子之间的信息交换,扩大了粒子的搜索空间,避免了算法陷入局部最优。