带有资源转移时间的RCPSP资源流模型及算法

本文在传统资源受限项目调度问题(resource-constrained project scheduling problem, RCPSP)中引入资源转移时间,为有效获得问题的最优解,采用资源流编码方式表示可行解,建立了带有资源转移时间的RCPSP资源流优化模型,目标为最小化项目工期。根据问题特征设计了改进的资源流重构邻域算子,分别设计了改进的禁忌搜索算法和贪心随机自适应禁忌搜索算法求解模型。数据实验结果表明,相较于现有文献中的方法,所提两种算法均可针对更多的项目实例求得最优解,并且得到最优解的时间更短,求解效率更高。此外,分析了算法在求解具有不同特征的项目实例时的性能,所得结果为项目经理结合项目特征评价算法适用性提供了指导。     

项目鲁棒调度的资源分配启发式算法研究

合理的资源配置是提高项目调度鲁棒性一种有效的方法。本文针对项目鲁棒调度问题,提出了Max-PRUA资源分配启发式算法,以期通过生成鲁棒性高的资源分配方案来提高调度计划的鲁棒性。本算法设计了最大化利用优先关系和不可避免弧传递资源的资源分配两项策略来传递最大资源量,以减少由额外约束传递的资源量,降低对项目调度鲁棒性的影响。为寻优最优资源分配方案,配合局部搜索算法,本算法构建了动态活动组GRA,通过对组内活动顺序重排以生成多种资源分配方案,以利于从解空间中寻优出最佳的鲁棒性方案。最后通过大量的仿真实验验证和与其它算法进行比较,结果表明本算法对于不同规模和不同因素影响的项目均有较好的适应性,生成的资源分配方案对调度计划鲁棒性影响较小,是一种有效的算法。     

求解资源受限项目调度问题的人工鱼群算法

提出了将人工鱼群算法应用于求解资源受限项目调度问题中的构想,建立了求解资源受限项目调度问题的人工鱼群算法模型,设计了一种标准随机键编码方式,构建了人工鱼的觅食行为、聚群行为、追尾行为和随机行为四种基本算子,采用了正向逆向局部改进技术和精英保留策略,并给出了算法流程。应用PSPLIB标准问题库对该算法进行了大量的测试,并与其他算法进行了比较,验证了该算法的有效性。     

Project Scheduling with Multiple Modes: A Genetic Algorithm

In this paper we consider the resource-constrained project scheduling problem with multiple execution modes for each activity and makespan minimization as objective. We present a new genetic algorithm approach to solve this problem. The genetic encoding is based on a precedence feasible list of activities and a mode assignment. After defining the related crossover, mutation, and selection operators, we describe a local search extension which is employed to improve the schedules found by the basic genetic algorithm. Finally, we present the results of our thorough computational study. We determine the best among several different variants of our genetic algorithm and compare it to four other heuristics that have recently been proposed in the literature. The results that have been obtained using a standard set of instances show that the new genetic algorithm outperforms the other heuristic procedures with regard to a lower average deviation from the optimal makespan.     

A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.     

广义优先关系约束下Max-npv项目调度问题及其遗传算法

以往Max-npv项目调度问题的研究都假定活动之间的关系为单一结束-开始类型,现实中活动之间关系复杂多变,因此,将广义优先关系引入Max-npv项目调度问题中,构建了广义优先关系约束下的Max-npv项目调度模型。针对该优化模型设计了一种双层遗传算法,外层遗传算法负责任务执行模式的优化,内层遗传算法负责任务调度的优化。在内层遗传算法中,采用任务开始时间之差作为新的编码方式,大大简化了交叉变异算子,针对网络图中的环状结构设计了修复算子,确保了编码的有效性。通过一个算例对算法进行了测试,实验结果验证了算法的有效性。     

LOB中基于工期决定因素分析的资源均衡算法研究

资源均衡是重复性项目中的经典调度问题,本文提出一种新的基于平衡线法(line of balance,LOB)的资源均衡方法。首先,本文提出LOB中关键路线的确定方法,确定关键路线及关键工序类型。而后,本文分析项目总工期的决定因素,对不同类型关键工序的特性及其与总工期、资源调整之间的关系进行了研究,论证了在LOB的资源均衡问题中,由于逆关键工序、点关键工序这些特殊工序的存在,可以在保证项目总工期不变的前提下,通过同时调整关键工序和非关键工序实现资源优化。按照这一思路,论文设计了LOB中资源均衡的遗传算法。算例分析表明该资源均衡算法的优化性能。本文提出的资源均衡思路和算法能帮助项目计划人员拓展资源优化空间,达到更好的资源均衡效果。     

考虑返修的建设项目时间-成本-质量问题

当施工过程中质量不达标需要返修以改进项目质量,但相应地会影响项目工期和成本。本文基于经典多模式资源受限项目调度问题构建一种新的非线性规划模型,目标为项目成本最小和工期最短,其中项目成本考虑返修成本以提高项目质量。首先,使用二元非独立正态分布函数描述活动质量,根据活动间的串联或并联关系定义隐蔽工程质量为活动质量的函数。其次,本文提出一种基于NSGA的混合蛙跳算法,采用串行进度产生方案和调整的活动列表编码,其中蛙跳过程结合了遗传算法中的交叉操作和基于置换的局域搜索。最后,整个模型算法应用于框架铁路立交桥施工项目,验证本文算法性能在支配解数量和质量上都优于标准NSGA。     

基于资源随机中断的反应性多模式项目调度优化

资源中断是项目实施过程中一种常见现象,它会导致项目进度计划的变更并引起额外的成本。本文研究资源随机中断下的项目调度问题,目标是对基准进度计划进行合理的调整,以最小化由此所造成的额外成本。作者首先对研究问题进行界定,随后构建问题的优化模型。针对模型的NP-hard属性,设计禁忌搜索启发式算法。最后以基准列表算法和随机生成算法为参照,在随机生成的标准算例集合上对算法进行测试,得到如下结论:在可接受的计算时间范围内,禁忌搜索获得的满意解质量明显高于其他两种启发式算法;算法的平均计算时间随着项目活动数的增加而增加,随着网络复杂度、资源强度或资源中断次数的增加而减小;满意解的平均目标函数值,随着项目活动数或网络复杂度的增加而增加,随着资源中断次数的增加而减小,与资源强度无明显关系。     

柔性工期下的资源受限项目调度双目标优化研究

在项目调度过程中,活动工期应根据项目截止工期以及资源供给情况进行合理设置,而在传统的资源受限项目调度问题(RCPSP)中,活动的工期往往是已知且固定的,这在一定程度上限制了项目调度的灵活性。多模式下的项目调度方式虽然弥补了这一缺点,但其提供的工期-资源组合种类固定且有限,并不一定能保证包含最优的工期-资源组合。本文将活动工期作为项目调度问题的决策变量,允许其在一定范围内取值。这种柔性工期调度方式虽然增加了项目调度难度,但提高了项目调度灵活性,同时可以起到压缩项目完工时间的作用。为验证柔性工期调度方式对项目工期和成本的影响,本文建立了工期-成本双目标权衡优化模型,设计了两阶段嵌套算法(NSGAⅡ-RS)对其求解,实验证明,柔性工期调度策略是一种鲁棒性较好的项目完工时间压缩策略。     

Using resource scarceness characteristics to solve the multi-mode resource-constrained project scheduling problem

In the past decades, resource parameters have been introduced in project scheduling literature to measure the scarceness of resources of a project instance. In this paper, we incorporate these resource scarceness parameters in the search process to solve the multi-mode resource constrained project scheduling problem, in which multiple execution modes are available for each activity in the project. Therefore, we propose a scatter search algorithm, which is executed with different improvement methods, each tailored to the specific characteristics of different renewable and nonrenewable resource scarceness values. Computational results prove the effectiveness of the improvement methods and reveal that the procedure is among the best performing competitive algorithms in the open literature.     

A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem

In this paper we present a genetic algorithm for the multi-mode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. We also introduce the preemptive extension of the problem which allows activity splitting (P-MRCPSP). To solve the problem, we apply a bi-population genetic algorithm, which makes use of two separate populations and extend the serial schedule generation scheme by introducing a mode improvement procedure. We evaluate the impact of preemption on the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that our procedure is amongst the most competitive algorithms.     

Scheduling non-professional table-tennis leagues

In this paper we consider a sports league scheduling problem which occurs in planning non-professional table-tennis leagues. The problem consists in finding a schedule for a time-relaxed double round robin tournament where different hard and soft constraints have to be taken into account. We model the problem as an integer linear program and a multi-mode resource-constrained project scheduling problem, respectively. Based on the second model a heuristic solution algorithm is proposed, which proceeds in two stages using local search and genetic algorithms. Computational results show the efficiency of the approaches.     

Multiple-Project Scheduling with Controllable Project Duration and Hard Resource Constraint: Some Solvable Cases

In many large-scale project scheduling problems, multiple projects are either taking place at the same time or scheduled into a tight sequence in order to efficiently share a common resource. One example of this is the computing resource allocation at an Application Service Provider (ASP) which provides data processing services for multiple paying customers. Typical services provided by ASPs are data mining, payroll processing, internet-based storage backup services and Customer Relation Management (CRM) services. The processing mode of an ASP can be either batch or concurrent, depending on the type service rendered. For example, for CPU intensive or long processing time required services, it would be more economical to processes one customer request at a time in order to minimize the context switching overhead. While the data transaction processes within a service request are subject to certain precedence relationships, the requests from different customers to an ASP are independent of each other, and the total time required to process a service request depends on the computing resource allocated to that request. The related issue of achieving an optimal use of resources at ASPs leads to problem of project scheduling with controllable project duration.In this paper, we present efficient algorithms for solving several special cases of such multi-project scheduling problems with controllable project duration and hard resource constraints. Two types of problems are considered. In type I, the duration of each project includes a constant and a term that is inversely proportional to the amount of resource allocated. In type II, the duration of each individual project is a continuous decreasing function of the amount of resource allocated.     

人力资源约束下的项目群调度问题建模与求解

基于人员胜任力是影响工作绩效的关键因素,将资源受限项目调度问题中的可更新资源通过一系列科学合理的方法或者手段转变为存在胜任力差异的人力资源,由此构建起一个强调胜任力差异的人力资源约束项目调度问题模型,此模型最突出的优势在于选取了能够客观合理评估人员胜任力的指标,提供了严谨科学的关系式,将复杂的多项目总工期与总成本的双目标最小化问题转换为综合指标单目标最大化问题,建立数学优化模型,采用遗传算法求解。通过算例研究证实,相较于传统多模式模型,基于人员胜任力水平差异的模型明显更胜一筹,其优势集中表现为最优工期更短、最优成本更低。考虑了胜任力差异的数学优化模型更符合研发项目群管理实践,同时遗传算法在求解方面不仅效率高,并且更容易获得客观准确的结果。     

Linear preselective policies for stochastic project scheduling

In the context of stochastic resource-constrained project scheduling we introduce a novel class of scheduling policies, the linear preselective policies. They combine the benefits of preselective policies and priority policies; two classes that are well known from both deterministic and stochastic scheduling. We study several properties of this new class of policies which indicate its usefulness for computational purposes. Based on a new representation of preselective policies as and/or precedence constraints we derive efficient algorithms for computing earliest job start times and state a necessary and sufficient dominance criterion for preselective policies.  A computational experiment based on 480 instances empirically validates the theoretical findings.     

A biased random-key genetic algorithm for the project scheduling problem with flexible resources

In this paper, we investigate a resource-constrained project scheduling problem with flexible resources. This is an \(\mathcal {NP}\)-hard combinatorial optimization problem that consists of scheduling a set of activities requiring specific resource units of several skills. The goal is to minimize the makespan of the project. We propose a biased random-key genetic algorithm for computing feasible solutions for the referred problem. We study different decoding mechanisms: an already existing method in the literature, a new adapted serial scheduling generation scheme, and a combination of both. The new procedure is tested using a set of benchmark instances of the problem. The results provide strong evidence that the new heuristic is robust and yields high-quality feasible solutions.     

不确定环境下基于时间、费用及鲁棒性权衡的多目标项目调度优化

项目调度中的时间和费用是两个重要的指标,而在不确定环境下进度计划的鲁棒性则是保证项目平稳实施的关键。本文研究不确定环境下的多目标项目调度优化问题,以优化项目的工期、鲁棒值和成本为目标安排各活动的开始时间。基于此,作者构建多目标项目调度优化模型,将模型分解为三个子模型分析目标间的权衡关系,然后设计非劣排序遗传算法进行求解,应用精英保留策略和基于子模型权衡关系的优化策略优化算法,进行算法测试和算例参数敏感性分析。最后,应用上述方法研究一个项目实例,计算得到非劣解集,实例的敏感性分析结果进一步验证了三个目标间的权衡关系,据此提出资源的有效利用策略。本文的研究可以为多目标项目调度制定进度计划提供定量化决策支持。     

A genetic algorithm for multi-mode resource constrained project scheduling problem

This article considers a general class of nonpreemptive multi-mode resource-constrained project scheduling problems in which activity durations depend on committed renewable resources (multi-mode time resource tradeoff). We propose a genetic algorithm for these problems and compare it with a stochastic scheduling method proposed by Drexl and Gruenewald. Computational results show that the proposed genetic algorithm is superior to the stochastic scheduling method.     

Solving resource-constrained project scheduling problem with evolutionary programming

This paper presents an evolutionary programming (EP)-based approach to solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling, with minimization of project duration as the objective subject to precedence and resource constraints. The individual representation of EP for the problem is based on random keys. The serial generation scheme is used in the decoding scheme to generate the project plan. Experimental analyses are presented to investigate the performance of the proposed EP-based methodology, including comparison of the four variants of EP, namely, CEP, FEP, MCEP and IMCEP, with each other and GA to find the best variant of EP for the RCPSP, and comparison of this best variant of EP (MCEP) with other approaches using the J30 standard instances set in PSPLIB. The computational results validate the effectiveness of the proposed algorithm.