企业级IT项目管理中多项目计划编制的研究

有效进行多项目管理是金融IT行业在IT项目管理中面对的关键问题。企业级项目管理需要在资源有限的情况下完成和企业战略密切相关的各项目的平衡和调度。如何确定各项目的优先级以及在优先级确定的情况下如何编制各项目的计划进度是多项目管理的核心和关键前提。首先分析了金融IT的特点,然后结合传统的甘特图计划方法,提出了一种优先级确定情况下项目计划编制简便方法,使得企业在实施多项目管理时能够参照进行计划安排,使得项目群整体最优从而提高企业核心竞争力。     

求解任务可拆分多项目协同调度问题的启发式算法

生产项目计划与调度过程中任务可以被拆分为更小粒度的子任务分批次执行,实现缩短项目总工期的优化目标.针对抢占式任务可拆分多项目调度问题,从协同优化角度探讨任务拆分与重组方式,提出一个长工期任务优先拆分、长工期项目优先拆分和高资源利用率项目优先拆分3种任务拆分优先级判断规则,设计一种求解任务可拆分多项目协同调度问题的启发式算法.最后通过数值实例和仿真分析验证了所提出方法在多项目调度总工期的优化效果和求解效率.     

Simulating the dynamic scheduling of project portfolios

A main challenge in project management is to provide methodologies that facilitate coordination among the projects in a portfolio or in a firm. Each incoming project in an existing portfolio affects its schedule, the resources availability and the planned performance. There are no analytical solutions for the problem of dynamic scheduling of resources for multiple projects in real time. Mathematical approaches, like integer programming or network based techniques, cannot describe the complexity of real problems (multi-projects environments have many interrelated elements), and have difficulties to adapt the analysis to dynamic changes.We propose a multi-agent system, where projects negotiate the procurement of resources through an auction mechanism all over the portfolio life. Both, projects and resources are modelled as agents. Projects demand resources for fulfilling their scheduled planned work, whereas resources offer their capabilities and workforce. This framework allows project portfolio management and the assessment in the decision of acceptance/rejection new projects.     

Multiagent based dynamic resource scheduling for distributed multiple projects using a market mechanism

The resource scheduling problem in a multi-project environment extends job-shop scheduling problems by allowing for task dependency and multiple self-interested entities. In this paper we deal with short-term scheduling of resources, which are shared by multiple projects. In specific, we address the dynamic nature of the situation. We model this as a dynamic economy, where the multiple local markets are established and cleared over time, trading resource time slots (goods). Due to the dynamic and distributed nature of the economy, through our approach we can achieve higher levels of flexibility, scalability and adaptability. Unlike most market-based mechanisms, which are based on equilibrium concepts and iterative adjustment of resources prices, we propose a novel market mechanism called precedence cost tâtonnement (P-TâTO), which solves individual resource-constrained local resource scheduling in an optimal way, and searches for a precedence conflict-free schedule through a tâtonnement type procedure. In this paper, we discuss our dynamic economy model and some details of the market mechanism along with empirical analysis results.     

Multi-Project Management in Software Engineering Using Simulation Modelling

Multi-project management is crucial in Software Engineering as it draws the resources from common pools, affects the completion date of other projects, determines the priority of use of resources among various projects, involves the judgment of multi-tasking of a common resource, and eventually, determines the success or failure of the projects. Hence, this paper argues that a formal simulation model using System Dynamics principles should be built to study the dynamics of software multi-project management. However, System Dynamics modelling by itself lacks the capability to construct the multi-project network, and thus confines the use of simulation in a single project environment. Thus, this paper is proposing an integration of the System Dynamics model with a multi-project network constructing method, called Critical Chain Project Management (CCPM). CCPM, not only constructs the network, but also recognizes the interdependencies of the multiple projects. However, the combination of these two principles does not simulate unexpected situations, change of policies and strategies that may be encountered during the project development. Hence, a Scenario model is proposed to be integrated with the System Dynamics and CCPM. With such integration, the project manager can identify the restraining factors in various possible scenarios in the multi-project environment, and provide feasible solutions to the senior management.     

多项目多任务选择计划模型及其免疫遗传算法

针对多资源受限下项目群选择计划及项目流程调度问题,依据项目的权重和承继约束,将项目群按权值从大到小进行排序,各项目内部的任务分别采用网络计划图进行拓扑排序,并以资源约束和合同期等因素为项目选择标准,建立多项目多任务选择计划的资源配置数学模型。进而,在遗传算法中引入免疫系统的记忆性和多样性功能,设计免疫遗传算法求解所获模型的最佳决策方案。比较性的数值实验结果说明了模型的合理性和算法的有效性。     

A random generator of resource-constrained multi-project network problems

Many scheduling problems in project management, manufacturing, and elsewhere require the generation of activity networks to test proposed solution methods. Single-network generators have been used for the resource-constrained project scheduling problem (RCPSP). Since the first single-network generator was proposed in 1993, several advances have been reported in the literature. However, these generators create only one network or project at a time; they cannot generate multi-project problems to desired specifications. This paper presents the first multi-network problem generator. It is especially useful for investigating the resource-constrained multi-project scheduling problem (RCMPSP), where a controlled set of multi-project test problems is crucial for analyzing the performance of solution methods. In addition to the single-project characteristics handled by existing network generators—such as activity duration, resource types and usage, and network size, shape, and complexity—the proposed generator produces multi-project portfolios with controlled resource distributions and amounts of resource contention. To enable the generation of projects with desired levels of network complexity, we also develop several theoretical insights on the effects of network topology on the probability of successful network generation. Finally, we generate 12,320 test problems for a full-factorial experiment and use analysis of means to conclude that the generator produces “near-strongly random” problems. Fully “strongly random” problems require much greater computational expense.     

基于BS-GA的资源约束多项目调度问题研究

针对资源约束的多项目调度问题(RCMPSP),考虑到项目、项目任务和资源各自之间的差异性,引入项目权重系数、活动质量因子和资源能力系数3个概念,提出了一个工期与质量的均衡优化模型.该模型根据资源的配置计划,确定了项目任务的资源平均能力系数,然后用项目权重系数和活动质量因子计算出多项目的单位工期时间内资源平均能力系数,利...     

A multi-agent system for the decentralized resource-constrained multi-project scheduling problem

A restart evolution strategy (RES) for the resource‐constrained project scheduling problem (RCPSP), as well as its integration in a multi‐agent system (MAS) for solving the decentralized resource‐constrained multi‐project scheduling problem (DRCMPSP) will be presented. To evaluate the developed approach, problem instances of the RCPSP taken from the literature with up to 300 activities are used, as well as 80 generated instances of the DRCMPSP, with up to 20 projects and with up to 120 activities each. For 73 instances of the RCPSP, the RES found better solutions than the best ones found so far. In addition, the MAS is suitable for solving large multi‐project instances decentrally. The results for the DRCMPSP instances show that the presented decentralized MAS is competitive with a central solution approach.     

Dialogue games that agents play within a society

Human societies have long used the capability of argumentation and dialogue to overcome and resolve conflicts that may arise within their communities. Today, there is an increasing level of interest in the application of such dialogue games within artificial agent societies. In particular, within the field of multi-agent systems, this theory of argumentation and dialogue games has become instrumental in designing rich interaction protocols and in providing agents with a means to manage and resolve conflicts. However, to date, much of the existing literature focuses on formulating theoretically sound and complete models for multi-agent systems. Nonetheless, in so doing, it has tended to overlook the computational implications of applying such models in agent societies, especially ones with complex social structures. Furthermore, the systemic impact of using argumentation in multi-agent societies and its interplay with other forms of social influences (such as those that emanate from the roles and relationships of a society) within such contexts has also received comparatively little attention. To this end, this paper presents a significant step towards bridging these gaps for one of the most important dialogue game types; namely argumentation-based negotiation (ABN). The contributions are three fold. First, we present a both theoretically grounded and computationally tractable ABN framework that allows agents to argue, negotiate, and resolve conflicts relating to their social influences within a multi-agent society. In particular, the model encapsulates four fundamental elements: (i) a scheme that captures the stereotypical pattern of reasoning about rights and obligations in an agent society, (ii) a mechanism to use this scheme to systematically identify social arguments to use in such contexts, (iii) a language and a protocol to govern the agent interactions, and (iv) a set of decision functions to enable agents to participate in such dialogues. Second, we use this framework to devise a series of concrete algorithms that give agents a set of ABN strategies to argue and resolve conflicts in a multi-agent task allocation scenario. In so doing, we exemplify the versatility of our framework and its ability to facilitate complex argumentation dialogues within artificial agent societies. Finally, we carry out a series of experiments to identify how and when argumentation can be useful for agent societies. In particular, our results show: a clear inverse correlation between the benefit of arguing and the resources available within the context; that when agents operate with imperfect knowledge, an arguing approach allows them to perform more effectively than a non-arguing one; that arguing earlier in an ABN interaction presents a more efficient method than arguing later in the interaction; and that allowing agents to negotiate their social influences presents both an effective and an efficient method that enhances their performance within a society.     

云环境下基于神经网络和群搜索优化的资源分配机制

在云环境下,各种闲置资源可以通过池化形成资源池,进而利用虚拟化技术将资源池中的不同资源组合以服务的形式提供给用户使用,因此需要合理而有效的机制来分配资源.针对云环境下资源的特点,将经济学和智能方法相结合,提出了一种基于双向组合拍卖的智能资源分配机制.在该机制中,提出了基于体验质量（quality ofexperience,简称QoE）的威望系统,引入威望衰减系数和用户信誉度,降低拍卖中恶意行为造成的影响,为资源交易提供QoE 支持.对拍卖中的竞价决策,综合考虑多种因素,提出了基于BP 神经网络的竞标价格决策机制,不仅可以合理确定竞标价,而且使价格可以动态适应市场变化.最后,由于组合拍卖胜标确定问题是NP 完全的,因此引入群搜索优化算法,以市场盈余和总体威望为优化目标,得到资源分配方案.仿真研究结果表明,该机制是可行和有效的.     

有限资源约束的产品开发多项目协同排程

针对有供应商参与的大批量制造企业产品协同开发特点,分析了多项目协同计划编制与设计资源在不同需求、不同时段、有无冲突等状态下的匹配情况,建立了多项目协同计划编制与资源颗粒约束匹配模型和实现算法,针对常见的计划与资源颗粒匹配时的无前置计划、有前置计划、计划与资源冲突等三种情况,提出了一种基于有限设计资源约束的新产品开发多项目协同计划编制方法,并用一实例对所建模型和计划编制方法进行了验证,证明提出的方法是可行的。     

Auction-based cooperation mechanism to parts scheduling for flexible job shop with inter-cells

This paper addresses cell part scheduling (CPS) problem. In this problem, parts may need to visit machines in different cells with consideration Inter-cell transportation time. The processing route of parts can be flexible. The objective is to minimize the overall process make-span. An integer nonlinear programming (INLP) model is formulated to determine the schedule scheme of all parts. An auction-based heuristic approach is proposed to solve it, which focuses on dealing with cooperation between different cells. In this approach, each cell can act as an auctioneer or a bidder. In an auction, it contains call for auction, bid construction, modify bids and winner announcement. A reference matrix is also applied in the auction to guarantee parts to finish as early as possible. Numerical experiments were conducted to test the auction-based approach. The results demonstrate the effectiveness, sensitivity and stability of the proposed auction-based approach, especially suitable for instances in large scale within a short calculating time.     

Designing a hybrid system dynamic model for analyzing the impact of strategic alignment on project portfolio selection

One of the key challenges in project organizations is the alignment of portfolio management with major corporate strategies. Usually, project-based organizations use shared resources to control and plan the project portfolio. Therefore, the exploitation of shared resources and project planning decisions made in this regard can change the progress of projects and affect the success rate of the projects. In this article, the integration of system dynamics with multi-objective decision making is applied to address project portfolio selection. The project portfolio has been modeled using four basic dimensions including technology, complexity, innovation and time sensitivity. The aim is to plan and control the progress of project portfolio while maximizing the strategic adaptation subject to the changes of the human resources. For this purpose, a two-stage MO-PSO with TOPSIS is proposed for portfolio selection problem that can solve real-world instances of the problem in a reasonable time. The result of the sensitivity analysis indicated that the proposed decision support system (DSS) provides insights into the impact of strategic alignment on project portfolio selection. According to the simulation results, the integrated methodology of this research can assist in choosing the suitable projects to achieve a project's strategic goals following the organization strategy.     

面向建材装备集团制造的分布式多项目资源调度

针对建材装备集团项目执行过程中存在的项目内和项目间多类别资源协同共用现象,提出并行调度机制下考虑多类别资源转移时间和转移成本的分布式多项目资源调度问题,以最小化资源转移成本和项目执行工期为目标建立问题的数学模型.为改善进化算法在局部搜索能力方面的不足,提出将禁忌搜索与进化算法相结合,构造一种内嵌禁忌搜索寻优搜索的多目标混合进化算法,在保证算法全局搜索能力的前提下提升局部精确搜索能力.同时,考虑资源转移成本和时间对任务选取的影响,改进任务选择的优先权值,提出并行调度机制下资源转移冲突消解策略.数据实验表明,所提算法能够有效避免不合理的资源转移,在求解质量方面具有良好的性能.     

基于NSGA-II算法的认知无线电频谱分配

认知无线电中,频谱拍卖是解决动态频谱分配的有效方法,其主要目的是最大化所有主用户的收益。然而,主用户间的收益是存在冲突的,给拍卖算法的优化带来了困难。为此,提出一种新的拍卖方案,来解决获胜者确定问题(Winner Determination Problem, WDP)。在该方案中,动态频谱拍卖问题被建模成多背包问题,并通过非支配排序遗传算法II (NSGA-II)得到最终的解决方案。最后,仿真实验结果表明,就解决WDP问题而言,与贪心算法相比,NSGA-II算法有更好的表现。     

A Petri Net Approach to Support Resource Assignment in Project Management

Petri nets have long been used in modeling and simulating project execution because of their great capability to describe concurrent activities and simulate the evolvement of processes. Although a number of extended Petri net models have been proposed to model and simulate resource sharing and activity dependence in projects, none of them has ever included a resource assignment mechanism into their models. Because resource assignments influence how limited resources are allocated among conflicting activities, they may heavily affect the availability of resources and the execution of projects. Therefore, a model without considering resource-sharing and resource assignment strategies may lead to a misunderstanding about project scheduling, resource consumption behaviors, and estimated project time. Accordingly, this paper proposes a new extended Petri net model that can describe how resources are shared and assigned among concurrent activities of multiple projects. The proposed model is named as resource assignment Petri net (RAPN), which extends an object composition Petri net with new places, transitions, attributes, and firing rules to model resource-sharing and resource assignment strategies. Finally, we prove that RAPN can correctly model the resource consumption behaviors of projects and can correctly compute the total elapsed time of projects.     

Handling shared resources in a temporal data base management system

This paper presents an approach to managing shared resources through the use of a temporal data base and special purpose reason maintenance system. All resource requests are processed through a central data base management system that assists employees in scheduling events to take advantage of available resources. A temporal data base is used to keep track of the condition and use of these resources. The system assists in resolving conflicts between employees competing for the same resources and detects when a change in the condition of a resource selected for a particular task makes it unsuitable for its intended purpose.     

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。