考虑多种偏好信息的多阶段方案选择方法

针对方案选择过程中专家倾向于采用不同偏好信息的问题,提出了考虑多种偏好信息的方案选择方法。专家可以从方案集中选择出子集,从效用值、偏好次序、互反判断矩阵和模糊偏好关系四种形式中选择一种对方案子集进行评价;采用转化函数将专家给出的偏好信息转化为偏好向量;根据各个专家的偏好向量集结方案分类结果;将结果用于反馈调整各专家的方案子集;最后通过多个阶段的反馈与调整,改进群体一致性以获得最优方案。将该方法用于某汽车制造商汽车零部件再制造方案的选择,验证了所提方法的有效性。     

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。     

动态多项目选择计划管理及其免疫优化决策

针对资源受限情况下动态多项目选择计划管理问题,探讨其数学模型,并设计免疫优化算法对其求解。算法设计中,分别引入基因块的随机漂移与确定性漂移两种变异方式,以及个体的亲和选择与激励选择两种选择方案。最后,数值实验验证了模型的合理性以及各算法的有效性。     

多目标优化特征选择研究综述

特征选择是模式识别领域中有效的降维方法,当特征选择涉及到的多个目标彼此冲突,难以平衡时,将特征选择视为多目标优化问题是时下的研究热点。为方便研究者系统地了解多目标特征选择领域的研究现状和发展趋势,对多目标特征选择方法进行综述。阐明了特征选择和多目标优化的本质;根据多目标优化方法的区别和特点,重点对比剖析各类多目标优化特征选择方法的优劣势;讨论现有多目标优化特征选择研究方法存在的问题以及对未来的展望。     

多项目多工作选择模型及其动态克隆选择算法

针对多项目多工作选择计划问题,考虑多种资源的限制及项目内部工作数不同,将工期最短和资源均衡统筹考虑,采用加权的方法,建立改进的多项目多工作选择计划的数学模型。依据克隆选择原理,实施动态克隆、变异等操作,设计动态克隆选择算法对模型进行求解。通过数值实验,与基于优先规则的启发式算法进行比较,该算法具有良好的性能。     

基于标签共现关系的多标签特征选择

多标签数据广泛存在于现实世界中,多标签特征选择是多标签学习中重要的预处理步骤.基于模糊粗糙集模型,研究人员已经提出了一些多标签特征选择算法,但是这些算法大多没有关注标签之间的共现特性.为了解决这一问题,基于样本标签间的共现关系评价样本在标签集下的相似关系,利用这种关系定义了特征与标签之间的模糊互信息,并结合最大相关与最小冗余原则设计了一种多标签特征选择算法LC-FS.在5个公开数据集上进行了实验,实验结果表明了所提算法的有效性.     

基于强化学习的多技能项目调度算法

多技能项目调度存在组合爆炸的现象, 其问题复杂度远超传统的单技能项目调度, 启发式算法和元启发式 算法在求解多技能项目调度问题时也各有缺陷. 为此, 根据项目调度的特点和强化学习的算法逻辑, 本文设计了基 于强化学习的多技能项目调度算法. 首先, 将多技能项目调度过程建模为符合马尔科夫性质的序贯决策过程, 并依 据决策过程设计了双智能体机制. 而后, 通过状态整合和行动分解, 降低了价值函数的学习难度. 最后, 为进一步提 高算法性能, 针对资源的多技能特性, 设计了技能归并法, 显著降低了资源分配算法的时间复杂度. 与启发式算法的 对比实验显示, 本文所设计的强化学习算法求解性能更高, 与元启发式算法的对比实验表明, 该算法稳定性更强, 且 求解速度更快.     

基于信息熵的模糊多属性决策供应商选择方法

提出了一种针对供应商选择最优决策问题的基于信息熵的模糊多属性决策方法。针对供应商的多属性决策问题,确定属性集,利用信息熵求出各属性权重,运用模糊数排序方法对方案的模糊效用值进行排序,以确定最优方案。最后以某企业为例做了实例分析,将此方法应用于对这五个供应商进行评分排序,并从中选择出最为合适的供应商,表明此方法行之有效。     

考虑战略实现干扰和风险双层关联的项目组合风险应对策略选择模型

项目组合受项目组合风险影响无法有效支撑企业战略实现,企业需采取合适的风险应对策略以保证项目组合成功,但目前缺少项目组合风险应对策略选择相关方法.基于此,首先结合信息熵和球形模糊集分析并测度风险对战略实现的干扰程度;然后使用网页链接分析方法和决策实验与评价实验室方法对项目组合中双层风险间关联强度进行分析测度;接着根据上述过程确定风险优先级,引用组合心理账户和后悔理论建立以决策者综合效用值最大化的项目组合风险应对决策模型;最后通过案例分析验证所提出方法的有效性和实用性.结果表明:1)风险对战略实现干扰程度以及风险间关联关系对应对策略选择均有影响; 2)不同应对预算下,风险对战略实现干扰权重变化对应对决策的影响程度不同; 3)较少关注风险对战略实现干扰的决策者,倾向选择能更多应对关联较强风险的策略集;更多注重风险对战略实现干扰的决策者,倾向选择能更多应对项目组合层级风险的策略集.     

多文档文摘中句子优化选择方法研究

在多文档文摘子主题划分的基础上,提出了一种在子主题之间对文摘句优化选择的方法．首先在句子相似度计算的基础上,形成多文档集合的子主题,通过对各子主题打分,确定子主题的抽取顺序．以文摘中有效词的覆盖率作为优化指标,在各个子主题中选择文摘句．从减少子主题之间及子主题内部的信息的冗余性两个角度选择文摘句,使文摘的信息覆盖率得到很大提高．实验表明,生成的文摘是令人满意的．     

A clustering-based review on project portfolio optimization methods

Project portfolio management and optimization constitutes a critical activity for organizations in different industrial sectors and business. The scientific literature in this subject is extremely vast, which makes it difficult to understand the connections among the existing approaches and perspectives. This paper provides a clustering map of the existing work on the subject, thus identifying the main trends and approaches from different scientific communities. After analyzing each of the identified clusters, the paper provides insights and emerging trends that can be useful both for researchers and practitioners in the area.     

An stochastic multiattribute acceptability analysis‐based method for the multiattribute project portfolio selection problem with rank‐level information

In many cases of practical multiattribute project portfolio selection problems, it is hard to obtain accurate measurements of attributes and precise preference information. Even after a long and costly information gathering, the attribute measurements and the preference information can still be uncertain or inaccurate. Considerable cost saving will be obtained if the selection of an optimal project portfolio can be done using rank‐level information based on some or all the attributes, without knowing the preference information. In this paper, we propose a stochastic multiattribute acceptability analysis‐based method that can deal with mixed rank and cardinal attribute measurements and uses little or no weight information. In the proposed stochastic multiattribute acceptability analysis‐based method, the decision makers need not to express their preferences explicitly or implicitly, so it is particularly useful when no weight information is available at all. A numerical example involving selection of photovoltaic plants in an industrial province in Eastern China is provided to demonstrate the proposed method.     

Score-hesitation trade-off and portfolio selection under intuitionistic fuzzy environment

Qualitative portfolio selection approach is a suitable technique for obtaining an optimal portfolio when quantitative data are unavailable and traditional portfolio models are ineffective. However, few studies focus on this issue. This study addresses the lack of research by defining the score-hesitation trade-off rule and introducing the intuitionistic fuzzy set (IFS), based on which an intuitionistic fuzzy portfolio selection (IFPS) model is proposed. The IFS is introduced because of its comprehensive consideration of preference and nonpreference, and is used to represent qualitatively evaluated information from investors and experts. Furthermore, an intuitionistic fuzzy investment scenario is established and a trisection approach is designed to distinguish three types of risk investors, based on which three corresponding IFPS models are constructed. After this, a portfolio selection process under the intuitionistic fuzzy environment is provided, and a simple example is given to show the application of the process. In addition, the investment opportunities and efficient frontier of the IFPS model are investigated to demonstrate the effectiveness of the proposed portfolio selection model. Finally, an example of calculating optimal investment ratios and selecting an optimal portfolio for four newly listed stocks in China is provided to demonstrate the feasibility and practicability of the proposed approaches.     

基于战略对应的IT项目组合选择模型

把信息技术项目当作组合来管理可以通过平衡风险和收益来促进企业目标和IT应用的结合,但由于决策信息的不确定性和IT项目目标与企业战略的难以对应,企业面临IT项目组合选择的挑战。构建基于战略对应的IT项目组合选择模型,其中模糊集和模糊层次分析法用来刻画不确定信息和评估IT项目风险、成本及收益,关键成功因素法用来提高IT项目与企业战略的对应,并建立模糊0－1整数规划。利用定性可能性理论把模糊组合选择模型转化为一般可求解的整数规划形式,最后用一个案例说明模型的用法。     

项目组合交互风险的网络聚类及协同演化研究

将交互关系纳入项目组合风险研究框架,分析项目组合交互风险网络复杂性及其演化规律。从交互关系分类入手,将项目组合表征为由多个子系统相互作用构成的交互耦合网络,并提出了基于直觉模糊生态聚类算法的项目组合交互风险网络聚类识别模型;基于扩展的Lotka-Volterra方程建立项目组合交互风险协同演化模型,对比研究具有竞争、共生和偏利偏害效应的项目组合交互风险演化规律。研究表明,项目组合风险效率最大化及系统稳定性的关键在于项目组合交互关系及其强度,同时合理控制组合配置比例。     

Solving a comprehensive model for multiobjective project portfolio selection

Any organization is routinely faced with the need to make decisions regarding the selection and scheduling of project portfolios from a set of candidate projects. We propose a multiobjective binary programming model that facilitates both obtaining efficient portfolios in line with the set of objectives pursued by the organization, as well as their scheduling regarding the optimum time to launch each project within the portfolio without the need for a priori information on the decision-maker's preferences. Resource constraints, the possibility of transferring resources not consumed in a given a period to the following one, and project interdependence have also been taken into account. Given that the complexity of this problem increases as the number of projects and the number of objectives increase, we solve it using a metaheuristic procedure based on Scatter Search that we call SS-PPS (Scatter Search for Project Portfolio Selection). The characteristics and effectiveness of this method are compared with other heuristic approaches (SPEA and a fully random procedure) using computational experiments on randomly generated instances.

Statement of scope and purpose

This paper describes a model to aid in the selection and scheduling of project portfolios within an organization. The model was designed assuming strong interdependence between projects, which therefore have to be assessed in groups, while allowing individual projects to start at different times depending on resource availability or any other strategic or political requirements, which involves timing issues. The simultaneous combination of project portfolio selection and scheduling under general conditions involves known drawbacks that we attempt to remedy. Finally, the model takes into account multiple objectives without requiring a priori specifications regarding the decision-maker's preferences.The resolution of the problem was approached using a metaheuristic procedure, which showed by computational experiments good performance compared with other heuristics.     

基于多Agent和网格技术的煤炭企业项目组合信息管理平台研究

为了解决煤炭企业项目组合各参与方信息平台的异构问题,提出了一种基于多Agent和网格技术的煤炭企业项目组合信息管理平台,给出了该平台的框架、各Agent实体的功能设计及平台运行流程。该平台采用网格技术实现了各异构平台的集成,使得项目组合各参与方可协作工作,并采用多Agent系统将网格环境融合到各异构平台中,实现了各参与方信息平台的统一配置和管理。该平台在某煤炭企业的应用验证了其有效性。     

Uncertain programming models for portfolio selection with uncertain returns

In an indeterminacy economic environment, experts’ knowledge about the returns of securities consists of much uncertainty instead of randomness. This paper discusses portfolio selection problem in uncertain environment in which security returns cannot be well reflected by historical data, but can be evaluated by the experts. In the paper, returns of securities are assumed to be given by uncertain variables. According to various decision criteria, the portfolio selection problem in uncertain environment is formulated as expected-variance-chance model and chance-expected-variance model by using the uncertainty programming. Within the framework of uncertainty theory, for the convenience of solving the models, some crisp equivalents are discussed under different conditions. In addition, a hybrid intelligent algorithm is designed in the paper to provide a general method for solving the new models in general cases. At last, two numerical examples are provided to show the performance and applications of the models and algorithm.     

Multi-period portfolio selection using kernel-based control policy with dimensionality reduction

This paper studies a nonlinear control policy for multi-period investment. The nonlinear strategy we implement is categorized as a kernel method, but solving large-scale instances of the resulting optimization problem in a direct manner is computationally intractable in the literature. In order to overcome this difficulty, we employ a dimensionality reduction technique which is often used in principal component analysis. Numerical experiments show that our strategy works not only to reduce the computation time, but also to improve out-of-sample investment performance.     

Distributionally robust fuzzy project portfolio optimization problem with interactive returns

Effective project selection and staff assignment strategies directly impact organizational profitability. Based on critical value optimization criterion, this paper discusses how uncertainty and interaction impact the project portfolio return and staff allocation. Since the exact possibility distributions of uncertain parameters in practical project portfolio problems are often unavailable, we adopt variable parametric possibility distributions to characterize uncertain model parameters. Furthermore, this paper develops a novel parametric credibilistic optimization method for project portfolio selection problem. According to the structural characteristics of variable parametric possibility distributions, we derive the equivalent analytical expressions of credibility constraints, and turn the original credibilistic project portfolio model into its equivalent nonlinear mixed-integer programming models. To show the advantages of the proposed parametric credibilistic optimization method, some numerical experiments are conducted by setting various values of distribution parameters. The computational results support our arguments by comparing with the optimization method under fixed possibility distributions.