Probabilistic Analysis and Multicriteria Decision for Machine Assignment Problem with General Service Times

In this paper we carried out a probabilistic analysis for a machine repair system with a general service-time distribution by means of generalized Markov renewal processes. Some formulas for the steady-state performance measures. such as the distribution of queue sizes, average queue length, degree of repairman utilization and so on. are then derived. Finally, the machine repair model and a multiple critcria decision-making method are applied to study machine assignment problem with a general service-time distribution to determine the optimum number of machines being serviced by one repairman.

The Impact of Personality Traits and Problem Structures on Management Decision-Making Outcomes

Since decision-making behavior has been in the focus both from a scientific and a professional position, there seems to be a dispute whether rational or intuitive decision making leads to better outcomes. By now, scholars have agreed that effective organizations do not have the luxury to choose between the ＂applications＂ of intuitive or rational decision making. Instead, they try to understand how different factors like personality traits and problem characteristics influence the decision-making process. Reviewing the literature reveals that personality pre-determination and the structure of problems （e.g., well-structured problems （WSPs） versus ill-structured problems （ISPs）） seem to have a significant impact on the decision-making efficiency. Further, the review also shows that there is a lack of application-oriented empirical studies in this area of research. Therefore, the aim of this research paper is to propose a framework for an empirical study on how personality traits and problem structure influence the decision-making process. First, hypotheses are derived from the literature on how personality pre-determination and behavioral patterns in the decision-making process lead to higher socioeconomic efficiency within certain problem categories. Second, a causal model and a setup for a laboratory experiment are proposed to allow testing the hypotheses. Finally, the conclusions provide an outlook on how this research could support organizations in their decision-making processes.

THE SPECTRALITY DECISION PROBLEM

An efficient algorithm for deciding whether a given integer vector is the spectrum of some Boolean function is presented. The algorithm performs a step-by-step spectral decomposition of the input vector and checks at each step a set of necessary conditions for spectrality for the resulting vectors. The algorithm concludes that the input vector cannot lead to a valid Boolean function as soon as a vector not satisfying the conditions is found, which, as proved in the paper, for almost all cases happens after the first step of the decomposition.

Multi-attribute group decision making method under 2-dimension uncertain linguistic variables

A method is proposed to deal with the uncertain multiple attribute group decision making problems,where 2-dimension uncertain linguistic variables(2DULVs)are used as the reliable way for the experts to express their fuzzy subjective evaluation information.Firstly,in order to measure the 2DULVs more accurately,a new method is proposed to compare two 2DULVs,called a score function,while a new function is defined to measure the distance between two 2DULVs.Secondly,two optimization models are established to determine the weight of experts and attributes based on the new distance formula and a weighted average operator is used to determine the comprehensive evaluation value of each alternative.Then,a score function is used to determine the ranking of the alternatives.Finally,the effectiveness of the proposed method is proved by an illustrated example.

Constraints Based Decision Support for Site-Specific Preliminary Design of Wind Turbines

This study presents a decision-support tool for preliminary design of a horizontal wind turbine system. The function of this tool is to assist the various actors in making decisions about choices inherent to their activities in the field of wind energy. Wind turbine cost and site characteristics are taken into account in the used models which are mainly based on the engineering knowledge. The present tool uses a constraint-modelling technique in combination with a CSP solver (numerical CSPs which are based on an arithmetic interval). In this way, it generates solutions and automatically performs the concept selection and costing of a given wind turbine. The data generated by the tool and required for decision making are: the quality index of solution (wind turbine), the amount of energy produced, the total cost of the wind turbine and the design variables which define the architecture of the wind turbine system. When applied to redesign a standard wind turbine in adequacy with a given site, the present tool proved both its ability to implement constraint modelling and its usefulness in conducting an appraisal.

New Explorations for Decision Trees

Traditionally, the decision tree method is defined and used for finding the optimal solution of a Bayesian decision problem. And it is difficult to use the decision tree method to find the sub-optimal solution, not to mention to rank alternatives. This paper discusses how to use the decision tree method for the alternative selecting and ranking. A practical case study is given to illustrate the applicability.

Application of Fuzzy Optimization Method in Decision-Making for Personnel Selection

The paper describes the necessity of application of intelligent technologies to support decisions of more objective problems in human resource management. In this paper, we describe the methodology for personnel selection problem for the vacancy with regard to the importance and nonequivalence of numerous indicators characterizing the alternatives. The specific features of the selection problem are highlighted, immersing the problem into a fuzzy environment. A fuzzy multicriterial model of the personnel selection problem is proposed. A technique of order preference by similarity to ideal solition (TOPSIS), was applied for evaluation and regulation of alternatives. This technique is based on criteria of qualitative character, which are hierarchically structured by multiple experts to intellectually support decisions made in personnel selection problem. Using TOPSIS method and generated criteria system an experiment was conducted for evaluation of the candidates during solution of hiring problems. The obtained and reviewed results were compared with results obtained using in reality.

A Theoretical Approach to the Effective Decision-Making Process

This research develops and elaborates studies done for a contribution to the 2019 PIC International Conference 2019 in Malta, about the decision-making process. Decision-making is the act of choosing between two or more courses of action. In the wider process of problem-solving, decision-making involves choosing between possible solutions to a problem, and these decisions can be made through either an intuitive or reasoned process, or a combination of the two. The study of decision-making processes, to be understood as the role of human factors, becomes particularly interesting in complex organizations. This research aims to analyze how an effective team, within organizations, can develop a more correct and effective decision-making, in order to get an optimal solution, overcoming the typical uncertainty. The paper describes the point of departure of decision in complex, time-pressured, uncertain, ambiguous and changing environments. The use of a leading case (the Tenerife air accident, 1977), will lead us to the desired results, i.e. to demonstrate how an effective decisional process, including team dynamics, can be useful to reduce the risk, present in all decisions, and reduce errors. The case of Tenerife air disaster, confirm our research. In that case, in fact, the group dynamics prove not to have worked. Thus, we can state that if a team approach had been followed instead of a more individual one, the results would probably have been different. The central belief of the research, is that classic decision theory could benefit from a team approach, which reduces the risk that a decision may lead to undesirable consequences. As demonstrated with the case study, within organizations, the decision-making is not a solitary action. Decisions, in fact, are made within a team and in order to be able to function effectively in a group, and manage group situations, there are essential skills. The team can then become a resource for the decisional process and problem solving, but it is necessary to understand the dynamics.

基于颠覆性技术创新项目评价及立项决策模型的研究

为实现我国颠覆性技术创新项目的精准遴选,促进颠覆性创新产业的培育发展,在分析梳理国内外关于颠覆性技术创新项目遴选、立项决策管理方面研究的基础上,分析对比颠覆性技术创新项目与一般科技计划项目的特征属性差异,借鉴海尔迈耶问题的做法,结合当前政策导向,针对颠覆性技术具有隐蔽性、破坏性、不确定性的特征属性,提出相应的项目遴选角度和评审标准的思路,并利用层次分析法构建颠覆性技术创新项目立项评价的三级指标体系。从项目影响力、项目可能性两方面构建颠覆性技术创新项目的决策矩阵模型,为颠覆性创新项目立项决策提供判断依据。

改进的差分演化算法及其在资金决策中的应用

针对传统的基本差分演化算法存在对参数设置太过于敏感,算法容易陷入早熟收敛、稳定性不强等等缺陷,提出了改进措施,采取了对称算法来保持种群多样性,通过变异个体,对称个体,个体三者进行有效竞争,增强算法的全局搜索能力;同时采用了轮盘赌的选择策略和二次变异算子保证种群个体的有效性与多样性,增加算法的全局稳定性.文中的算法应用于求解资金决策分配问题上,通过模拟仿真,该算法在稳定性、收敛速度、求解的精度方面优于其他被比较的算法.

协同智能体强化学习算法的柔性作业车间调度方法研究

为提高柔性作业车间调度效率,构建一种具有柔性作业车间调度问题约束条件的马尔可夫决策过程,针对工件与机器的同时选择问题,提出一种协同智能体强化学习方法进行求解。在构建马尔可夫决策过程中,引入析取图表述状态特征,采用两种智能体执行工件与机器的选取,预测不同时刻最小化最大完工时间的差值来映射整个调度过程的奖励参数;求解时,嵌入GIN(graph isomorphic network)图神经网络提取状态,为工件与机器智能体分别设置编码器-解码器构件输出两种动作策略,以PPO(proximal policy optimization)算法与D3QN算法训练工件与机器智能体的决策网络参数。通过正交试验法选取算法超参数,以标准实例与其他文献进行对比,实验结果表明,所提方法在求解FJSP方面明显优于其他算法,进一步验证所提方法的可行性与有效性。

ChatGPT助力领导效能开发的策略探析

随着人工智能技术的飞速发展,ChatGPT等大型语言模型凭借其强大的自然语言处理能力,在领导效能开发中发挥了积极作用,但同时也可能带来潜在风险。对此,在分析两个ChatGPT应用案例的基础上,以适应--选配--开发理论框架为基础,提出ChatGPT助力领导效能开发的策略,包括构建智能决策支持系统、提供问题解决和创新支持、培养数据驱动思维、实现个性化学习、搭建沟通和领导力训练五种策略。

基于距离信息的追逃策略:信念状态连续随机博弈

追逃问题的研究在对抗、追踪以及搜查等领域极具现实意义.借助连续随机博弈与马尔科夫决策过程(Markov decision process, MDP),研究使用测量距离求解多对一追逃问题的最优策略.在此追逃问题中,追捕群体仅领导者可测量与逃逸者间的相对距离,而逃逸者具有全局视野.追逃策略求解被分为追博弈与马尔科夫决策两个过程.在求解追捕策略时,通过分割环境引入信念区域状态以估计逃逸者位置,同时使用测量距离对信念区域状态进行修正,构建起基于信念区域状态的连续随机追博弈,并借助不动点定理证明了博弈平稳纳什均衡策略的存在性.在求解逃逸策略时,逃逸者根据全局信息建立混合状态下的马尔科夫决策过程及相应的最优贝尔曼方程.同时给出了基于强化学习的平稳追逃策略求解算法,并通过案例验证了该算法的有效性.

管理层结构对国际组织决策偏好的影响——以国际货币基金组织为例

基于委托代理理论,本文收集国际货币基金组织人力资源数据,分析管理层员工结构对国际货币基金组织贷款决策偏好的影响。研究发现,管理层员工结构会对国际货币基金组织贷款决策偏好产生正向影响。借款国国民在国际货币基金组织担任中层管理人员越多,担任总裁办公室主任和地区部门主任的管理岗位的国民越多,担任执行董事和副执行董事人数越多,在欧洲和西半球地区部门任职管理人员越多,国际货币基金组织贷款决策更偏好于该国。进一步研究发现,当借款国与美国外交立场一致,获得西方发达国家经济援助越多,借款国国民担任管理人员越多,国际货币基金组织贷款决策会更偏好于该国。中国在参与国际金融治理时,常处于劣势地位。本文从人力资源视角提供中国参与国际金融治理的新思路和新路径,以期提升中国在国际金融治理中的话语权。

考虑设施规模决策的两级选址-路径优化

针对目前电商行业基础设施布局和空间利用不合理的问题,提出考虑设施规模决策的两级选址-路径问题(2E-LRP)求解模型。首先,在传统2E-LRP中引入差异性设施规模约束,通过识别客户群设计不同设施规模组合,利用规模弹性变化调整总成本组成,并以最小运营成本为目标建立顾及设施规模弹性变化的2E-LRP模型;其次,提出两阶段混合迭代局部搜索启发式算法求解模型;最后,分析所提模型和优化算法,并以Prodhon等不同数据集为实例进行验证。实验结果表明,所提模型具有针对区域差异和不同数据规模的普适性,且设施规模的弹性变化范围值与总成本呈负相关;与拉格朗日松弛粒度禁忌搜索(LRGTS)等算法的最优成本相比,所提算法对所有算例的最优成本平均值降低了6.67%,可以有效节约运行成本。

纯粹主义、不纯主义与阈值问题——兼论共同体纯粹主义

随着知识论的实用入侵,正统的纯粹主义知识论面临巨大的挑战。实用入侵者认为,纯粹主义蕴含不可信的不可错论,相反,他们自己所倡导的不纯主义不仅与可错论相容,而且解决了其阈值问题。不纯主义解决阈值问题的路径主要有三条:统一性、相关性和共同体路径。然而,三条路径无一例外都失败了,根由在于实用入侵背后的认知决策论。认知决策论盛行于当代科学哲学和知识论,主张把证成视为一种认知决策,因此错置了信念与接受的认识论地位。与之相对,共同体纯粹主义捍卫信念的认识论地位,把信念视为一种探索世界的基本机制,一种跨越目标和语境的承诺状态。总之,共同体纯粹主义吸收了不纯主义和共同体主义的合理要素,兼顾了知识的理论与实践维度。

借助教学困境决策培养教育硕士研究生教学能力的策略

中小学核心素养变革要求师范院校培养能够解决真实教学问题的职前教师。以两难或多难问题为核心的教学困境是课堂生活的组成部分,对教学困境的决策凸显教育硕士研究生的教学能力。困境决策视角的教育硕士研究生教学能力包括诊断教学困境类型并设计教学目标的能力、把握教学困境的学科特殊性以整合教学内容的能力、在动态困境中做出即时性教学决策的能力、对教学困境决策进行反思的“元决策”能力。借助教学困境决策提高教育硕士研究生的教学能力,高校要在培养方案中强化研究生的问题解决能力培养,以“创中学”为主线整合三类学习方式,校地协同丰富研究生的教学困境决策,指导研究生对教学困境决策进行深度反思,以提高研究生的培养质量。

论自动驾驶汽车伦理难题之多数决策道德算法解决

电车难题的本质是行驶中的电车面对不可避免的冲撞时的伦理选择困境。自动驾驶汽车也会面临同电车难题一样的伦理困境。同时,面对自动驾驶汽车伦理难题的变体问题,人们会因为情境的变化而改变自己先前所秉持的伦理原则。基于伦理原则的多数决策道德算法依据“少数服从多数”原则,在尊重各利益相关者伦理原则偏好的基础上,能够有效地解决自动驾驶汽车伦理难题及其变体问题。

问题提示清单配合共享决策指导干预对肺癌患者术后心理状态及复发恐惧的影响

目的:观察问题提示清单配合共享决策指导干预对肺癌患者术后心理状态及复发恐惧的影响。方法:选取某院2023年1-12月肺癌患者473例纳入研究,使用数字编号信封法将其随机分为观察组(n=236)和对照组(n=237),两组患者均给予手术治疗和常规护理干预,对照组给予问题提示清单护理,观察组在以上基础上给予共享决策指导。比较两组患者干预前后的心理状态[汉密尔顿焦虑量表(HAMA)、汉密尔顿抑郁量表(HAMD)]、复发恐惧[癌症复发恐惧量表(FCRI)]和生活质量[WHO生活质量测定简表(WHO-Qol-BREF)]差异,记录两组患者术后并发症发生率并比较。结果:经干预后,观察到两组患者在HAMA、HAMD以及FCRI评分上均呈显著降低趋势(t=27.353,19.549,23.827,25.247,17.674,22.294;P<0.05),并且观察组评分降低幅度明显大于对照组(t=2.378,2.651,2.304;P<0.05);此外,两组患者在心理、生理、环境以及社会关系4个领域的评分均较干预前有显著上升趋势(t=24.031,20.165,13.324,16.801,21.271,17.236,10.603,13.977;P<0.05),而观察组在这些领域的提升幅度上明显高于对照组(t=2.445,2.553,2.799,2.787;P<0.05);在术后并发症发生率上,观察组也表现出明显低于对照组的趋势(χ^(2)=10.774,P<0.05)。结论:肺癌患者术后给予问题提示清单配合共享决策指导干预可有效改善患者心理状态,降低患者术后复发恐惧感,提高患者生活质量,降低术后并发症发生风险。

基于决策变量时域变化特征分类的动态多目标进化算法

动态多目标优化问题(Dynamic multi-objective optimization problems,DMOPs)广泛存在于科学研究和工程实践中,其主要考虑在动态环境下同时联合优化多个冲突目标.现有方法往往关注于目标空间的时域特征,忽视了对单个决策变量变化特性的探索与利用,从而在处理更复杂的问题时不能有效引导种群收敛.为此,提出一种基于决策变量时域变化特征分类的动态多目标进化算法(Dynamic multi-objective evolutionary algorithm based on classification of decision variable temporal change characteristics,FT-DMOEA).所提算法在环境动态变化时,首先基于决策变量时域变化特征分类方法将当前时刻决策变量划分为线性变化和非线性变化两种类型;然后分别采用拉格朗日外插法和傅里叶预测模型对线性和非线性变化决策变量进行下一时刻的初始化操作.为了更有效地识别非线性决策变量变化模式,傅里叶预测模型通过傅里叶变换将历史种群数据从时域转换到频域,在分析周期性频率特征后,使用自回归模型进行频谱估计后再反变换至时域.在多个基准数据集上和其他算法进行对比,实验结果表明,所提算法是有效的.