Dombi-Normalized Weighted Bonferroni Mean Operators with Novel Multiple-Valued Complex Neutrosophic Uncertain Linguistic Sets and Their Application in Decision Making

Although fuzzy set concepts have evolved,neutrosophic sets are attractingmore attention due to the greater power of the structure of neutrosophic sets.The ability to account for components that are true,false or neither true nor false is useful in the resolution of real-life problems.However,simultaneous variations render neutrosophic sets unsuitable in specific circumstances.To enable the management of these sorts of issues,we combine the principle of multi-valued neutrosophic uncertain linguistic sets and complex fuzzy sets to develop the principle of multivalued complex neutrosophic uncertain linguistic sets.Multi-valued complex neutrosophic uncertain linguistic sets can contain grades of truth,abstinence,and falsity,and uncertain linguistic terms,which are expressed as complex numbers whose real and imaginary parts are limited to the unit interval.Some important Dombi laws are elaborated along with Bonferroni mean operators,which offer a flexible general structure with modifiable factors.Bonferroni means aggregation operators perform a significant role in conveying the magnitude level of options and characteristics.To determine relationships among any number of attributes,we develop multi-valued complex neutrosophic uncertain linguistic Dombi-normalized weighted Bonferroni mean operators and discuss their important properties with some special cases.By using these laws,we can deploy themulti-attribute decisionmaking(MADM)technique using the novel principle of multi-valued complex neutrosophic uncertain linguistic sets.To determine the power and flexibility of the elaborated approach,we resolve some numerical examples based on the proposed operator.Finally,the work is validated with the help of comparative analysis,a discussion of its advantages,and geometric expressions of the elaborated theories.

Response analysis based on smallest interval-set of parameters for structures with uncertainty

An integral analytic process from quantification to propagation based on limited uncertain parameters is investigated to deal with practical engineering problems. A new method by use of the smallest interval-set/hyper-rectangle containing all exper- imental data is proposed to quantify the parameter uncertainties. With the smallest parameter interval-set, the uncertainty sponse and the least favorable response propagation evaluation of the most favorable re- of the structures is studied based on the interval analysis. The relationship between the proposed interval analysis method （IAM） and the classical IAM is discussed. Two numerical examples are presented to demonstrate the feasibility and validity of the proposed method.

面向新能源高渗透系统调峰和调频的系统级储能容量需求分析

高渗透新能源的强不确定性及火电机组占比的逐步减小使得系统灵活性资源不足,系统级储能可高效缓解系统的调峰和调频压力。为此,从系统角度提出了一种面向新能源高渗透系统调峰和调频的系统级储能需求容量确定方法。基于分位数回归分析和高斯混合模型聚类,提出了一种刻画净负荷不确定性的场景集生成方法;基于生成的场景集,建立了一种无储能容量约束的常规火电机组与储能联合优化运行模型,以获取最优运行时储能参与调峰和调频的功率;为了同时兼顾储能利用率和系统运行成本,构建了基于储能电量偏离程度、运行成本增长率指标的储能功率修正模型,并确定储能需求功率和容量,进而分析新能源渗透率、储能功率和容量与满足需求的置信度水平三者之间的关系。算例仿真结果表明,所提方法能够有效确定新能源高渗透系统调峰和调频对储能的需求容量。

计及源荷互补特性的“源网荷储一体化项目”优化配置

针对“源网荷储一体化项目”(后简称一体化项目)在风光消纳率、清洁用电率等方面的建设要求,提出了计及源荷互补特性的一体化项目优化配置方法。基于包含风光发电制氢负荷、可平移工业负荷、电动汽车快充站的一体化项目拓扑,建立了一体化项目内部的多类型负荷用电功率模型。提出基于分时Copula函数生成时序一致的风光联合出力场景集的方法,进而提出分为风光额定功率配比优化、容量配置优化、负荷接入位置优化三阶段的一体化项目优化配置方法。通过算例分析对所提方法的可行性和有效性进行仿真验证。

基于SVC的电动汽车集群并网鲁棒优化调度模型

针对电动汽车(electric vehicle,EV)入网时长和荷电状态(state of charge,SOC)的不确定性,提出基于支持向量聚类(support vector clustering,SVC)的电动汽车集群并网鲁棒优化调度模型。以EV的充放电功率作为决策变量,用户最小充电成本为目标函数,建立集群EV调度模型。利用EV历史充电数据,以包含所有样本数据的最小超球体作为不确定集形状,将广义直方图交叉核作为核函数,计算EV入网时间和充电时长参数的不确定集,建立基于SVC的集群EV鲁棒优化调度模型。算例分析结果表明,所提方法能更准确地描述EV充电的不确定性参数,所提模型在保证经济性的同时能迅速响应分时电价,具有较好的实用性。

基于区间概率不确定集的微电网两阶段自适应鲁棒优化调度

光伏出力的随机性和负荷用电的波动性对微电网的优化调度影响显著,为此提出了预测-调节-决策一体化的策略框架。基于高斯过程回归(Gaussian process regression,GPR)将光伏出力和负荷用电典型日历史数据自适应生成的置信区间与鲁棒优化中不确定集的构建相结合,建立了基于区间概率不确定集的自适应鲁棒优化调度模型。首先,通过GPR生成自适应鲁棒优化调度模型中不确定集的固定项,然后调节决策环节所考虑的风险水平以确定不确定集中的波动项,进而确定衡量不同调度保守度下的不确定集边界;接着采用预测区间质量评测指标来考核各个不确定集所对应的区间优劣。最后,通过改进的IEEE-37节点微电网系统验证了所提模型在有效抵御光伏出力和负荷用电波动的同时保持较低的运行成本。

电动车辆路径问题:可调鲁棒数学模型与算法

针对行驶时间不确定的允许部分充电的带时间窗电动车辆路径问题,考虑多面体不确定集度量行驶时间的不确定性,建立了一个可调鲁棒优化模型,设计了基于行生成和集划分的求解算法,采用标号法对路径的可行性进行判定,并将不可行的路径作为新的约束加入到模型中.数值实验表明,94%的算例可求得最优解,这验证了本算法的效率;利用多面体不确定集进行度量对总行驶距离和车辆总数目具有正向的影响;相比于普通鲁棒优化,可调鲁棒优化的求解结果有显著提高,可以提升车辆调度的灵活性.

Some interval-valued intuitionistic uncertain linguistic hybrid Shapley operators

Two interval-valued intuitionistic uncertain linguistic hybrid operators cal ed the induced interval-valued intuitionistic uncertain linguistic hybrid Shapley averaging （I-IIULHSA） operator and the induced interval-valued intuitionistic uncertain linguistic hybrid Shapley geometric （I-IIULHSG） operator are defined. These operators not only reflect the importance of elements and their ordered positions, but also consider the correlations among elements and their ordered positions. Since the fuzzy measures are defined on the power set, it makes the problem exponentially complex. In order to simplify the complexity of solving a fuzzy measure, we further define the induced interval-valued intuitionistic uncertain linguistic hybrid λ-Shapley averaging （I-IIULHλSA） operator and the induced interval-valued intuitionistic uncertain linguistic hybrid λ-Shapley geometric （I-IIULHλSG） operator. Moreover, an approach for multi-attribute group decision making under the interval-valued intuitionistic uncertain linguistic environment is developed. Finally, a numerical example is provided to verify the developed procedure and demonstrate its practicality and feasibility.

Pythagorean Uncertain Linguistic Variable Hamy Mean Operator and Its Application to Multi-attribute Group Decision Making

Pythagorean fuzzy set(PFS) can provide more flexibility than intuitionistic fuzzy set(IFS) for handling uncertain information, and PFS has been increasingly used in multi-attribute decision making problems. This paper proposes a new multiattribute group decision making method based on Pythagorean uncertain linguistic variable Hamy mean(PULVHM) operator and VIKOR method. Firstly, we define operation rules and a new aggregation operator of Pythagorean uncertain linguistic variable(PULV) and explore some properties of the operator.Secondly, taking the decision makers' hesitation degree into account, a new score function is defined, and we further develop a new group decision making approach integrated with VIKOR method. Finally, an investment example is demonstrated to elaborate the validity of the proposed method. Sensibility analysis and comprehensive comparisons with another two methods are performed to show the stability and advantage of our method.

Interactive group decision making procedure based on uncertain multiplicative linguistic preference relations

Group decision making problems are investigated with uncertain multiplicative linguistic preference relations.An unbalanced multiplicative linguistic label set is introduced,which can be used by the experts to express their linguistic preference information over alternatives.The uncertain linguistic weighted geometric mean operator is utilized to aggregate all the individual uncertain multiplicative linguistic preference relations into a collective one,and then a simple approach is developed to determine the experts＇ weights by utilizing the consensus degrees among the individual uncertain multiplicative linguistic preference relations and the collective uncertain multiplicative linguistic preference relations.Furthermore,a practical interactive procedure for group decision making is proposed based on uncertain multiplicative linguistic preference relations,in which a possibility degree formula and a complementary matrix are used to rank the given alternatives.Finally,the proposed procedure is applied to solve the group decision making problem of a manufacturing company searching the best global supplier for one of its most critical parts used in assembling process.

Group Decision Making With Consistency of Intuitionistic Fuzzy Preference Relations Under Uncertainty

Intuitionistic fuzzy preference relation(IFPR) is a suitable technique to express fuzzy preference information by decision makers(DMs). This paper aims to provide a group decision making method where DMs use the IFPRs to indicate their preferences with uncertain weights. To begin with, a model to derive weight vectors of alternatives from IFPRs based on multiplicative consistency is presented. Specifically, for any IFPR,by minimizing its absolute deviation from the corresponding consistent IFPR, the weight vectors are generated. Secondly,a method to determine relative weights of DMs depending on preference information is developed. After that we prioritize alternatives based on the obtained weights considering the risk preference of DMs. Finally, this approach is applied to the problem of technical risks assessment of armored equipment to illustrate the applicability and superiority of the proposed method.

空间方向关系表达与推理模型研究综述

空间方向关系作为空间关系不可缺少的组成部分,广泛应用于城市智能交通管控、环境资源检测、防灾减灾等诸多任务,日益成为地理信息系统、空间数据库、人工智能、模式识别等领域研究的热点。综合分析和梳理现有空间方向关系表达与推理模型,针对二维空间对象,从单目标对象、群组目标对象等方面详细介绍目前二维空间方向关系表达与推理模型的研究进展,依据描述对象的不同进行分类,对每类模型的特性及适用范围进行定性分析比较。针对三维空间对象,从点到区域依次分析当前三维空间方向关系表达与推理模型的特点、优势和不足。针对不确定性对象,从确定性空间方向关系模型的扩展模型、基于不确定性集合理论模型2个方面阐述不确定性方向关系模型的研究现状,并对每类模型的优缺点、适用范围等进行分析总结。最后阐明当前工作存在的不足,并从自动推理技术、空间关系的联合表达以及群组目标对象等方面对空间方向关系的未来研究方向进行展望。

需求不确定下基于鲁棒优化的新旧充电站规划

充电基础设施的合理规划是促进电动汽车行业发展的重要环节,现有研究均未考虑用户充电需求的不确定性对新旧充电站规划的影响。针对这一问题,采用鲁棒优化方法,引入不确定情境集来刻画用户充电需求的不确定性,构建了一个基于鲁棒优化的新旧充电站规划模型。并通过不确定预算来调节不确定集合的大小,以满足决策者的不同风险偏好。此外,将该模型应用于上海市杨浦区电动汽车充电站选址案例中,研究结果表明:(1)需求不确定条件下基于鲁棒优化的新旧充电站规划模型更符合实际,可以更好地应对未来用户充电需求增加的情况。(2)不确定预算对投资成本和建站数量有显著影响,决策者可以根据对不确定风险的偏好程度选择最佳的选址方案。

考虑中介平台收益的企业-投资商双边匹配决策方法

针对概率不确定语言环境下的企业-投资商双边匹配问题,提出了一种考虑中介平台收益的双边匹配模型.首先,定义了可信度,对匹配主体偏好信息的可靠度进行测量,进而根据可信度给出了概率不确定语言术语集得分值的计算公式;其次,通过改进的TODIM方法得到满意度矩阵;然后,给出了中介平台费用函数并规范化其函数,基于此,将企业与投资商匹配满意度最大化以及中介平台收益最大化作为目标,构建双边匹配优化模型并求解;最后,通过某投资理财平台的案例,证明了该方法的可行性及有效性.

不确定环境下新能源汽车企业技术融合前因组态研究

通过提升技术融合能力拓展组织边界,对企业在不确定性环境下保持和提升竞争力至关重要。以21家中国新能源汽车整车上市公司为样本,基于TOE框架和组态理论,采用模糊集定性比较分析法探讨不确定性环境下企业技术融合影响因素间的复杂关系和技术融合度提升路径。结果表明,存在4条支撑企业实现高水平技术融合的路径,可归纳为环境波动—技术驱动型、环境波动—资源支撑型、环境平衡—组织支撑—政府拉动型以及技术动荡—组织支撑—政府拉动型。当技术与市场不确定性均高时,技术广度与组织冗余之间具有替代关系;当技术与市场不确定性均低时,组织冗余与政府补助之间具有互补关系。在联动匹配中,企业技术环境不确定性是超高技术融合度组态的核心条件。企业非高技术融合度的实现路径有3条,且与高技术融合度路径存在非对称性关系。研究结论有助于加深对不确定性环境下技术融合多重影响因素间复杂互动关系的理论认识,揭示企业技术融合的支撑力量与推动因素,为提升企业技术融合水平提供实践启示。

考虑可再生能源出力不确定性的微电网能量优化鲁棒模型

为了实现含可再生能源的并网微电网经济运行,提出了出力不确定集,并综合考虑"源储荷"协调调控,构建了广义能量优化鲁棒模型。模型采用多阶段求解策略,其中端点场景根据设计的生成规则产生,而二层规划问题则转化为单层非线性数学结构进行求解,得到使交互成本最大端点场景下社会效益成本最小的经济运行调度方案。算例验证了模型和算法的可行性,可为调度提供决策参考。此外,仿真结果还表明:微电网向主网购电价格大小会影响交互功率传输量;而售电价格与其比值的增加在一定程度上会减少系统总的社会效益成本。

不确定性信息下的河流纳污能力计算初探

基于河流水文、水质条件等因素的变化,在考虑参量取值可信度的基础上,运用未确知数学中有关盲数理论对不确定性信息下河流纳污能力计算问题进行了探讨。并以实际水体为例,求得相应可信度水平下纳污能力的未确知期望值。研究表明,运用未确知数学计算不确定信息下河流纳污能力,理论上是可行的,计算结果是可信的。为水质管理和保护,提供了科学依据。

基于边界和距离的离群点检测

近年来,离群点检测已经引起人们的广泛关注.离群点检测在网络入侵检测、信用卡欺诈、电子商务犯罪、医疗诊断以及反恐等诸多领域都具有十分重要的作用.离群点检测的目的是为了发现数据集中的一小部分对象,与数据集中其余的大部分对象相比,这一小部分对象有着特殊的行为或者具有反常的属性.针对现有的离群点检测方法不能有效处理不确定与不完整数据的问题,本文将粗糙集中边界的概念与Knorr等所提出的基于距离的离群点检测方法结合在一起,在粗糙集的框架中提出一种新的离群点定义与检测方法.针对于该方法,我们设计出相应的离群点检测算法BDOD,并且通过在临床诊断数据集上所进行的实验,验证了算法BDOD的有效性.实验结果表明本文的方法为处理离群点检测中的不确定与不完整数据问题提供了一条新的途径.

贝叶斯分类器与粗糙集相结合的变压器综合故障诊断

由于电力变压器故障诊断中的测试数据信息不完备、有偏差,且贝叶斯网络处理不确定性问题能力强,文中提出了用于变压器故障诊断的NB、TAN和BAN三种贝叶斯分类器模型,并提出了贝叶斯网络分类器与粗糙集相结合的变压器故障诊断的新方法,它综合使用溶解气体分析结果和其它电气试验结果作为故障分类所需的属性。其相应的混合分类器为NB粗集、TAN粗集和BAN粗集分类器。实验表明提出的三种混合分类器都适于变压器故障诊断,具有处理信息缺失多的能力和容错特性,克服了粗糙集刚性推理的弱点,其性能明显优于单独使用贝叶斯网络分类器或粗糙集的方法。

不确定规划的研究现状及其发展前景

本文简要介绍不确定规划理论、算法以及应用研究的现状 ,描绘了不确定规划理论的基本框架。首先介绍处理各种不确定性的数学工具 ;接着阐述不确定规划的建模机理与求解方法 ;然后提出了不确定规划中值得进一步研究的一些问题 ;