Some new distance measures for type-2 fuzzy sets and distance measure based ranking for group decision making problems

In this paper, we propose some distance measures between type-2 fuzzy sets, and also a new family of utmost distance measures are presented. Several properties of differ- ent proposed distance measures have been introduced. Also, we have introduced a new ranking method for the ordering of type-2 fuzzy sets based on the proposed distance measure. The proposed ranking method satisfies the reasonable prop- erties for the ordering of fuzzy quantities. Some properties such as robustness, order relation have been presented. Lim- itations of existing ranking methods have been studied. Fur- ther for practical use, a new method for selecting the best alternative, for group decision making problems is proposed. This method is illustrated with a numerical example.     

A systematic literature review on intelligent automation: Aligning concepts from theory,practice, and future perspectives

With the recent developments in robotic process automation (RPA) and artificial intelligence (AI), academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications and automated business workflows and processes to accommodate context awareness, adaptation to environment and customisation. The emerging research via RPA, AI and soft computing offers sophisticated decision analysis methods, data-driven DM and scenario analysis with regard to the consideration of decision choices and provides benefits in numerous engineering applications. The emerging intelligent automation (IA) – the combination of RPA, AI and soft computing – can further transcend traditional DM to achieve unprecedented levels of operational efficiency, decision quality and system reliability. RPA allows an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured and repetitive decisions involving enormous data, in a digital system, while AI has the cognitive capabilities to emulate the actions of human behaviour and process unstructured data via machine learning, natural language processing and image processing. Insights from IA drive new opportunities in providing automated DM processes, fault diagnosis, knowledge elicitation and solutions under complex decision environments with the presence of context-aware data, uncertainty and customer preferences. This sophisticated review attempts to deliver the relevant research directions and applications from the selected literature to the readers and address the key contributions of the selected literature, IA’s benefits, implementation considerations, challenges and potential IA applications to foster the relevant research development in the domain.     

A maximum self-esteem degree based feedback mechanism for group consensus reaching with the distributed linguistic trust propagation in social network

This paper focuses on consensus reaching process (CRP) under social network in which the trust relationship expressed by linguistic information. A new feedback mechanism in social network group decision making (SN-GDM) is proposed, which mainly consists of the following two aspects: (1) The propagation of distributed linguistic trust is investigated to study trust relation among experts; (2) A maximum self-esteem degree based feedback mechanism is developed to produce personalized advice for reaching higher group consensus. To do so, a novel linguistic trust propagation method is proposed to obtain the complete trust relationship among group. The self-esteem degree is used to define the extent that an individual makes concessions. Then, a maximum self-esteem degree based optimal feedback mechanism is built to produce personalized advice to help inconsistent experts make change of their opinion. Its novelty lies in the establishment of an optimization model with the nonlinear group self-esteem degree function as the objective function while group consensus threshold as the restrictions. Therefore, the inconsistent experts will reach a group consensus with the minimum loss of self-esteem degree, and then, it achieves the optimal balance between individual self-esteem and group consensus. Finally, a ranking process is applied to derive the appropriate consensus solution.     

区间Pythagorean犹豫模糊连续熵及灰色关联度的应用

区间Pythagorean犹豫模糊集,可以更加全面完整地描述决策者给出的决策结果,因此它是一个表示不确定现象的强有力的工具。针对模糊信息下的决策问题,提出了一种基于区间Pythagorean犹豫模糊连续熵的多属性决策方法。提出连续区间Pythagorean犹豫模糊有序加权平均（CIPHFOWA）算子,并提出了区间Pythagorean犹豫模糊连续熵,同时给出了属性权重完全未知和部分已知时的权重确定方法;提出了一种基于区间Pythagorean犹豫模糊连续熵-灰色关联度的多属性决策方法,并通过新型农村医疗制度完善情况评价案例说明该方法的可行性和有效性。     

基于信号经验模态分解与分集合并的齿轮故障诊断

为了准确地进行齿轮故障诊断,结合信号经验模态分解与分集合并,提出了一种新的故障诊断方法;首先,运用经验模态分解对齿轮振动信号进行分解得到若干个分量;其次,根据分量的峭度大小以及相邻分量的峭度是否接近,筛选、合成有效分量;然后,运用等增益分集合并技术对有效分量进行合并,即将其包络进行叠加;接着,使用快速傅立叶变换得到信号包络和的频率谱;最后,根据该频率谱进行故障诊断;通过对仿真信号和齿轮断齿故障振动信号的分析,验证了方法的有效性.     

不完备系统多准则决策的云PDR方法

针对多准则决策问题的意义以及经典粗糙集方法在不完备系统方案排序问题中的不足,在前人基于概率优势关系(PDR)排序方法的基础上,结合云理论提出了基于云概率优势关系的不完备系统多准则决策方法,即云PDR排序法。将期望、熵、超熵等参数引入PDR方法,充分考虑了决策的模糊性、波动性和随机性,评价过程和结果更加客观全面。以煤炭资源型城市为例进行应用研究,获得其中各城市发展水平及其波动情况的排序,并通过与其他方法结果的比较验证了本文方法的优势与有效性。     

基于投影及优势度的不确定语言群决策方法

针对属性权重信息不完全的多粒度不确定语言多属性群决策问题,提出一种基于投影及优势度的群决策方法.该方法采用连续区间二元语义的有序加权调和平均(ITC-OWH)算子将多粒度不确定语言评价矩阵转化为二元语义决策矩阵,基于投影法构建目标规划模型,从而确定单个决策者的属性权重向量.在每个决策者之下,计算任意2个方案之间比较的综合属性优势度,根据综合优势度矩阵及决策者权重向量获得群体综合优势度矩阵,由各方案的总优势度获取方案的排序结果.最后,将提出的群决策方法应用到潜艇研制方案遴选问题中,验证了该方法的可行性与有效性.     

煤粉源集合体对水力压裂效果的影响

煤粉颗粒在压裂液中发生"造浆"及"聚集"作用是影响煤层气藏水力压裂效果的关键.构造煤粉源集合体对压裂效果影响显著,研究结果表明:原位状态下,煤粉以构造煤粉源和原生裂缝煤粉源集合体两种形式赋存.前者体积在20~30m3之间,呈顺层和断层两种产状,其明显受煤储层小微构造的控制;后者规模很小,主要分布在内生裂隙缝间,其受煤变质程度和煤岩类型影响.煤层气完井期间,井筒附近构造煤坍塌扩径,构造煤粉源卸压膨胀,煤粉颗粒启动难度降低;压裂期间,在压裂液动压、紊流搅动作用下,煤粉颗粒均匀混合在流体中形成浆体;排采期间,回排的压裂液中煤粉含量较高,导致气井近井部位压裂裂缝及炮眼较早地堵塞,气井减产.而原生裂缝内煤粉在压裂液中通过颗粒疏水相互作用发生聚集,在压裂裂缝内形成堵塞,但堵塞程度较轻.稳定压裂液注入排量、减小流速,利用煤储层小微构造解剖技术预测构造煤粉源集合体发育部位能够降低近井部位堵塞的风险,改善水力压裂效果.     

大学英语泛口语类教材多模态分析与多元识读

通过对现行大学英语泛口语类教材多模态化分析,尝试将多模态话语和多元识读理论运用到大学英语口语教材的编写中,建立多模态语篇并关注语言文字与视听觉模态之间意义建构的互动机制;同时融入多元识读中的＂设计＂理念,让学习者在文化多元性和世界连通性的背景下,通过听觉、视觉、体势、空感等渠道进行多模态学习,并对多媒介信息进行批判性识读。     

基于I-IFHA算子的动态直觉模糊多属性群决策方法

假设各决策时段的时间权重、各决策时段下的属性权重以及决策者权重均已知,各决策者针对各决策时段给出的属性值以直觉模糊数形式给出的动态多属性群决策问题,提出了一种基于导出直觉模糊混合平均(I-IFHA)算子动态直觉模糊多属性群决策方法.该方法利用动态直觉模糊加权平均(DIFWA)算子和动态加权平均(DWA)算子集结动态决策信息分别得到各决策者的综合直觉模糊决策矩阵和综合属性权重向量,并在此基础上利用直觉模糊加权平均(IFWA)算子计算得到各决策者下各方案的直觉模糊评价值;利用I-IFHA算子集结决策者群体决策信息得到各方案的综合直觉模糊评价值;利用直觉模糊数的得分函数及精确函数对决策方案进行排序和择优.最后,通过投资银行对企业进行投资的实例说明了该方法的应用.