An improved iterative stochastic multi-objective acceptability analysis method for robust alternative selection in new product development

Alternative selection in new product development (NPD) is a multi-criteria decision-making (MCDM) problem. It usually starts with incomplete, imprecise or even partially missing information. Currently, most existing methods in dealing with this problem cannot work well if required information is incomplete or missing. It is acknowledged that stochastic multi-objective acceptability analysis (SMAA) can be applied to address MCDM problem with incomplete preference information and uncertain criteria measurements. In SMAA, alternatives are evaluated based on SMAA measurements (acceptability index, central weight vector and confidence factor). The discriminability of SMAA for the optimum alternative heavily depends on differences of SMAA measurements among different alternatives. Usually, a large number of alternatives and high level of uncertainty are involved in alternative selection in NPD. In this situation, the differences among SMAA measurements are not obvious, and therefore SMAA cannot deal with such problem very well. To this end, this paper proposes an improved SMAA method called Iterative-SMAA (I-SMAA) for alternative selection in NPD. In the I-SMAA, an iterative multi-step decision-making process is suggested to improve differences of SMAA measurements among different alternatives, and thus assist decision makers (DMs) to positively discern from the most preferred alternative. To enhance the decision-making efficiency, sensitive criteria are acquired in each iteration by ranking sensitivity analysis. DMs are guided to provide partial preference information and give more accurate criteria measurements for sensitive criteria rather than all criteria. Eventually, to verify the proposed method, a numerical example of the existing literature is solved with the method, and the results are compared. And then, a practical example of a preparation equipment for coal samples is further employed to verify the practicability of the proposed I-SMAA.     

基于云平台的矿井水害智慧应急救援系统与应用

随着云计算、物联网、大数据、人工智能等技术的发展,智慧应急救援已成为矿山安全生产监察的迫切需求。提出了基于云平台的矿井水害智慧应急救援服务体系,采用混合云架构,提供按需和弹性计算能力及存储资源。通过集成与挖掘所有可能的历史和实时数据,建立云环境下多源异构、大规模、跨领域、动态信息资源"一张图",推动信息资源共享,建设应急救援智库。在此基础上,研发了应急救援系统,实现数值模拟、预测预警、应急疏散、应急评估、人员设备管理等功能,支持矿山应急演练和应急决策,依托现代信息技术为矿井水害应急救援提供智能化的服务和管理,实现矿井水害事故预防、检测、预警、处置、评估等全过程动态管理和控制。     

Emergency decision support modeling for COVID-19 based on spherical fuzzy information

Significant emergency measures should be taken until an emergency event occurs. It is understood that the emergency is characterized by limited time and information, harmfulness and uncertainty, and decision-makers are always critically bound by uncertainty and risk. This paper introduces many novel approaches to addressing the emergency situation of COVID-19 under spherical fuzzy environment. Fundamentally, the paper includes six main sections to achieve appropriate and accurate measures to address the situation of emergency decision-making. As the spherical fuzzy set (FS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). First, we discuss basic algebraic operational laws (AOLs) under spherical FS. In addition, elaborate on the deficiency of existing AOLs and present three cases to address the validity of the proposed novel AOLs under spherical fuzzy settings. Second, we present a list of Einstein aggregation operators (AgOp) based on the Einstein norm to aggregate uncertain information in DMPs. Thirdly, we are introducing two techniques to demonstrate the unknown weight of the criteria. Fourthly, we develop extended TOPSIS and Gray relational analysis approaches based on AgOp with unknown weight information of the criteria. In fifth, we design three algorithms to address the uncertainty and ambiguity information in emergency DMPs. Finally, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed three algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.     

苏里格气田气井井口远程控制紧急截断阀改进

苏里格气田管理区域大、气井数量多,目前使用的紧急截断阀存在冬季冰堵、电池亏电造成无法正常远程开关井以及远程数据传输不准确等问题。通过阀门结构改进、密封副结构设计、角位移传感器组件优化和供电系统优化等措施,解决了以上问题,并成功应用于气井井口,使之更好地适用于气井的自动化管理。     

煤矿井下避难硐室系统的构建与应用分析

为进一步减少煤矿事故发生后的人员伤亡、财产损失，在分析国内外煤矿井下避难系统发展的基础上，以何家塔煤矿井下避难硐室系统的构建设计与应用为例，详细介绍了井下避难硐室系统的分类、结构构成、功能需求和配套设施等，并通过紧急避险疏散时间的校验计算，提出避难硐室距离采掘工作面以不超过1 000 m为宜，且应按照“地面最安全，先逃生后避险”原则进行避险救援，为国内其他矿井避难硐室的构建应用和相关标准的制定提供了参考。     

压水堆堆芯损伤评价系统研究与开发

堆芯损伤评价是反应堆事故后应急评价的重要组成部分。本文在国外文献基础上,结合国内的运行经验,对压水堆堆芯损伤评价进行研究,并开发了相应的软件程序。堆芯损伤评价包括基于堆芯裸露时间、在线监测仪表读数和取样分析数据三种方法。考虑应急实时要求、电厂实际情况与国际经验,本文采用了基于在线监测仪表读数的评价方法,该方法主要是基于堆芯热电偶读数与安全壳辐射监测仪表读数进行评价,其他监测仪表读数进行辅助合理性证实。     

海上钻井平台井喷液柱高度的图像识别测量方法

油气井钻井作业井喷事故一旦发生,在抢险人员不能直接靠近井口读取井口压力参数的情况下,制订压井抢险方案的关键是准确测量和判定井喷液柱的高度。为此,基于OpenCV计算机视觉库的图像处理技术建立了井喷液柱高度的测量方法,包括:①在救援船上使用CCD相机对井喷液柱拍摄进行连续瞬态成像,每次对井喷液柱进行拍摄时应分别旋转120°拍摄3张照片;②对图像经过直方图均衡化、高频强调滤波预处理,再经过改进的Otsu算法进行二值化处理,分割出井喷液柱的轮廓;③利用数学形态学运算处理二值图像中的边界点与孔洞,采用参照物法计算出井喷液柱的实际高度。室内模拟实验的结果表明:①该方法对井喷液柱识别的准确率较高,误差约为6%,能够较为精确地获取井喷液柱高度;②实验过程中,处理一幅图像平均耗时0.86 s,可以满足事故发生后对井喷液柱的实时识别与测量要求。结论认为,该方法应用于对井喷液柱高度的测量是可行的,利用其对井喷图像进行程序化处理,可以在海上钻井平台井喷失控后实时连续测量井喷液柱高度,第一时间获取与井喷相关的关键参数,为压井抢险方案的制订提供依据。     

Multicriteria decision-making using Archimedean aggregation operators in Pythagorean hesitant fuzzy environment

In multicriteria decision-making (MCDM), the existing aggregation operators are mostly based on algebraic t-conorm and t-norm. But, Archimedean t-conorms and t-norms are the generalized forms of t-conorms and t-norms which include algebraic, Einstein, Hamacher, Frank, and other types of t-conorms and t-norms. From that view point, in this paper the concepts of Archimedean t-conorm and t-norm are introduced to aggregate Pythagorean hesitant fuzzy information. Some new operational laws for Pythagorean hesitant fuzzy numbers based on Archimedean t-conorm and t-norm have been proposed. Using those operational laws, Archimedean t-conorm and t-norm-based Pythagorean hesitant fuzzy weighted averaging operator and weighted geometric operator are developed. Some of their desirable properties have also been investigated. Afterwards, these operators are applied to solve MCDM problems in Pythagorean hesitant fuzzy environment. The developed Archimedean aggregation operators are also applicable in Pythagorean fuzzy contexts also. To demonstrate the validity, practicality, and effectiveness of the proposed method, a practical problem is considered, solved, and compared with other existing method.