Fuzzy multiple attribute decision-making model for synergic knowledge innovation in supply chain

The procedure of supply chain development is the process of continuously congregating knowledge and transforming knowledge.First,the precondition of synergic knowledge innovation in the supply chain is narrated.Then the characteristics of synergic knowledge innovation in the supply chain are analyzed,including complexity,accumulating and evolving process,and the cooperation of members and network integration.Due to the characteristics of multi-factors and uncertainties of the supply chain system,the fuzzy multi-attribution group decision-making model is introduced to solve the involved problem of synergic knowledge innovation in the supply chain.After elaborating on steps of using the fuzzy multiple attribute decision-making(MADM)model,the procedure of decision making for synergic knowledge innovation in the supply chain is explained from an example in the application of a fuzzy MADM model.The fuzzy MADM model,which amalgamates intuition and resolution decision-making can effectively improve the rationality of decision-making for synergic knowledge innovation in the supply chain.

Air combat decision-making of multiple UCAVs based on constraint strategy games

Game theory can be applied to the air combat decision-making problem of multiple unmanned combat air vehicles(UCAVs).However,it is difficult to have satisfactory decision-making results completely relying on air combat situation information,because there is a lot of time-sensitive information in a complex air combat environment.In this paper,a constraint strategy game approach is developed to generate intelligent decision-making for multiple UCAVs in complex air combat environment with air combat situation information and time-sensitive information.Initially,a constraint strategy game is employed to model attack-defense decision-making problem in complex air combat environment.Then,an algorithm is proposed for solving the constraint strategy game based on linear programming and linear inequality(CSG-LL).Finally,an example is given to illustrate the effectiveness of the proposed approach.

DNA extraction method selection for agricultural soil using TOPSIS multiple criteria decision-making model

There is an increased interest in the extraction of nucleic acids from various environmental samples since culture-independent molecular techniques contribute to deepen and broaden the understanding of a greater portion of uncultivable microorganisms. Due to difficulties to select the optimum DNA extraction method in view of downstream molecular analyses, this article presents a straightforward mathematical framework for comparing some of the most commonly used methods. Four commercial DNA extraction kits and two physical-chemical methods (bead-beating and freeze-thaw) were compared for the extraction of DNA under several quantitative DNA analysis criteria: yield of extraction, purity of extracted DNA (A260/280 and A260/230 ratios), degradation degree of DNA, easiness of PCR amplification, duration of extraction, and cost per extraction. From a practical point of view, it is unlikely that a single DNA extraction strategy can be optimum for all selected criteria. Hence, a systematic Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to compare the methods. The PowerSoil? DNA Isolation Kit was systematically defined as the best performing method for extracting DNA from soil samples. More specifically, for soil:manure and soil:manure:biochar mixtures, the PowerSoil?DNA Isolation Kit method performed best, while for neat soil samples its alternative version gained the first rank.

Cotangent Similarity Measure of Consistent Neutrosophic Sets and Application to Multiple Attribute Decision-Making Problems in Neutrosophic Multi-Valued Setting

A neutrosophic multi-valued set(NMVS)is a crucial representation for true,false,and indeterminate multivalued information.Then,a consistent single-valued neutrosophic set(CSVNS)can effectively reflect the mean and consistency degree of true,false,and indeterminate multi-valued sequences and solve the operational issues between different multi-valued sequence lengths in NMVS.However,there has been no research on consistent single-valued neutrosophic similarity measures in the existing literature.This paper proposes cotangent similarity measures and weighted cotangent similarity measures between CSVNSs based on cotangent function in the neutrosophic multi-valued setting.The cosine similarity measures showthe cosine of the angle between two vectors projected into amultidimensional space,rather than their distance.The cotangent similaritymeasures in this study can alleviate several shortcomings of cosine similarity measures in vector space to a certain extent.Then,a decisionmaking approach is presented in viewof the established cotangent similarity measures in the case of NMVSs.Finally,the developed decision-making approach is applied to selection problems of potential cars.The proposed approach has obtained two different results,which have the same sort sequence as the compared literature.The decision results prove its validity and effectiveness.Meantime,it also provides a new manner for neutrosophic multi-valued decision-making issues.

Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary Optimization

Agent based simulation has successfully been applied to model complex organizational behavior and to improve or optimize aspects of organizational performance. Agents, with intelligence supported through the application of a genetic algorithm are proposed as a means of optimizing the performance of the system being modeled. Local decisions made by agents and other system variables are placed in the genetic encoding. This allows local agents to positively impact high level system performance. A simple, but non trivial, peg game is utilized to introduce the concept. A multiple objective bin packing problem is then solved to demonstrate the potential of the approach in meeting a number of high level goals. The methodology allows not only for a systems level optimization, but also provides data which can be analyzed to determine what constitutes effective agent behavior.

Cooperative Decision-Making for Multiple UAVs Autonomous Confrontation

This paper presents a rule-based framework for addressing decision-making problems within the context of the\UI-STRIVE"Competition.First,two distinct autonomous confrontation scenarios are described:autonomous air combat and cooperative interception.Second,a State-Event-Condition-Action(SECA)decision-making framework is developed,which integrates thefinite state machine and event-condition-action frameworks.This framework provides three products to describe rules,i.e.the SECA model,the SECA state chart,and the SECA rule description.Third,the situation assessment and target assignment during autonomous air combat are investigated,and the mathematical models are established.Finally,the decisionmaking model's rationality and feasibility are verified through data simulation and analysis.

Group Decision-Making Method with Incomplete Intuitionistic Fuzzy Preference Relations Based on a Generalized Multiplicative Consistent Concept

Based on the analyses of existing preference group decision-making(PGDM)methods with intuitionistic fuzzy preference relations(IFPRs),we present a new PGDM framework with incomplete IFPRs.A generalized multiplicative consistent for IFPRs is defined,and a mathematical programming model is constructed to supplement the missing values in incomplete IFPRs.Moreover,in this study,another mathematical programming model is constructed to improve the consistency level of unacceptably multiplicative consistent IFPRs.For group decisionmaking(GDM)with incomplete IFPRs,three reliable sources influencing the weights of experts are identified.Subsequently,a method for determining the weights of experts is developed by simultaneously considering three reliable sources.Furthermore,a targeted consensus process(CPR)is developed in this study with reference to the actual situation of the consensus level of each IFPR.Meanwhile,in response to the proposed multiplicative consistency definition,a novel method for determining the optimal priority weights of alternatives is redefined.Lastly,based on the above theory,a novel GDM method with incomplete IFPRs is developed,and the comparative and sensitivity analysis results demonstrate the utility and superiority of this work.

HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA

Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.

Threat sequencing of multiple UCAVs with incomplete information based on game theory

The threat sequencing of multiple unmanned combat air vehicles(UCAVs) is a multi-attribute decision-making(MADM)problem. In the threat sequencing process of multiple UCAVs,due to the strong confrontation and high dynamics of the air combat environment, the weight coefficients of the threat indicators are usually time-varying. Moreover, the air combat data is difficult to be obtained accurately. In this study, a threat sequencing method of multiple UCAVs is proposed based on game theory by considering the incomplete information. Firstly, a zero-sum game model of decision maker( D) and nature(N)with fuzzy payoffs is established to obtain the uncertain parameters which are the weight coefficient parameters of the threat indicators and the interval parameters of the threat matrix. Then,the established zero-sum game with fuzzy payoffs is transformed into a zero-sum game with crisp payoffs(matrix game) to solve. Moreover, a decision rule is addressed for the threat sequencing problem of multiple UCAVs based on the obtained uncertain parameters. Finally, numerical simulation results are presented to show the effectiveness of the proposed approach.

Aczel-Alsina Weighted Aggregation Operators of Simplified Neutrosophic Numbers and Its Application in Multiple Attribute Decision Making

The simplified neutrosophic number(SNN)can represent uncertain,imprecise,incomplete,and inconsistent information that exists in scientific,technological,and engineering fields.Hence,it is a useful tool for describing truth,falsity,and indeterminacy information in multiple attribute decision-making(MADM)problems.To suit decision makers’preference selection,the operational flexibility of aggregation operators shows its importance in dealing with the flexible decision-making problems in the SNN environment.To solve this problem,this paper develops the Aczel-Alsina aggregation operators of SNNs for MADM problems in view of the Aczel-Alsina operational flexibility.First,we define the Aczel-Alsina operations of SNNs.Then,the Aczel-Alsina aggregation operators of SNNs are presented based on the defined Aczel-Alsina operations of SNNs.Next,a MADM method is established using the proposed aggregation operators under the SNN environment.Lastly,an illustrative example about slope treatment scheme choices is provided to indicate the practicality and efficiency of the established method.By comparison with the existing relative MADM methods,the results show that the established MADM method can overcome the insufficiency of decision flexibility in the existing MADM methods and demonstrate the metric of flexible decision-making.

中国工业低碳转型进程再评估及其多样化分解

工业低碳转型是中国落实“双碳”目标、推进绿色低碳发展的关键所在,全面理解中国工业低碳转型进程具有重要价值。为此,首先构建工业低碳转型进程再评估及其分解的理论框架,随后基于跨国、省份和行业样本数据,采用SBM方向性距离函数和Luenberger生产率指标对2001—2021年中国工业低碳转型进程展开再评估,并从生产技术演化、要素效率变动和经济结构变迁三个维度对该进程进行分解。研究发现,国际视角下的中国工业低碳转型进程在研究期内稳步推进,节奏更为平稳、方式更可持续;国内视角下的中国工业低碳转型进程在研究期内具有先快后缓的推进特征,且省份间和行业间低碳转型分化程度持续加深;经典分解表明技术进步和技术效率提升在推动工业低碳转型过程中无法有效协同,存在普遍的正负或强弱特征;要素分解表明工业低碳转型的驱动要素逐渐多元化,能源效率和碳效率的贡献逐步凸显,但具有一定波动性;结构分解显示区域结构效率总体呈小幅下降趋势,产业结构效率始终为负且后期受政策影响而呈现周期性波动特征。基于研究结论,从协同推进技术进步与技术效率改善、提升区域及行业维度能源效率和碳效率、优化经济结构效率三方面为高质量推进中国工业低碳转型进程提出了具体建议。

交通工程专业设计类课程高质量教材建设刍议

在新工科建设背景下,交通工程专业设计类课程教材建设面临层级目标的设定、教材内容与标准规范的联系、知识表达形式、教材的修编再版等挑战,围绕“层级目标、专业实战、多元资源”课程教材建设目标,创新性提出了对教材内容进行从基础设计能力到创新设计能力的层级模块化编排和设计类教材核心词汇语言表达方式的教材建设思路,以实战为导向探索设计类教材知识多样化表现手法,并针对设计类课程的特点,提出教材“编写—修订—再版”全过程动态监测评估的建设技术路径。

早期多元化活动护理联合EGDS策略在机械通气多发伤患者中的应用

目的探讨机械通气多发伤患者采用早期多元化活动护理联合早期目标导向镇静(EGDS)策略的效果。方法选取2021年1月至2022年1月南昌大学第二附属医院收治的80例行机械通气多发伤患者为研究对象,按照随机数字表法分为对照组(40例)与观察组(40例)。对照组采用常规ICU护理联合标准镇静策略,观察组采用早期多元化活动护理联合EGDS策略,比较两组的镇静程度、临床指标、ICU获得性衰弱、谵妄发生率。结果两组的躁动及深镇静发生率比较,差异无统计学意义(P>0.05);观察组浅镇静达标率高于对照组,差异有统计学意义(P<0.05);观察组机械通气时间、总住院时间及ICU住院时间均短于对照组,肌力评分高于对照组,差异有统计学意义(P<0.05);观察组ICU获得性衰弱及谵妄发生率均低于对照组,差异有统计学意义(P<0.05)。结论机械通气多发伤患者采用早期多元化活动护理联合EGDS策略可达到浅镇静目标,降低ICU获得性衰弱、谵妄发生率风险,改善患者肌力状况,促进患者恢复。

考虑多种工业负荷参与的多时间尺度源荷储优化调度

为解决大规模新能源接入导致传统电网中常规机组调节压力不断增大的问题,提出了一种考虑储能和多种工业负荷参与的多时间尺度优化调度策略。该策略通过协调荷侧电解铝、水泥、钢铁、非生产性负荷与储能以及源侧常规机组调用计划,有效缓解电网调节压力。首先,分析储能和多种工业负荷的调节特性,建立风-光-荷-储的滚动调节框架。然后,针对源荷不确定性,采用多场景随机规划与模糊机会约束目标规划相结合的方法,建立日前和日内阶段以系统经济性最优为目标、实时调度阶段兼顾安全性和经济性的多时间尺度调度模型。最后,通过新能源充足与不足典型日算例可知,本文所提调度策略能够充分发挥可调控资源的调节能力,在两种典型日的总成本较日前最优调度分别降低了17.08%、44.13%,弃新能源量降低39%、失负荷量降低23%,有效提高了电网运行经济性和安全性。

水资源短缺地区多能互补发电与水资源综合利用竞争关系

我国西北地区是“西电东送”的能源基地,亦是水资源匮乏且生态系统较为脆弱的地区,围绕水资源产生的竞争关系相对突出。针对该关系,建立了梯级水库调度模型,以发电量最大、适宜生态偏差系数最小为目标,兼顾多能互补的出力波动平抑要求,获得发电、生态等综合效益较优的调度方案,并剖析水资源短缺条件下发电与生态之间的竞争关系;引入协调性等指标定量评价调度风险,并采用模糊综合评价法计算调度方案的评分,从生态供水风险和多目标竞争关系两个方面评价了调度方案的优劣。以黄河上游为对象的实例研究表明:多能互补发电量越多,生态偏差系数越大,特别是枯水年的生态偏差系数变化较明显,最大值与最小值相差0.069;枯水年不同调度方案的变化过程存在明显偏差,不同方案的生态偏差系数相差超过2%,适当减小发电量可大幅提升生态效益;非劣解前沿端点的评分较低,为0.171,拐点的评分较高,为0.193。

Extension of Paraconsistent Many-Valued Similarity Method to Group Decision

The Paraconsistent Many-Valued Similarity (PMVS) method for multi-attribute decision making will be incomplete as a decision model if it is not extended to the realm of group decision-making. Therefore, in this article, our primary objective is to show how the paraconsistent many-valued similarity method can be used to solve group decision-making problems involving choice making or ranking of a finite set of decision alternatives. Moreover, since weights are very important parameters in multi-attribute decision-making, we have introduced the Borda rule to calculate the weights of experts and that of every criterion under consideration. To demonstrate how the proposed method works, a numerical example on energy sources of an economy from the points of view of a group of experts is investigated. Further, we compare the results of this new approach with that of fuzzy TOPSIS group decision-making method to illustrate the robustness and effectiveness of the former.

思想政治理论课情感目标的价值意蕴与实践进路

情感目标是思想政治理论课教学目标的重要组成部分,具有感性价值、理性价值和现实价值三重意蕴。优化和改进情感目标是提升思想政治理论课亲和力、感染力的内在要求。思想政治理论课情感目标存在重视程度不够、部分内容空泛、难以操作和落实不到位等具体问题。落实思想政治理论课情感目标,应当在思想上重视其价值意蕴、内涵上重塑结构体系、实践中探索实现思想政治理论课情感目标的多元路径,并健全和完善评价方法。

以目标执行理念为基础的健康宣教联合人文关怀干预在多发性骨髓瘤化疗患者中的应用效果

目的探讨以目标执行理念为基础的健康宣教联合人文关怀干预对多发性骨髓瘤(MM)化疗患者的影响。方法选取2021年12月—2023年6月巨野县人民医院血液内科收治的176例MM化疗患者为研究对象,按照随机数字表法将其分为对照组及观察组,每组88例。对照组采用人文关怀干预,观察组在此基础上实施以目标执行理念为基础的健康宣教。比较两组患者的应对方式、疼痛程度及自护能力。结果干预后,观察组的医学应对问卷表中回避、屈服评分均低于对照组,面对评分高于对照组,组间差异有统计学意义(P<0.05);观察组的疼痛视觉模拟评分为(1.14±0.91)分,低于对照组的(3.04±1.05)分,差异有统计学意义(P<0.05);观察组的自我护理能力测定量表评分高于对照组,差异有统计学意义(P<0.05)。结论MM化疗患者采用以目标执行理念为基础的健康宣教结合人文关怀干预,能够提升其自护能力,缓解骨痛程度,促使患者以积极的心态面对疾病。

“双碳”目标下我国绿色金融政策议程分析——基于多源流理论视角

绿色金融的创新支持工具对助推高质量发展、“双碳”目标实现具有强大动能,绿色金融基本政策体系逐步建立。多源流理论模型分析适配性较强,从问题源流分析,全球气候变化、金融行业风险加剧等不同程度推动政策出台;从政策源流分析,政府部门、人大代表和专家学者就丰富完善绿色金融政策方案达成一致;从政治源流分析,绿色金融政策的政治流由执政理念的推动、国民情绪与媒体传播和国际政策的带动构成。独立运行的三股源流在实现“双碳”目标背景下交汇,开启政策之窗,为推动绿色金融政策配套措施出台提供了实践指导。

Linear goal programming approach to obtaining the weights of intuitionistic fuzzy ordered weighted averaging operator

The multiple attribute decision making problems are studied, in which the information about attribute weights is partly known and the attribute values take the form of intuitionistic fuzzy numbers. The operational laws of intuitionistic fuzzy numbers are introduced, and the score function and accuracy function are presented to compare the intuitionistic fuzzy numbers. The intuitionistic fuzzy ordered weighted averaging （IFOWA） operator which is an extension of the well-known ordered weighted averaging （OWA） operator is investigated to aggregate the intuitionistic fuzzy information. In order to determine the weights of intuitionistic fuzzy ordered weighted averaging operator, a linear goal programming procedure is proposed for learning the weights from data. Finally, an example is illustrated to verify the effectiveness and practicability of the developed method.