The Research on and Application of the Multi-regression Technique in the Course of the Marketing Decision-making of Enterprises

The reformation of the economy system has led the f un ctional department and status of the enterprises into a variable state. Under th e condition of the market economy, the kernel of the enterprises’ functional dep artment has diverted to that of marketing decision-making, which face to market and meet with the need of consumption. Assuredly, the kernel of marketing decis ion-making is to prognosticate the future market demand of the production of en terprises accurately, so that it can ensure and realize the maximum of the enter prises’ profit increase. Using empirical research and the multi-regression technique, this paper ana lyzes the enterprises’ production demand forecast of the GMC (Global Management Challenge, held every year globally) and changes most of uncontrollable factors of demand forecast to the controllable ones of the enterprises. The method we us ed to forecast demand by using the multi-regression technique is as follows: 1. Look for the main factors which influence the demand of productions; 2. Establish the regression model; 3. Using the historical data, find the resolution of the correlative index an d do the prominent test; 4. Analyze and compare, regression, adjust parameter and optimize the regress ion model. Our method will make the forecast data closer to the actual prices of the future market requirement quantity in the production marketing decision-making of the enterprises and realize the optimizing combination and the working object w ith the minimum of the cost and the maximum of the profit. And it can ensure the realization of the equity maximum of the enterprises and increase the lifecycle of the production.

Extension of TOPSIS for fuzzy multi-attribute decision making problem based on experimental analysis

This paper is concerned with a technique for order performance by similarity to ideal solution（TOPSIS） method for fuzzy multi-attribute decision making,in which the information about attribute weights is partly known and the attribute values take form of triangular fuzzy numbers.Considering the fact that the triangular fuzzy TOPSIS results yielded by different distance measures are different from others,a comparative analysis of triangular fuzzy TOPSIS ranking from each distance measure is illustrated with discussion on standard deviation.By applying the most reasonable distance,the deviation degrees between attribute values are measured.A linear programming model based on the maximal deviation of weighted attribute values is established to obtain the attribute weights.Therefore,alternatives are ranked by using TOPSIS method.Finally,a numerical example is given to show the feasibility and effectiveness of the method.

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.

Ranking approach of cross-efficiency based on improved TOPSIS technique

The cross-efficiency evaluation method is reviewed which is developed as a data envelopment analysis （DEA） extensive tool. The cross-efficiency evaluation method is utilized to identify the decision making unit （DMU） with the best practice and to rank the DMUs by their respective cross-efficiency scores. The main drawbacks of the cross-efficiency evaluation method when the ultimate average cross-efficiency scores are used to evalu- ate and rank the DMUs are also pointed out. With the research gap, an improved technique for order preference by similarity to ideal solution （TOPSIS） is introduced to rank the crossfficiency by eliminating the average assumption. Finally, an empirical example is illustrated to examine the validity of the proposed method.

Decision making tools for optimal material selection:A review

The present work reviews different decision making tools(material comparing and choosing tools)used for selecting the best material considering different parameters.In this review work,the authors have tried to address the following important enquiries:1)the engineering applications addressed by the different material choosing and ranking methods;2)the predominantly used decision making tools addressing the optimal material selection for the engineering applications;3)merits and demerits of decision making tools used;4)the dominantly used criteria or objectives considered while selecting a suitable alternative material;5)overview of DEA on material selection field.The authors have surveyed literatures from different regions of the globe and considered literatures since 1988.The present review not only stresses the importance of material selection in the early design stage of the product development but also aids the design and material engineers to apply different decision making tools systematically.

TOPSIS with Belief Structure for Group Belief Multiple Criteria Decision Making

The technique for order performance by similarity to ideal solution （TOPSIS） is one of the major techniques in dealing with multiple criteria decision making （MCDM） problems, and the belief structure （BS） model has been used successfully for uncertain MCDM with incompleteness, impreciseness or ignorance. In this paper, the TOPSIS method with BS model is proposed to solve group belief MCDM problems. Firstly, the group belief MCDM problem is structured as a belief decision matrix in which the judgments of each decision maker are described as BS models, and then the evidential reasoning approach is used for aggregating the multiple decision makers＇ judgments. Subsequently, the positive and negative ideal belief solutions are defined with the principle of TOPSIS. To measure the separation from ideal solutions, the concept and algorithm of belief distance measure are defined, which can be used for comparing the difference between BS models. Finally, the relative closeness and ranking index are calculated for ranking the alternatives. A numerical example is given to illustrate the proposed method.

A Load-Fairness Prioritization-Based Matching Technique for Cloud Task Scheduling and Resource Allocation

In a cloud environment,consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost.On the other hand,Cloud Service Providers(CSPs)seek to maximize their profits by attracting and serving more consumers based on their resource capabilities.The literature has discussed the problem by considering either consumers’needs or CSPs’capabilities.A problem resides in the lack of explicit models that combine preferences of consumers with the capabilities of CSPs to provide a unified process for resource allocation and task scheduling in a more efficient way.The paper proposes a model that adopts a Multi-Criteria Decision Making(MCDM)method,called Analytic Hierarchy Process(AHP),to acquire the information of consumers’preferences and service providers’capabilities to prioritize both tasks and resources.The model also provides a matching technique to assign each task to the best resource of a CSP while preserves the fairness of scheduling more tasks for resources with higher capabilities.Our experimental results prove the feasibility of the proposed model for prioritizing hundreds of tasks/services and CSPs based on a defined set of criteria,and matching each set of tasks/services to the best CSPS.

The Impact of Simulation Education on Self-Efficacy in Pre-Registration Nursing Students

This literature review primarily aims to explore and synthesise the previous studies in simulation education research conducted over the past five years related to the effects of simulation training on the self-efficacy of undergraduate pre-registration nursing students. The second aim of this study is to explore additional outcome variables that were examined in the previous studies. Five electronic databases were searched systematically. These databases were MEDLINE, CINAHL Plus, Scopus, Embase and PsycINFO. The PICO model was employed to identify the search terms, with a thesaurus being used to provide synonyms. Reference lists of relevant articles were examined and hand searches of journals were also undertaken. The quality of each study was assessed using the Simulation Research Rubric (SRR). A total of 11 studies were included. All studies explored the impact of simulation education on undergraduate pre-registration nursing. Six studies explored nursing students’ competence and performance and two papers examined their critical thinking. Problem solving, learning motivation, communication skills and knowledge acquisition were examined once. The majority of studies indicated that simulation training has a positive impact on pre-registration nursing students’ self-efficacy and other outcome variables. Furthermore, the study results indicate that simulation training is more dependable than traditional training, and students were extremely satisfied with the simulation training. However, most of the studies included in this review had several gaps, including study design, sample size and dissimilarities between the scales used. Further research with large samples, reliable and valid instruments, and outcomes measures (such as critical thinking and transferability of skills) is required to provide better insight into the effectiveness of simulation in undergraduate nursing education. .

C^2WH-YMK模型及其在制造业发展评价中的应用

数据包络分析是一种非参数的统计分析的新方法.考虑到现有的具有多个独立子系统的YMK-DEA模型不能体现决策者的偏好,建立了体现决策者偏好的锥比率C2WH-YMK模型,并将它应用于中国制造业发展效率评价的研究中.

战略采购中供应商选择的AHP-PROMETHEE方法研究

在供应链环境下,战略采购的出现给供应商群体的优化设计提出了一些新问题,并且多准则决策理论(MCDM)在供应商选择中并没有得到充分运用;本文结合战略采购的特点,选择指标着重体现供应商的合作性和长期性,选择方法上结合了层次分析法(AHP)和偏好顺序结构评估法(PROMETHEE),并进行了算例分析;旨在对战略采购中的供应商选择进行合理、客观的判断。

基于AHP-TOPSIS的网络化弹药攻击决策算法

针对网络化弹药攻击决策问题,提出了基于AHP-TOPSIS的网络化弹药攻击决策算法.分析了影响网络化弹药攻击决策的关键属性,利用AHP算法构建层次结构,构造判断矩阵,确定各属性的权重,进而使用改进的TOPSIS算法得到决策方案的排序结果.仿真结果表明,算法具有计算量低、实时性高的特点;算法能够有效解决多节点攻击决策问题,并有效抑制了逆序现象,具有一定稳健性和实用性.

PERT网络时间-费用优化数学模型及程序实现

根据公路工程的特点，将公路工程项目进行工序分解，利用网络计划图将其连接成一个整体。提出了网络计划技术中各工序均具有非增线性型时间－费用关系时的时间－费用优化数学模型，通过计算机程序处理，方便快捷地得出各工序实施的控制性和非控制性时间参数，按照不同的指令工期对工程项目进行优化处理，并能得出完成工程任务的最低成本和最低成本日程。为决策者在承担工程任务和实施工程计划时进行决策和分析提供有效的支持。

VERT-3仿真技术的发展及其在管理中的应用

风险评审技术(VERT-3)是以随机型网络为基础的计算机化仿真技术。它成功地应用了概率论、仿真原理和网络理论,既能用于计划决策,又能用于管理控制。它可用来确定新项目的风险和未来投资的需要量,从而对将进行的项目、计划和系统作出全面评价。 本文介绍VERT-3的功能和基本结构等方面的基本内容,并以电力产生方法的评价为例介绍了这种技术的实际应用。

基于组合赋权-TOPSIS法的拆卸方案决策研究

拆卸是回收和再制造的重要步骤,为了使拆卸方案达到高效率、低排放以及高经济效益的目标,文章建立一种基于组合赋权-TOPSIS(technique for order preference by similarity to an ideal solution)法的拆卸方案决策模型。针对拆卸方案,从环境、技术和经济3个角度构建拆卸方案评价指标体系;依据最小信息熵原理,综合改进层次分析法(analytic hierarchy process,AHP)、熵权法与CRITIC(criteria importance through intercriteria correlation)法,为各指标进行组合赋权;使用组合权重改进TOPSIS模型,构建拆卸方案的贴近度指标,对拆卸方案进行判定;以银行自动取款机(automated teller machine,ATM)拆卸为实例进行分析,并与其他决策模型对比,验证所提决策模型的合理性及有效性,实现多拆卸方案的决策。

基于广义正交三角模糊数的WSM-TOPSIS群决策方法

结合广义正交模糊集和三角直觉模糊数,给出广义正交三角模糊数的定义和运算法则,在此基础上,对WSM和TOPSIS方法进行拓展,提出一种多属性群决策方法WSM-TOPSIS。考虑到决策者和属性的权重,该方法使用WSM对决策者提出的决策矩阵进行第一次集结,通过TOPSIS计算各方案的相对贴近度得到方案的优劣排序。最后通过实例和对比分析验证了该方法的有效性和实用性。

基于D-AHP与TOPSIS的突发事件应急管理能力评估

在突发事件应急管理评估过程中,参评专家经验及语言描述差异会导致评估信息存在不确定性和模糊性,直接影响评估结果。针对该问题,基于D数偏好关系改进层次分析法(D-AHP)和逼近理想解排序法(TOPSIS),提出一种突发事件应急管理能力评估方法。根据应急管理实际情况建立评估指标体系,通过构建D-AHP层次结构模型求解各评估指标的影响权重,同时结合专家的评估结果,利用TOPSIS方法对评估对象的突发事件应急管理能力水平进行排序。对某市下辖4个区县的评估结果表明,该方法能有效识别应急管理过程中的关键环节,直观展示各个评估对象的实际水平并有助于给出完善应急管控建设的针对性建议。

糖尿病足住院患者决策信息需求问题提示列表的构建及可行性评估

目的 构建糖尿病足住院患者决策信息需求问题提示列表,为促进其参与医疗决策提供实用工具。方法 通过查阅文献、咨询临床一线的专科医护人员、半结构式访谈、德尔菲专家函询并经过临床调适后确定问题提示列表患者版本,便利抽样法选取39例住院患者对问题提示列表在临床使用的可行性进行评估。结果 2轮专家函询的积极系数分别为95.00%和100.00%,专家权威系数为0.951、0.948;第2轮专家函询,指标重要性及相关性的肯德尔协调系数分别为0.214、0.200(均P<0.05)。最终的问题提示列表包括6个一级指标和43个二级指标。41.03%~69.23%的患者认可问题提示列表的临床使用效果。结论 糖尿病足住院患者决策信息需求问题提示列表具有可行性,能够帮助住院患者提出更多疾病相关问题,改善医患沟通,促进患者参与医疗决策。

Pythagorean模糊环境下的三支多属性决策方法

将后悔理论与逼近理想解排序(TOPSIS)法相结合,提出了一种新的三支多属性决策方法,旨在减少决策者的后悔情绪,提高决策满意度.首先,通过评价信息表确定各属性下备选方案的状态集,计算各方案的条件概率.其次,结合TOPSIS和后悔理论,计算相对损失函数,进而确定期望损失,并建立三支决策规则,对方案进行排序并划分为接受域、延迟决策域或拒绝域.最后,以城市轨道交通站点选择为例,进行案例对比分析,验证了该方法的可行性和有效性;接着进行参数分析,以验证该方法的稳定性.

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent systems.To tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency.These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state.In addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning.MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels.The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

近十年对抗性项目运动员感知运动行为优势特征的研究进展与展望

感知并预判对手动作以做出应对决策是对抗性项目运动员执行动作反应的基础。采用系统综述方法,梳理近十年关于对抗性项目运动员感知运动行为的研究,从感知、预判和决策3个阶段概述运动员专项与非专项感知运动行为的特征及其认知神经机制。发现:运动员在感知和预判阶段通过投入更多认知资源以获取关键视觉线索和处理冲突情境,在决策阶段通过激活内部模型完成自动化决策,体现了较高的神经效率,反映了运动员感知运动行为全过程的不同脑神经活动特征。随着认知神经科学、计算机和可穿戴设备等新兴技术的发展,未来研究可利用机器学习和大数据模型等算法模拟和预测运动行为,揭示运动训练对人脑的可塑性规律,提升研究成果的解释力。