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.

A group decision making method based on double hesitant linguistic preference relations

A simple decision method is proposed to solve the group decision making problems in which the weights of decision organizations are unknown and the preferences for alternatives are provided by double hesitant linguistic preference relations. First, double hesitant linguistic elements are defined as representing the uncertain assessment information in the process of group decision making accurately and comprehensively, and the double hesitant linguistic weighted averaging operator is developed based on the defined operational laws for double hesitant linguistic elements. Then, double hesitant linguistic preference relations are defined and a means to objectively determine the weights of decision organizations is put forward using the standard deviation of scores of preferences provided by the individual decision organization for altematives. Finally the correlation coefficient between the scores of preferences and the scores of preferences are provided by the other decision organizations. Accordingly, a group decision method based on double hesitant linguistic preference relations is proposed, and a practical example of the Jiudianxia reservoir operation alternative selection is used to illustrate the practicability and validity of the method. Finally, the proposed method is compared with the existing methods. Comparative results show that the proposed method can deal with the double hesitant linguistic preference information directly, does not need any information transformation, and can thus reduce the loss of original decision information.

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.

How time-inconsistent preferences influence venture capital exit decisions?A new perspective for grandstanding

Considering that the assumption of time consistency does not adequately reveal the mechanisms of exit decisions of venture capital(VC),this study proposes two kinds of time-inconsistent preferences(i.e.,time-flow inconsistency and time-point incon-sistency)to advance research in this field.Time-flow inconsistency is in line with the previous time inconsistency literature,while time-point inconsistency is rooted in the VC fund’s finite lifespan.Based on the assumption about the strategies guiding future behaviors,we consider four types of venture capitalists:time-consistent,time-point-inconsistent,naïve,and sophisticated venture capitalists,of which the latter three are time-inconsistent.We derive and compare the exit thresholds of these four types of venture capitalists.The main results include:(1)time-inconsistent preferences acceler-ate the exits of venture capitalists;(2)the closer the VC funds expiry dates are,t`he more likely time-inconsistent venture capitalists are to accelerate their exits;and(3)future selves caused by time-flow inconsistency weaken the effect of time-point inconsist-ency.Our study provides a behavioral explanation for the empirical fact of young VCs’grandstanding.

Some properties of linguistic preference relation and its ranking in group decision making

The group decision making problem with linguistic pref- erence relations is studied. The concept of additive consistent linguistic preference relation is defined, and then some properties of the additive consistent linguistic preference relation are studied. In order to rank the alternatives in the group decision making with the linguistic preference relations, the weighted average is first utilized to combine the group linguistic preference relations to one linguistic preference relation, and then the transformation function is proposed to transform the linguistic preference relation to the multiplicative preference relation, and thus the Saaty＇s eigenvec- tor method （EM） of multiplicative preference relation is utilized to rank the alternatives in group decision making with the linguistic preference relations. Finally, an illustrative numerical example is given to verify the proposed method. A comparative study to the linguistic ordered weighted averaging （LOWA） operator method is also demonstrated.

Dynamic programming methodology for multi-criteria group decision-making under ordinal preferences

A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the total inconsistency between the rankings of all alternatives for the group and the ones for every decision maker is defined after the decision maker weights in respect to the criteria are considered. Similarly, the total inconsistency between their final rankings for the group and the ones under every criteria is determined after the criteria weights are taken into account. Then two nonlinear integer programming models minimizing respectively the two total inconsistencies above are developed and then transformed to two dynamic programming models to obtain separately the rankings of all alternatives for the group with respect to each criteria and their final rankings. A supplier selection case illustrated the proposed method, and some discussions on the results verified its effectiveness. This work develops a new measurement of ordinal preferences’ inconsistency in multi-criteria group decision-making （MCGDM） and extends the cook-seiford social selection function to MCGDM considering weights of criteria and decision makers and can obtain unique ranking result.

Preferences for participation in shared decision making of psychiatric outpatients with affective disorders

Objective: To assess preferences for participation in shared decision making in a representative sample of psychiatric outpatients with affective disorders and to understand how clinical and socio-demographic variables influence patients’ preferences for participation. Method: A cross-sectional survey of 172 consecutive psychiatric outpatients with affective disorders attending at Community Mental Health Care setting was carried out. Patients expressed preferences on each of 3 aspects of decision making (seeking information, discussing options, making the final decision). The “CGI Severity and Improvement Scales” and the “Beck Depression Inventory” scale were used for severity assessment. Additionally the “Drug Attitude Inventory”, the “Beliefs about Medicine Questionnaire” and the “Leeds Attitude toward Concordance Scale” were applied to all participants. Effects of variables considered on preferences were assessed using proportional odds regression models. Results: We registered a high response rate of 85%. Nearly all patients (91%) preferred to leave final decisions to their treating psychiatrists and 87% preferred to rely on psychiatrists for medical knowledge rather than seeking their own information. In contrast, 81% of patients preferred to be offered options and to be asked their opinion by their doctors. Gender, age, educational level, number of psychotropics used and belief about psychiatric medication overuse were significant predictors in decision making dimensions considered. Conclusion: Shared decision making approach of patients with affective disorder must take into consideration a more doctor-directed approach preferred by the patients in which the desire to be offered options is not automatically linked with the willingness of taking decisions or getting more knowledge.

Research on the Intelligent Distribution System of College Dormitory Based on the Decision Tree Classification Algorithm

The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects the students’personality traits,causes dormitory disputes,and affects the students’quality of life and academic quality.This paper collects freshmen's data according to college students’personal preferences,conducts a classification comparison,uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation,determines the description rules of students’personal preferences and decision tree classification preferences,completes the conceptual design of the database of entity relations and data dictionaries,meets students’personality classification requirements for the dormitory,and lays the foundation for the intelligent dormitory allocation system.

Multicriteria game approach to air-to-air combat tactical decisions for multiple UAVs

Air-to-air combat tactical decisions for multiple unmanned aerial vehicles(ACTDMU)are a key decision-making step in beyond visual range combat.Complex influencing factors,strong antagonism and real-time requirements need to be considered in the ACTDMU problem.In this paper,we propose a multicriteria game approach to ACTDMU.This approach consists of a multicriteria game model and a Pareto Nash equilibrium algorithm.In this model,we form the strategy profiles for the integration of air-to-air combat tactics and weapon target assignment strategies by considering the correlation between them,and we design the vector payoff functions based on predominance factors.We propose a algorithm of Pareto Nash equilibrium based on preference relations using threshold constraints(PNE-PRTC),and we prove that the solutions obtained by this algorithm are refinements of Pareto Nash equilibrium solutions.The numerical experiments indicate that PNE-PRTC algorithm is considerably faster than the baseline algorithms and the performance is better.Especially on large-scale instances,the Pareto Nash equilibrium solutions can be calculated by PNEPRTC algorithm at the second level.The simulation experiments show that the multicriteria game approach is more effective than one-side decision approaches such as multiple-attribute decision-making and randomly chosen decisions.

The Correlation Coefficient of Hesitancy Fuzzy Graphs in Decision Making

The hesitancy fuzzy graphs(HFGs),an extension of fuzzy graphs,are useful tools for dealing with ambiguity and uncertainty in issues involving decision-making(DM).This research implements a correlation coefficient measure(CCM)to assess the strength of the association between HFGs in this article since CCMs have a high capacity to process and interpret data.The CCM that is proposed between the HFGs has better qualities than the existing ones.It lowers restrictions on the hesitant fuzzy elements’length and may be used to establish whether the HFGs are connected negatively or favorably.Additionally,a CCMbased attribute DM approach is built into a hesitant fuzzy environment.This article suggests the use of weighted correlation coefficient measures(WCCMs)using the CCM concept to quantify the correlation between two HFGs.The decisionmaking problems of hesitancy fuzzy preference relations(HFPRs)are considered.This research proposes a new technique for assessing the relative weights of experts based on the uncertainty of HFPRs and the correlation coefficient degree of each HFPR.This paper determines the ranking order of all alternatives and the best one by using the CCMs between each option and the ideal choice.In the meantime,the appropriate example is given to demonstrate the viability of the new strategies.

基于三边匹配理论的应急救援分配决策

为解决大型地质灾害背景下应急救援初期救援人员、应急救援装备与受灾地点的分配问题,提高应急救援的效率和质量,首先,将应急救援三方主体之间的选择过程描述为三边匹配问题;然后,标准化处理匹配三方主体的多属性偏好评价信息,构造多属性综合偏好得分矩阵,并以系统稳定性和总偏好效用最大化为目标,建立应急救援初期背景下的三边匹配满意稳定模型;最后,通过算例验证模型的可行性和有效性。结果表明:该模型考虑各主体偏好对于资源分配的影响,能实现应急救援背景下救援人员、应急救援装备与受灾地点三方主体的合理匹配,有助于缩短应急救援部署时间,提高应急救援的效率和质量。

考虑用户出行差异与充电决策偏好的电动汽车充电站规划

大规模电动汽车的出行一般具有规律性,但其充电负荷的分布受到用户出行主观意愿的影响。首先考虑到用户在日常活动中存在的出行差异,针对不同性质车辆建立出行需求响应模型;依据用户出行中的决策偏好,确定不同状态下电动汽车的充电路径选择策略;其次,基于各候选站单位时间的最大并行服务量及优化约束对充电站进行容量配置,以用户经济损失与充电站建设运营年均综合成本最低为目标建立充电设施规划模型;最后,采用改进的粒子群算法求解模型,通过具体案例分析验证了所提规划方法的合理性与有效性。

双积分政策下考虑电耗、新能源偏好的决策研究

为探讨汽车制造商面对国内汽车行业电气化转型的生产决策问题,在双积分政策背景下针对制造商、零售商组成的二级供应链构建两种生产情形下不同决策模型,并引入新能源汽车电能消耗和消费者新能源偏好,运用Stackelberg博弈求得不同生产情形中不同决策模式下供应链各成员最优生产决策与收益分析。结果表明:双积分政策的深入实施有利于提高新能源汽车电能消耗水平,降低新能源汽车销售价格,扩大市场规模的同时促使燃油车提高减排水平;车企应选择在集中决策模式下提高新能源车型产能,逐渐减少传统燃油车产量,给供应链中各成员带来更多收益;从短期看制造商继续生产燃油车的生产情形所获收益虽高于停产燃油车情形,但其收益波动较大且制造商停产燃油车全面转型生产新能源汽车后的收益呈稳定增长趋势。

数据驱动下图模型冲突分析决策支持系统构建研究

当今世界由于经济、科技、地缘战略、国际秩序等问题频繁爆发冲突事件,冲突规模正由个体冲突、小规模群体冲突向复杂大规模群体冲突转变。相较于个体间的冲突,大规模群体冲突事件持续时间更长、波及范围更广,易对我国的社会秩序以及经济发展造成恶劣影响。图模型冲突分析(Graph model for conflict resolution,GMCR)理论提供了分析冲突、解决矛盾的有效方案,作为一门专业解决冲突问题的理论工具已经在水资源、环境管理和经济政策等领域得到广泛应用,并取得良好效果。然而,随着冲突事件参与者日渐增多、主体的策略日趋复杂形成了指数级增加的局势,以及主体的偏好行为不确定性加强,传统的决策支持系统GMCRⅡ难以求解此类复杂冲突问题。基于强度偏好冲突分析理论的代数表达,开发了基于.NET平台的冲突分析WEB系统SP-GMCRDSS,该系统包括可行状态生成、状态转移设置、强度偏好序列生成和稳定性分析引擎4个模块,对比现有的系统,SP-GMCRDSS能更高效地辅助冲突分析者解决数据驱动下的大型、复杂的冲突。并且运用文本挖掘技术提取决策者策略数据,辅助分析者确定决策支持系统建模信息的输入,降低模型构建的主观性。最后,通过“兰州水污染冲突事件”演示了该系统的建模、求解以及分析的功能。

数字鸿沟对家庭创业决策的影响——基于CHFS数据的实证研究

数字信息技术的接入和使用能够有效推动创业活动开展,但是不同群体对数字信息技术接触和运用存在明显差异,带来了新的不平等。基于中国家庭金融调查(CHFS)追踪数据,实证研究了数字鸿沟对家庭创业决策的影响。研究结果表明,数字鸿沟显著抑制了家庭创业决策,家庭面临的数字鸿沟越大,做出创业决策的概率越低,经检验结果稳健。这是因为,数字鸿沟削弱了决策者的社会网络,降低了其风险偏好;这种抑制作用在农村家庭、非一线城市家庭、低收入家庭和低学历群体中更强。数字鸿沟主要抑制了家庭机会型创业和初次创业,且对家庭创业绩效和创业意愿也有显著的负向影响。建议采取完善数字基础设施建设、为家庭提供数字技术培训、鼓励和支持创建线上社区和交流平台等措施缩小数字鸿沟。

不同偏好倾向下动力电池供应链定价决策

随着国内外对环境保护与可持续发展的持续关注,动力电池行业日益进入人们视野,其对于推动电动汽车产业、缓解能源危机具有重要意义。基于此研究了自利偏好、汽车制造商单向互惠偏好、动力电池生产商单向互惠偏好和双向互惠偏好四种不同偏好情形下,动力电池生产商与汽车制造商二者的定价决策与利润关系,以期为动力电池生产商与汽车制造商如何建立互惠偏好关系,实现共赢发展提供一些参考。研究表明,在单向互惠偏好情形下,具有互惠偏好倾向的参与方的自身利润会降低;当供应链双方均展现出较高的互惠偏好倾向时,各自的利润和供应链的利润均高于自利偏好下的水平。

社区团购三级供应链定价与渠道决策

社区团购平台进入理性竞争的稳健发展期,如何整合线下便利店渠道优化团长服务,以应对多渠道博弈是社区团购企业关注的重要内容。针对“社区团购+社区便利店”渠道整合有效性问题,基于消费者效用理论和Stakelberg博弈理论,构建由供应商、社区团购零售商和社区便利店的三级供应链博弈模型,对比分析不同渠道策略下供应商和社区团购零售商分别为渠道结构主导者时,供应链最优定价决策和利润分配,研究供应商和零售商博弈情形下社区团购零售商渠道整合策略获得利润增值的市场条件。研究表明,除了在分散渠道策略下的线下实体渠道产品销售价格会下降,社区团购渠道产品销售价格和批发价格都会随团长运营服务水平的提升而升高;供应商和零售商的权力结构并不影响社区团购渠道整合策略的选择,仅影响整合策略后获得利润增值大小;只有在消费者对线上渠道偏好程度较低且团长运营服务水平不太高时,整合策略才对社区团购渠道有利。研究结论不仅实现社区团购三级供应链最优定价决策和利润分配,同时帮助社区团购零售商有效整合社区便利店,提供可靠的理论依据和重要的政策启示。

应急安全投资项目评价决策准则研究

为了提高投资项目决策的科学性,促进大国应急事业的发展,提出了应急安全投资项目评价决策的准则。文章分析了期望损益值法、效用期望值理论和预期理论等三类投资项目决策准则的使用中决策者的行为特征,提出了根据风险决策者的风险类型来决定选用的风险决策理论,以及可用风险偏好规避混合决策者的概念来诠释阿莱悖论问题源于风险偏好规避混合者的决策行为的观点,并运用案例论证了风险偏好规避混合者在期望值附近拐点处的投资风险态度会发生逆转或突变的特征。结果表明预期理论的适用对象是风险偏好规避混合者,在应急预案组合决策中期望值理论与期望效用理论存在无效性,主要源于应急投资备选预案的决策者属于风险偏好规避混合决策者,其通常会选择承担具有随机性的事故损失的方案,也不会选择承担具有确定的事故损失的方案。所以在应急投资组合方案决策过程中,考虑风险偏好规避混合者的属性和依据预期理论作为决策准则具有现实指导意义。

基于零售商公平偏好的供应链减排与定价决策

将零售商公平偏好引入供应链减排博弈中,在碳税规制约束下分析了零售商无公平偏好、制造商考虑零售商公平偏好、制造商不考虑零售商公平偏好三种情况下的供应链减排与定价问题。研究发现:产品减排量与零售商公平偏好负相关,即零售商公平偏好在一定程度上阻碍了产品减排。因此,需要政府宣传低碳产品的优势,使零售商将关注焦点由利润分配的公平性转移到产品的低碳情况上,从而降低其公平偏好,促进产品减排;如果零售商具有公平偏好,此时制造商在决策中考虑其公平偏好的影响,能同时提高制造商和零售商的利润,从而实现双赢。

患者偏好信息在美国医疗产品上市前决策中的应用及启示

目的 通过研究患者偏好信息在美国医疗产品上市前决策中的应用,为我国患者偏好信息的应用提供借鉴。方法 采用文献研究法探究美国患者偏好信息收集与应用的概况、法律依据和参与主体,详细分析患者偏好信息在医疗产品上市前决策中的应用情况,分析我国患者偏好信息的应用现状与挑战,并提出建议。结果与结论 美国通过开展多项患者参与项目以及立法推动多主体进行患者偏好信息的收集和应用,将患者偏好信息用于为医疗产品上市前决策提供参考:在研发环节为医疗产品研发和设计提供信息、辅助临床试验设计;在上市审批环节作为FDA上市审批决策的证据之一,用于识别获益大于风险的患者群体,并纳入医疗产品说明书。我国患者偏好信息的应用缺乏更高层级法律文件的指导,尚无针对性的指南文件。建议我国借鉴美国经验,在更高层级的法律文件中明确鼓励患者偏好的研究与应用;针对患者偏好信息的收集与应用制定专门的指导文件;根据我国国情确定监管决策中患者偏好信息纳入的权重和形式。