有资格限制的指派问题的求解方法

在实际的指派工作中，常会遇到某个人有没有资格去承担某项工作的问题，因此，本建立了有资格限制的指派问题的数学模型。在此数学模型中，将效益矩阵转化为判定矩阵，由此给出了判定此种指派问题是否有解的方法；在有解的情况下，进一步将效益矩阵转化为求解矩阵，从而将有资格限制的指派问题化为传统的指派问题来求解。最后给出了一个数值例子来说明这样的处理方法是有效的。     

一种具有区间数信息的多目标指派方法

针对具有区间数信息的多目标指派问题,给出了一种指派方法。首先,将不同类型目标的区间数损益矩阵规范化为区间数成本矩阵,并应用区间数运算法则构建区间数多目标指派问题的总成本矩阵。然后,通过事先定义的任意两个区间数的序关系,将区间数指派问题优化模型转化为一个双目标优化模型,并采用线性加权法将其转化为单目标优化模型来进行求解,同时还考虑了如何处理人员数量与任务数量不相等的情形的指派问题;最后,通过一个实例分析说明了本文给出方法的可行性和有效性。本文的方法丰富了已有的求解方法,具有实际应用价值。     

Vague指派问题的求解方法研究

Vague指派问题的特殊性在于用Vague值表述效益矩阵,进而反映了指派问题中存在的诸多不确定性和模糊性。论文根据Vague值的特点,提出了Vague指派问题的求解转化为经典指派问题思想,进而借助“马太效应”函数、特征值向量和Pareto三种方法实现问题的求解。最后,论文以参考文献中的一组数据为例,采用以上方法进行计算,得到了理想的结果。     

基于熵权多目标决策的方案评估方法研究

给出了一种基于熵权多目标决策的方案评估方法.该方法在只有判断矩阵而没有专家权重的情况下,对有多个评价指标的方案评估问题,通过多对象关于多指标的评价矩阵的熵权计算,对多个合理方案进行优选评估,得出了可信度较高的优选方案,并将这一方法应用在航空装备维修费效益评价实例中.     

多因素指派模型全局优化问题研究

基于多因素资源优化分配问题的不确定性,建立基于区间数型下的不确定多因素指派模型,给出模型建立的理论依据与全局优化算法,拓展区间数型多因素指派模型,解决了不确定条件下多因素资源优化分配问题.考虑多因素影响,基于任务完成效率,以5类任务多因素分配问题为例,获得了指派模型全局优化的解.为不确定条件下资源优化分配问题的研究拓宽了决策途径.     

多目标指派问题在潜艇兵力配置中的应用

运用模糊数学的思想，首先将各目标下的属性值矩阵转化为模糊关系矩阵，再将模糊关系合成矩阵与解决传统指派问题的匈牙利法相结合，提出一种求解多目标指派问题的综合方法：模糊匈牙利法，并结合优化潜艇兵力配置问题进行了应用分析。     

不完全语言信息下的多准则群决策方法研究

针对决策者所给的自然语言信息缺失判断矩阵,提出了一种基于群体满意度最大的不完全语言信息多准则群决策规划模型.首先分析决策者所给的多准则语言评价信息矩阵,进而通过三角模糊数将多准则语言评价信息矩阵转化为三角模糊数矩阵;其次根据满意度函数构建不完全语言信息多准则群决策规划模型;最后通过实例验证本方法的可行性及有效性.实例表明该方法计算简单,易操作.     

基于超效率DEA-IAHP的物流企业绩效评价

本文在介绍超效率数据包络分析法及区间数层次分析法的原理和模型,深入研究DEA-AHP评价方法的基础上,提出了超效率DEA-IAHP方法对物流企业绩效进行评价,改进方法引入超效率DEA方法和区间层次分析法弥补了原方法的不足,其中超效率数据包络分析法弥补了原方法不能对效率均为1的决策单元有效排序的问题,可以对所有决策单元进行总排序;区间层次分析法使用区间数判断矩阵来表达各指标因素对总目标的相对重要程度,这有效地解决了决策者因为对物流企业信息掌握不全而导致的点判断矩阵不可靠的问题,更好地体现了决策者偏好。笔者给出了应用超效率DEA-IAHP方法对物流企业进行绩效评价的基本步骤,并用实例分析体现了该方法的实用性及优越性。     

基于模糊影响图与D-S证据理论的多属性群决策方法

针对影响因素复杂的总体评价问题,提出了一种多属性群决策方法。该方法通过模糊影响图构建多层决策模型,利用模糊关系矩阵传递专家的意见,利用证据理论集结专家意见。该决策方法利用属性间客观存在的模糊关系进行决策,不需要确定决策属性的权重。实例分析表明,本方法可行,有利于降低决策信息的不确定程度。     

城市可持续发展评价体系中指标群组赋权方法研究

在指标赋权的各种方法中,AHP法运用最为广泛,为使各指标权数更具客观性,往往采用群组决策的形式进行判断矩阵的构建。文中以各专家给出的判断矩阵为基础,利用统计分析法进行专家的选择,利用模糊聚类分析原理对专家进行分类、赋权,利用指标序列进行指标增减时的权数修正。最后,以“城市可持续指数”中“城市环境质量”评价为例．对其各指标加以赋权,详细探讨该方法的实施过程。     

基于距离测度及支持度的混合评价信息供应商选择决策

新零售模式的推进迫使企业不断加强供应链节点上的供应商选择优化,是企业新时代发展面临的新课题。针对供应商选择优化提出一种基于距离测度及支持度的群决策方法,根据不同类型属性评价信息的距离测度定义,得到不同决策者之间关于单一属性指标的相互支持度,从而确定单个供应商各属性的群体综合评价值,并采用灰关联法对备选供应商进行排序择优。     

Aggregating crisp values into intuitionistic fuzzy number for group decision making

This paper presents a multiple attribute group decision making model based on aggregating crisp values into intuitionistic fuzzy numbers. First, each alternative is evaluated with respect to their attributes, whose values are provided by decision maker as crisp numbers. Second, to make a reasonable normalization of attribute values in the group decision making environment, a maximum grade and a minimum grade are added to the attribute values. These normalized attribute values are then aggregated (per attribute) into an induced intuitionistic fuzzy number. Each alternative is then evaluated according to the induced intuitionistic fuzzy number. To show the major technical advances in this paper, comparisons with other methods are also made. Finally, an experimental analysis for supplier selection is given to illustrate the reasonableness and efficiency of the introduced method.     

不同直觉偏好信息下的多属性决策方法

研究了属性值为实数且决策者对属性的偏好信息以直觉判断矩阵或残缺直觉判断矩阵给出的模糊多属性决策问题.首先介绍了直觉判断矩阵、一致性直觉判断矩阵、残缺直觉判断矩阵、一致性残缺直觉判断矩阵等概念,而后分别考虑关于直觉判断矩阵和残缺直觉判断矩阵的多属性决策问题,接着建立了基于直觉判断矩阵和残缺直觉判断矩阵的多属性群决策模型,通过求解这些模型获得属性的权重.进而给出了不同直觉偏好信息下的多属性决策方法.最后通过一个例子说明了该方法的可行性和实用性.     

基于QUALIFLEX和LINMAP的简化中性犹豫模糊多属性决策方法

在处理多属性决策问题中,QUALIFLEX是一种非常有用的排序算法。针对属性取值为简化中性犹豫模糊集的多属性决策问题,提出了SNHFS-QUALIFLEX算法。另外考虑到属性权重不确定的情况,将LINMAP扩展到简化中性犹豫模糊集中,定义了符号距离,建立了最优数学规划模型来确定属性权重。最后将SNHFS-QUALIFLEX方法应用到多属性决策实例中,并验证了其可行性和有效性。     

考虑决策者风险偏好的区间直觉模糊多属性群决策方法

提出了一种考虑决策者风险偏好且属性权重信息不完全的区间直觉模糊数多属性群决策方法。同时考虑相似度和接近度,确定每一属性的决策者权重。为了考虑决策者风险偏好对决策结果的影响和避免区间直觉模糊矩阵的渐进性,引入了决策者风险偏好系数,将集结后的综合决策矩阵转换成区间数矩阵。然后,为了客观地求出属性权重信息不完全环境下属性的权重,构建了基于区间直觉模糊交叉熵的属性权重目标规划模型,该模型不仅考虑了评价值的偏差,也强调了评价值自身的可信度。最后,通过研发项目选择问题的实例分析说明了所提方法的合理性和优越性。     

A new multiple attribute group decision making method in intuitionistic fuzzy setting

The multiple attribute group decision making (MAGDM) problem with intuitionistic fuzzy information investigated in this paper is very useful for solving complicated decision problems under uncertain circumstances. Since experts have their own characteristics, they are familiar with some of the attributes, but not others, the weights of the decision makers to different attributes should be different. We derive the weights of the decision makers by aggregating the individual intuitionistic fuzzy decision matrices into a collective intuitionistic fuzzy decision matrix. The expert has a big weight if his evaluation value is close to the mean value and has a small weight if his evaluation value is far from the mean value. For the incomplete attribute weight information, we establish some optimization models to determine the attribute weights. Furthermore, we develop several algorithms for ranking alternatives under different situations, and then extend the developed models and algorithms to the MAGDM problem with interval-valued intuitionistic fuzzy information. Numerical results finally illustrate the practicality and efficiency of our new algorithms.     

Hybrid Multi-attribute Decision Making Method of Electric Coal Procurement in Industry

Electric coal procurement is the basis of electric power production. In this paper, the problem of supplier selection is studied in multi-source procurement of electric coal. Concretely, the index system of supplier selection is presented, including the evaluation attributes of price, quantity, quality, delivery time and the reputation of supplier. Then, the problem of supplier selection is converted into a problem of hybrid multi-attribute decision making, and a projection method based on hybrid technique for order preference by similarity to ideal solution (TOPSIS) is presented to rank all suppliers and select winners. Its decision example is also given to implement the presented decision method and to demonstrate its effectiveness and practicality. This paper gives an effective way to the hybrid multi-attribute decision making for multi-source procurement of electric coal under fuzzy uncertain environment.     

The efficiency of joint decision making in buyer-supplier relationships

This paper examines the effectiveness of joint decision making within 87 pairs of buyer-supplier relationships in manufacturing. Joint decision making is an important attribute of a more cooperative supply chain relationship that may ultimately result in a better performance. Efficiency is modeled as a multiple criteria problem using Data Envelopment Analysis (DEA). Inputs of five kinds of joint decision making activity are examined relative to two measures of output based on the assessment of the buying firm. Three contingent constructs (product customization and innovation, media richness of the communication between buyer and supplier, and continuity in the relationship) are then examined for their impact on the relative performance of each pair. The implications for the management of supply chain relationships and benchmarking of best practice are then discussed.     

A QFD-based fuzzy MCDM approach for supplier selection

Supplier selection is a highly important multi-criteria group decision making problem, which requires a trade-off between multiple criteria exhibiting vagueness and imprecision with the involvement of a group of experts. In this paper, a fuzzy multi-criteria group decision making approach that makes use of the quality function deployment (QFD) concept is developed for supplier selection process. The proposed methodology initially identifies the features that the purchased product should possess in order to satisfy the company’s needs, and then it seeks to establish the relevant supplier assessment criteria. Moreover, the proposed algorithm enables to consider the impacts of inner dependence among supplier assessment criteria. The upper and the lower bounds of the weights of supplier assessment criteria and ratings of suppliers are computed by using the fuzzy weighted average (FWA) method. The FWA method allows for the fusion of imprecise and subjective information expressed as linguistic variables or fuzzy numbers. The method produces less imprecise and more realistic overall desirability levels, and thus it rectifies the problem of loss of information. A fuzzy number ranking method that is based on area measurement is used to obtain the final ranking of suppliers. The computational procedure of the proposed framework is illustrated through a supplier selection problem reported in an earlier study.     

Evaluating and selecting the supplier in detergent production industry using hierarchical fuzzy TOPSIS

Supply chain management has increasingly attracted attention as a systematic approach to integrate the supply chain in order to planning and controlling the materials and information from suppliers to customers. One of the most important issues in supply chain management is selection of the appropriate supplier which has significant effect on purchasing cost decrease and increase in the organization’s competition ability. Selection of the best supplier is naturally complex with no definite structure, and dependent on the type of suppliers’ activity. In the process of decision making about suppliers and many qualitative and quantitative performance indicators such as quality, price, flexibility, and due date should be considered. Then, the supplier selection problem is a multi-criteria decision making problem where numerous methods have been proposed to solve this problem so far. In the current paper, four suppliers of imported raw material “Tripolyphosphate (TPP)” (primary material to produce the detergent powder with a case study in Iran) are evaluated based on 25 effective criteria using the hierarchical fuzzy TOPSIS (HFTOPSIS) approach.