基于G-TOPSIS法的企业生产系统柔性综合评价研究

柔性是生产系统应付动态多变的市场需求的重要手段之一,对提高生产系统的适应性和稳定性至关重要。通过对企业生产系统柔性的影响因素分析,建立企业生产系统柔性的综合评价模型。由于对企业生产系统柔性的认识处于一种灰色的状态,故在求解过程中用灰色关联度来确定各指标的权重,用逼近理想解的排序法(TOPSIS法)对数学模型求解,使得对企业生产系统柔性评价更科学、客观。     

对方案有偏好的TOPSIS供应商选择方法

基于供应链合作伙伴关系的概念,提出供应商选择的常用方法,重点研究了TOPSIS法（逼近于理想解的排序方法）在供应商选择中的应用。文章在传统TOPSIS法的基础上,引入了偏好信息为效用值的概念,从而使权重系数的确定兼顾了主客观因素,使得该方法更具实际使用价值。     

混合TOPSIS方法在货位优化方案选择中的应用

货位优化方案的选择是自动化立体仓库中的重要决策。货位布局合理关系着整个库内作业的效率。根据货位布局应遵循的原则。建立了选择方案的指标体系。考虑到信息的不确定性．以区间数形式表示定性指标值,应用混合型TOPSIS方法建立了目标优选的模型。最后通过实例对方法的有效性和实用性进行了验证。     

中国西部省际旅游竞争力评价——基于熵权TOPSIS法

构建旅游竞争力评价模型,通过熵值法客观确定指标权重,运用TOPSIS评价方法测度我国西部各省区的旅游竞争力,通过旅游业绩、旅游潜力和旅游支持3个细分指标揭示西部省区间旅游产业发展水平的差异。研究结果显示：我国西部12省旅游竞争力差异性比较突出;西南地区各省份的旅游竞争力显著高于西北地区;在3各细分指标方面,除四川、云南、重庆和广西4省之外,其他省份都呈现出较明显的不均衡性;西部各省旅游竞争力整体呈上升发展趋势。     

基于DEA/TOPSIS组合模型的钢铁企业绿色供应商选择

钢铁企业绿色供应商选择是一个典型的多目标、多准则、非结构化的问题。基于DEA/TOPSIS的组合模型,实现对DEA结果的进一步优选,算例验证了该模型的有效性。组合模型为供应链管理提供了一种新的供应商选择方法。     

基于环境因素的企业项目投资决策方法研究

随着企业竞争开始从以产品为重心转向以项目为重心,以及投资项目对生态环境的破坏日益严重的现象,环境因素在企业项目投资决策中占有的地位日益重要。在当前应用较为广泛的折现现金指标的基础上,建立起基于环境因素的企业项目投资决策方法。通过熵权法将企业项目财务收益水平与生态环境质量结合,为企业项目投资决策和未来研究提供参考。     

湖南省主体功能区的规划实施绩效评估研究

为合理评价和提升主体功能区规划的实施绩效,从经济效益、社会效应及生态环境3个维度构建了不同主体功能区实施绩效评估指标体系,采用TOPSIS模型和GIS技术建立了绩效评估体系,并以湖南省主体功能区进行了实证研究。研究结果显示:(1)湖南省不同功能区实施绩效结果呈现中、东部高,西北和西南低的空间分布,84个县(市)为中级和良好水平,占全省总县(市)个数比重的68.85%;(2)湖南省实施绩效结果由好到差依次为重点开发区、农产品主产区、生态功能区,应加强对生态功能区的各种政策落实提高实施效果。说明构建的评价体系可反映同一功能区内各县市实施相对绩效,有针对性的找出规划实施影响因素,为规划调整提供参考。     

Predicting Efficiency in Angolan Banks: A Two‐Stage TOPSIS and Neural Networks Approach

This paper presents an efficiency assessment of the Angolan banks using Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). TOPSIS is a multi‐criteria decision‐making technique similar to data envelopment analysis, which ranks a finite set of units based on the minimisation of distance from an ideal point and the maximisation of distance from an anti‐ideal point. In this research, TOPSIS is used first in a two‐stage approach to assess the relative efficiency of Angolan banks using the most frequent indicators adopted by the literature. Then, in the second stage, neural networks are combined with TOPSIS results as part of an attempt to produce a model for banking performance with effective predictive ability. The results reveal that variables related to cost structure have a prominent negative impact on efficiency. Findings also indicate that the Angolan banking market would benefit from higher level of competition between institutions.     

Local Action Groups and Rural Sustainable Development. A spatial multiple criteria approach for efficient territorial planning

Rural Sustainable Development is a very important topic under the European Union policy, and it is currently promoted through the European Agricultural Fund for Rural Development 2014–2020. This fund is managed at sub-regional level by the Community-Led Local Development approach that involves Local Action Groups in order to promote the objectives of Rural Sustainable Development within rural municipalities. Each Local Action Group applies the Strengths, Weaknesses, Opportunities and Threats analysis in order to identify for its own rural municipalities the strategic elements to which it will allocate the European Agricultural Fund for Rural Development budget. Nevertheless, this analysis has some general shortcomings, including difficulties in managing a large number of Strength and Weakness factors. In addition, the importance of each factor cannot be measured quantitatively, and the same factor may be characterized both as a Strength and a Weakness. Further difficulties may occur in the case of partnerships between different Local Action Groups, such as disagreement about whether a given factor is a Strength or a Weakness, lack of information about the relationships between Strength and a Weakness factors and decision alternatives, as well as impossibility of ranking the decision alternatives.Thus, this research aims to overcome the drawbacks of the Strengths, Weaknesses, Opportunities and Threats analysis and to support Local Action Group partnerships in the sustainability evaluation of their rural municipalities, and therefore to aid the identification of a common Rural Sustainable Development strategy to allocate the European Agricultural Fund for Rural Development budget. This decision problem was tackled by applying a Multiple Criteria Spatial Decision Support System that integrates a Geographic Information System with the Multiple Criteria Decision Aiding methods “Technique for Order Preference by Similarity to Ideal Solution” and “Dominance-based Rough Set Approach”.In order to demonstrate the validity of this methodological approach, this Multiple Criteria Spatial Decision Support System was applied to a study area of thirteen rural municipalities located in Apulia Region (Southern Italy); these municipalities belong to the same landscape unit, but they are managed by five different policy makers that represent the Local Action Groups. The results provided the maps of environmental, economic and social sustainability rankings of rural municipalities as well as their overall sustainability value. Based on these rankings, a specific Rural Sustainable Development strategy was identified for the allocation of the European Agricultural Fund for Rural Development. This methodology provided a common decision making framework that can also be applied to Local Action Group partnerships within the European Union.     

A data envelopment analysis game theory approach for constructing composite indicator: An application to find out development degree of cities in West Azarbaijan province of Iran

Data envelopment analysis (DEA) model has been widely applied for constructing composite indicator and finding development degree of areas. With the increasing number of indicators, the distinguish power of DEA model is decreased. In this paper, in order to increase distinguish power in DEA model and find out the fair weights in cross-efficiency DEA context, the game theory approach is applied. The DEA-Game theory approach is used to rank cities in West Azarbaijan province of Iran. First, 68 suitable indicators are determined and then, the indicators are classified in 10 sectors. Finally, the actual data for year 2013 is gathered and DEA-Game theory model is applied. To verify and validate the DEA-Game theory approach, simple additive weighting (SAW) and TOPSIS methods are used and the results are compared. The Spearman correlation between DEA-Game, SAW and TOPSIS models shows that the DEA-Game theory model is suitable for constructing the composite indicators.