基于AHP-PROMETHEEII的外包信息系统脆弱性评价模型

针对外包信息系统脆弱性评价问题,从技术脆弱性和管理脆弱性两个方面建立信息系统脆弱性评价指标体系.在对比分析层次分析方法和偏好顺序结构评估方法的基础上,提出一种基于层次分析方法和偏好顺序结构评估方法相结合的外包信息系统脆弱性评价方法.用层次分析方法确定外包信息系统脆弱性评价问题的层次结构和指标权重,用偏好顺序结构评估方法对候选外包信息系统脆弱性进行最终的评价,并进行灵敏度分析.最后结合案例分析说明该方法的有效性.     

供应商选择的AHP／PROMETHEE决策方法

针对企业界和理论界十分关注的供应商选择决策问题。在对比分析AHP和偏好顺序结构评估方法特点的基础上，提出了一种基于AHP与偏好顺序结构评估法相结合的供应商选择方法。以质量、经济因素、地理位置、响应能力与供应能力因素作为评价准则，用AHP确定供应商选择问题的层次结构及其评价准则权重，用偏好顺序结构评估法对候选供应商进行最终的评价．并结合案例分析说明该方法的有效性．最后给出了未来的研究方向。     

组合评价方法在银行信用风险评价中的应用

针对贷款客户评价选择问题,在对已有研究方法分析的基础上,提出了以熵权与偏好顺序结构评估法相结合的排序方法。以借贷人的财务状况、借贷人的管理水平、借贷人的行业地位、借贷人资产的变现性和借贷人历史信用状况为评价准则,利用信息熵来确定评价准则的权重,用偏好顺序评价法确定贷款客户的贷款顺序,并进行了灵敏度分析。最后通过算例证明了该方法的有效性。     

基于ANP的信息系统外包提供商选择评价与决策过程

随着企业信息化的不断推进,信息系统外包必将成为企业实施信息化项目的一种主要趋势。企业如何合理、准确地评价和选择信息系统外包提供商,将是决定企业实施信息化成功与否的关键。为此,在讨论了信息系统外包提供商选择流程的基础上,本文主要建立了提供商评价指标体系,构造了基于ANP法的决策模型,并对外包提供商进行了评价与决策。算例结果表明,ANP法的决策思路具有很好的实际参考价值。     

层次分析法在审计外包决策中的应用

内部审计外包需要明确外包的具体内容，层次分析法适合运用于内部审计外包内容的选择决策。层次分析法是一种定性与定量相结合的系统化决策方法，它将决策者的主观判断与实践经验导入模型，并进行量化处理，体现了决策中分析、判断、综合的基本特征。通过建立层次模型，将内部审计外包内容决策统一到一个模型中，从而解决多因素分析时所遇到的复杂问题，为企业进行内部审计外包内容的选择提供一些依据。     

基于层次分析法的企业IT外包风险评价分析

针对企业IT外包过程中存在的风险,本文首先利用文献研究总结的方法识别出IT外包的风险因素,建立了比较完整的风险指标体系,接着介绍了层次分析法的一般原理和步骤,而后在层次分析法的基础上对存在的风险建立风险评价模型,从而为企业在进行IT外包决策时提供参考依据。     

论“单一外包商”与“多外包商”两种外包战略的选择

随着资源外包关系的日益复杂化,单一外包商与多外包商的数量确定成为外包决策中的一个重要问题.本文针对这两种战略的选择问题,通过建立"两阶段外包决策分析模型",从知识专用性影响的角度出发,对两种不同战略进行交易成本分析,进而总结出两种战略不同的适用条件,以期为外包商数量的决策提供一定的指导.     

基于MDHGF的物流业务外包风险评估

面对日趋剧烈的竞争,企业越来越重视集中自己的主要资源和业务,而将其它资源与业务外包.但将业务外包过程中存在着风险,有些可能是致命的,本文将讨论采用MDHGF法,即将Delphi方法、层次分析法、灰色关联和模糊评价方法结合起来,进行风险的模糊评估,从而为企业是否将物流业务外包提供决策支持.     

外包信息系统脆弱性评价模型研究

针对外包信息系统脆弱性评价问题,从技术脆弱性和管理脆弱性两个方面提出了信息系统脆弱评价指标体系。在此基础上,给出外包信息系统脆弱性评价流程,构建基于技术脆弱性和管理脆弱性的二维评价矩阵模型。最后通过一个制造企业的电子商务外包案例说明该评价模型的科学性和有效性。     

论传统企业电子商务外包的过程及控制

电子商务外包是传统企业发展电子商务的有效手段.本文以外包理论为基础,结合传统企业电子商务的特点,对电子商务外包的过程及管理控制要点进行了分析.电子商务外包的过程可以分为电子商务外包业务确定、电子商务外包商选择、电子商务外包实施、电子商务外包绩效评估等4个阶段,企业首先要确定拟外包的电子商务业务,然后根据自身需要选择合适的电子商务外包商.在电子商务外包的实施过程中要考虑电子商务业务与企业线下业务的整合,同时要注意控制电子商务外包的风险.最后,企业要对电子商务外包的绩效进行评估,为下一步的决策提供依据.     

Best-worst multi-criteria decision-making method

In this paper, a new method, called best-worst method (BWM) is proposed to solve multi-criteria decision-making (MCDM) problems. In an MCDM problem, a number of alternatives are evaluated with respect to a number of criteria in order to select the best alternative(s). According to BWM, the best (e.g. most desirable, most important) and the worst (e.g. least desirable, least important) criteria are identified first by the decision-maker. Pairwise comparisons are then conducted between each of these two criteria (best and worst) and the other criteria. A maximin problem is then formulated and solved to determine the weights of different criteria. The weights of the alternatives with respect to different criteria are obtained using the same process. The final scores of the alternatives are derived by aggregating the weights from different sets of criteria and alternatives, based on which the best alternative is selected. A consistency ratio is proposed for the BWM to check the reliability of the comparisons. To illustrate the proposed method and evaluate its performance, we used some numerical examples and a real-word decision-making problem (mobile phone selection). For the purpose of comparison, we chose AHP (analytic hierarchy process), which is also a pairwise comparison-based method. Statistical results show that BWM performs significantly better than AHP with respect to the consistency ratio, and the other evaluation criteria: minimum violation, total deviation, and conformity. The salient features of the proposed method, compared to the existing MCDM methods, are: (1) it requires less comparison data; (2) it leads to more consistent comparisons, which means that it produces more reliable results.     

Multiple Criteria Hierarchy Process with ELECTRE and PROMETHEE

Robust Ordinal Regression (ROR) supports Multiple Criteria Decision Process by considering all sets of parameters of an assumed preference model, that are compatible with preference information elicited by a Decision Maker (DM). As a result of ROR, one gets necessary and possible preference relations in the set of alternatives, which hold for all compatible sets of parameters, or for at least one compatible set of parameters, respectively. In this paper, we propose an extension of ELECTRE and PROMETHEE methods to the case of the hierarchy of criteria, which was never considered before. Then, we adapt ROR to the hierarchical versions of ELECTRE and PROMETHEE methods.     

Performance management of suppliers in outsourcing project: case analysis from the financial services industry

This study investigates the characteristics of an effective performance management framework for outsourcing projects in a UK-based financial services organisation and how this may contribute to the success of the outsourcing arrangement. The analysis draws on outsourcing and performance management theory, and uses both primary and secondary data. Valuable information was found on objective setting, performance measurement and performance improvement in the outsourcing project. An adapted version of the Balanced Scorecard, termed a Logic Scorecard, is suggested as a measurement tool; a service credit system and a continuous improvement schedule used to enhance supplier performance. The performance management framework, which is one of the pillars of the supply chain operations reference (SCOR) model, was developed, and provides practitioners with step-by-step guidance for the implementation of performance management in outsourcing projects. This combines both suggestions for performance management before and after the outsourcing decision, thus considering the entire outsourcing lifecycle. The proposed 10-step framework for outsourcing not only incorporates strategic propositions but also shows its implementation at an operational level.     

科学基金项目立项评估:综合评价信息可靠性的多指标证据推理规则研究

公正合理的科研项目立项评估与选择是国家自然科学基金管理活动的关键环节。综合考虑科研项目的评估指标体系和选择流程,提出了利用历史评估准确性度量专家提供信息可靠性的方法,进而提出了一种系统性的基于证据推理规则的科学基金项目评估决策模型。该模型使用证据推理合成规则对多专家多指标评估信息进行集结。在集结过程中：充分考虑评估指标的权重以及评估等级的多样性;鉴于参与项目评估的专家具有不同的知识背景和经验,提出利用历史评估结果的准确性衡量专家提供的评价信息可靠性的方法;应用信度分布表征项目的整体评价结果,包含了更丰富的信息。国家自然科学基金项目评估的实例分析证明了该评估决策模型的有效性。     

Multi-criteria Decision-Making for Greenways: The Case of Trabzon,Turkey

Greenways are multi-objective planning tools for sustainable development that take several criteria into account. In order to resolve the conflicts among objectives, greenway planning requires advanced strategies for decision-making processes and techniques. To aid in this, multi-criteria decision-making (MCDM) systems have significant capacity to analytically prioritize and select alternatives. This research develops a strategy for defining, prioritizing and selecting greenway alternatives with the support of Analytical Hierarchy Process (AHP) based on an empirical study in the urban region of Trabzon (Turkey). The study finds that AHP has promising capacity to analytically prioritize alternatives and rationally select the best alternative.     

Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods

One of the essential problems in multi-criteria decision-making (MCDM) is ranking a set of alternatives based on a set of criteria. In this regard, there exist several MCDM methods which rank the alternatives in different ways. As such, it would be worthwhile to try and arrive at a consensus on this important subject. In this paper, a new approach is proposed based on the half-quadratic (HQ) theory. The proposed approach determines an optimal weight for each of the MCDM ranking methods, which are used to compute the aggregated final ranking. The weight of each ranking method is obtained via a minimizer function that is inspired by the HQ theory, which automatically fulfills the basic constraints of weights in MCDM. The proposed framework also provides a consensus index and a trust level for the aggregated ranking. To illustrate the proposed approach, the evaluation and comparison of ontology alignment systems are modeled as an MCDM problem and the proposed framework is applied to the ontology alignment evaluation initiative (OAEI) 2018, for which the ranking of participating systems is of the utmost importance.     

A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering

We consider multicriteria clustering problems where the groups are defined by a total order. We assume that the pairwise comparisons between the alternatives are expressed by a valued preference model built by PROMETHEE. In order to analyze the characteristics of each cluster on the different criteria, we propose three concepts based on the PROMETHEE principles: the preference profile of a cluster, the similarity profile of a cluster and the inconsistency profile of a cluster. These notions are illustrated based on a real application on the supplier segmentation.     

Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain

The food sector has a prodigious focus and is constantly gaining in importance in today’s global economic marketplace. Due to an increasing global population, society faces a greater challenge for sustainable food production, quality, distribution, and food safety in the food supply chain. Adopting green supply chain management (GSCM) elements is essential for utilizing the food supply chain in an environmentally benign way. As a solution to the above challenge, the economic and green characteristics for supplier selection in green purchasing are studied in this paper. For an organization, the evaluation and selection of the green supplier is a vital issue due to several tangible and intangible criteria involved. Accordingly, we apply multiple criteria decision aiding techniques.We propose a hybrid approach that combines the revised Simos procedure, PROMETHEE methods, algorithms for constructing a group compromise ranking, and robustness analysis. At first, the revised Simos procedure is used to derive the criteria weights. Next, the PROMETHEE method is applied to rank the suppliers according to each Decision Maker׳s (DM׳s) preferences. Then, the compromise ranking is constructed to minimize the distance of the individual׳s rankings from the solution adopted by the whole group. For this purpose, we introduce and apply some original procedures based on Binary Linear Programming. Finally, the results are validated against the outcomes of robustness analysis. The applicability and efficiency of the proposed approach is endorsed with a case study in an Indian food industry.