Selection of technology acquisition mode using the analytic network process

Selecting the appropriate acquisition mode for a required technology, is one of the critical strategic decisions in formulating a technology strategy. Although a number of factors were found to be influential in the choice of technology acquisition mode, it still remains a void in the literature how to make a strategic decision, based on a huge set of those factors with the help of a systematic approach. This study deals with the selection of technology acquisition mode as a multiple criteria decision making (MCDM) problem. The proposed solution to the problem in this study, is the analytic network process (ANP) approach. Since the ANP is a MCDM method that can accommodate interdependency among decision attributes, it is capable of providing priorities of alternatives with consideration of interrelationships among strategic factors. The 21 influential factors identified from the empirical studies are included as sub-criteria in the ANP model, and they are grouped into five criteria: capability, strategy, technology, market, and environment. The final decision can be made based on the resulting priorities of the alternative acquisition modes. The proposed approach is expected to effectively aid decision making on which mode is adopted for acquisition of required technologies. A case of a software company is presented for the illustration of the proposed approach.     

A mathematical programming model for the fuzzy analytic network process—applications of international investment

Currently, the analytic network process (ANP) method is widely employed to consider the multiple criteria analysis problems with dependence and feedback effects. However, in order to extend the ANP to resolve the problem of uncertainty or human subjective judgment, the concepts of fuzzy numbers should be incorporated into the ANP to represent the subjective uncertain pairwise judgments. In this paper, therefore, we propose a novel fuzzy analytic network process (FANP) model by solving a mathematical programming problem. Unlike other FANPs, the proposed method does not require the reciprocity assumption of the weight ratios between criteria, and it can derive local and global weights simultaneously in a single model. Two numerical examples of international investment problems are used to demonstrate the proposed method.     

An integrated approach for deriving priorities in analytic network process

A multiple objective programming approach for the analytic network process (ANP) is proposed to obtain all local priorities for crisp or interval judgments at one time, even in an inconsistent situation. The weakness of the ANP and fuzzy ANP (FANP) is that the complexity of generating priorities is equal to the number of comparison matrices. In the proposed approach, all sets of crisp priorities for each pairwise comparison matrix can be obtained directly. Moreover, from the outcomes of three examples, the power to reach a limiting supermatrix is less than or equal to the power of the FANP. Thus, the proposed approach can be regarded as an efficient alternative of the fuzzy ANP.     

From vicious to virtuous circles: problem structuring for quantified decision making in operationalization of corporate social responsibility

There is no formalised approach for problem structuring and quantitative decision support to operationalise corporate social responsibility (CSR) implementation. In this paper, techniques for considering criteria relationships are outlined and a holistic, systematic framework combining a qualitative and quantitative method for practical CSR integration is provided. Cognitive mapping (CM) is applied to structure the problem picture, and the cause–effect relationships between decision elements. Soft CM methodology is employed to assess the cross-criteria interactions, at both an individual and a collective level. The interactions of criteria can have a significant impact upon CSR implementation. Such impacts can be direct or indirect through their close linkages to other criteria. The causal strategic map serves as an input to the analytic network process (ANP) to carry out the multi-criteria decision analysis. Then, CM and ANP are applied in a comparative analysis to verify whether the measures of criteria significance do correspond. The key criteria in networks are identified using centrality in CM and single limited priorities in ANP. This study demonstrates that using criteria without considering their interactions will result in shortcomings in the evaluation and assessment of CSR programmes. The holistic framework, combining CM and ANP proposed in this work, enhances the process of problem structuring and supports preference-based evaluation of decision alternatives. The results of our study yield that the mapping procedure has an influence on the criteria significance in networks. The correspondence between CM and ANP is stronger when cause relationships are rigidly interpreted. More unambiguous interpretations of causal relations can be achieved if methods are used jointly and common peaks of importance in both CM and ANP could potentially serve as indications of key decision elements.     

An advanced quality function deployment model using fuzzy analytic network process

Quality function deployment (QFD) is a customer-oriented design tool used to ensure that the voice of customers is employed throughout the product planning and design stages. QFD uses the house of quality (HOQ), which is a matrix that provides a conceptual map for inter-functional planning and communication. In this paper, an advanced QFD model, based on fuzzy analytic network process (ANP) approach, is proposed to systematically take into account the interrelationship between and within the QFD components. The proposed method is aimed at expanding the current research scope from the product planning phase to the part deployment phase to provide product developers with more valuable information (ex. the importance and bottleneck level of part characteristics). Both customer requirements and the company’s production demands will be used as the inputs for the QFD process to enhance the completeness and accuracy of the QFD analysis results. A case study is presented to illustrate the application of the proposed method.     

A novel two-phase group decision making approach for construction project selection in a fuzzy environment

This paper considers a construction project problem under multiple criteria in a fuzzy environment and proposes a new two-phase group decision making (GDM) approach. This approach integrates a modified analytic network process (ANP) and an improved compromise ranking method, known as VIKOR. To take uncertainty and risk into account, a new decision making approach is presented with multiple fuzzy information by a group of experts, and a risk attitude for each expert is incorporated that can be expressed linguistically. First, a modified fuzzy ANP method is introduced to address the problem of dependence as well as feedback among conflicting criteria and to determine their relative importance. Then, a fuzzy VIKOR method is extended to rank potential projects on the basis of their overall performance. An illustrative example from the literature is provided for the construction project problem to demonstrate the effectiveness and feasibility of the proposed approach. The computational results show that the proposed two-phase GDM approach is suitable to cope with imprecision and subjectivity for the complicated decision making problem. Finally, the associated results of the proposed approach with risk attitudes and without risk attitudes are compared with the results reported by Cheng and Li [1], and the merits are highlighted.     

基于ANP-RBF神经网络的化工行业绿色供应商选择

基于绿色供应链理念,提出了化工行业绿色供应商选择的特色指标,构建了化工行业绿色供应商选择的ANP-RBF神经网络模型。通过ANP确定各指标权重,再结合RBF神经网络,从训练数据中提取隐含的知识和规律,能够方便地用于新供应商的选择。该模型求解算法为增量算法,具有很好的可扩展性,从而增加了评价的动态性。算例验证结果表明,将ANP-RBF神经网络模型用于化工行业绿色供应商的选择具有较强的实用性。     

Analytic network process in supplier selection: A case study in an electronic firm

Supplier selection, which is the first step of the activities in the product realization process starting from the purchasing of material till to the end of delivering the products, is evaluated as a critical factor for the companies desiring to be successful in nowadays competition conditions. With the scope of this paper, supplier selection was considered as a multi criteria decision problem. A model aiming the usage of analytic network process (ANP) in supplier selection is developed owning to the evaluation of the relations between supplier selection criterias in a feedback systematic. The proposed model is implemented in a company of electronic.     

Analysis of compatibility between interdependent matrices in ANP

Analytic network process (ANP) addresses multi-attribute decision-making where attributes exhibit dependencies. A principal characteristic of such problems is that pairwise comparisons are needed for attributes that have interdependencies. We propose that before such comparison matrices are used—in addition to a test that assesses the consistency of a pairwise comparison matrix—a test must also be conducted to assess ‘consistency’ across interdependent matrices. We call such a cross-matrix consistency test as a compatibility test. In this paper, we design a compatibility test for interdependent matrices between two clusters of attributes. We motivate our exposition by addressing compatibility in Sinarchy, a special form of ANP where interdependency exists between the last and next-to-last level. The developed compatibility test is applicable to any pair of interdependent matrices that are a part of an ANP.     

An ANP- and AHP-based approach for weighting criteria in public works bidding

When the bidding conditions for a new public works contract are officiallypublished, the contracting authority should count with objective criteria andweights to evaluate tenders. The evaluation criteria and weights are obtainedfrom official contract documents. In this paper a new approach for the selectionand measurement of the bidding criteria based on Analytic Hierarchy Processes(AHP) and Analytic Network Processes (ANP) is proposed. A hierarchy model and a21-criterion network model have been developed and applied to two real cases:the first one is devoted to study the public bidding for the construction of aneducational centre at the Polytechnic University of Valencia, whereas the secondone analyses the public bidding for the remodelling and improvement of onesection of a national road.     

Implementing the balanced scorecard using the analytic hierarchy process & the analytic network process

The balanced scorecard (BSC) is a multi-attribute evaluation concept that highlights the importance of non-financial attributes. By incorporating a wider set of non-financial attributes into the measurement system of a firm, the BSC captures not only a firm's current performance, but also the drivers of its future performance. Although there is an abundance of literature on the BSC framework, there is a scarcity of literature on how the framework should be properly implemented. In this paper, we use the analytic hierarchy process (AHP) and its variant the analytic network process (ANP) to facilitate the implementation of the BSC. We show that the AHP and the ANP can be tailor-made for specific situations and can be used to overcome some of the traditional problems of BSC implementation, such as the dependency relationship between measures and the use of subjective versus objective measures. Numerical examples are included throughout.     

An intelligent decomposition of pairwise comparison matrices for large-scale decisions

A Pairwise Comparison Matrix (PCM) has been used to compute for relative priorities of elements and are integral components in widely applied decision making tools: the Analytic Hierarchy Process (AHP) and its generalized form, the Analytic Network Process (ANP). However, PCMs suffer from several issues limiting their applications to large-scale decision problems. These limitations can be attributed to the curse of dimensionality, that is, a large number of pairwise comparisons need to be elicited from a decision maker. This issue results to inconsistent preferences due to the limited cognitive powers of decision makers. To address these limitations, this research proposes a PCM decomposition methodology that reduces the elicited pairwise comparisons. A binary integer program is proposed to intelligently decompose a PCM into several smaller subsets using interdependence scores among elements. Since the subsets are disjoint, the most independent pivot element is identified to connect all subsets to derive the global weights of the elements from the original PCM. As a result, the number of pairwise comparison is reduced and consistency is of the comparisons is improved. The proposed decomposition methodology is applied to both AHP and ANP to demonstrate its advantages.     

Quality function deployment for the good of soccer

This article presents a model for prioritizing and designing rule changes for the game of soccer in order to make it more attractive to soccer enthusiasts. The model, which is loosely based on quality function deployment (QFD), incorporates market segments, sports enthusiast interests, soccer activities, and rules of the game as rows and columns of interconnected QFD matrices. In addition, analytic hierarchy process (AHP) is used to determine the intensity of the relationship between the row and column variables of each matrix, while analytic network process (ANP) is used to determine the intensity of synergy effects among column variables. Finally, a trend extrapolation forecasting technique is used to suggest rule change specifications. The model fine-tunes and adds precision to the otherwise ad hoc process concerning the improvement of the game of soccer, and can provide insight about improvements in other professional sports.     

A hybrid multiple-criteria decision analysis framework for corporate social responsibility implementation applied to an extractive industry case study

Integration of corporate social responsibility (CSR) into a company’s mainstream strategy is a complex task. Practical implementation of CSR requires analysis of both the external and internal environments to determine the prospects and challenges significantly influencing integration of sustainability into business strategy. In order to overcome limitations of single multiple-criteria decision analysis models, this paper proposes a hybrid integrated framework combining cognitive mapping and analytic networks process (ANP) to determine, prioritise and select CSR programmes for implementation. The strategic cognitive map serves as a foundation to build the ANP network and identify the importance of CSR programmes. A knapsack optimisation method is then used to optimally assign resources to CSR alternatives. We demonstrate the usefulness of the framework through a case study in the extractive sector. The framework was empirically tested with 61 respondents using postal and online surveys, MBA workshops, and conference networking.     

An integrated FCM and fuzzy soft set for supplier selection problem based on risk evaluation

Supplier selection problem, considered as a multi-criteria decision-making (MCDM) problem, is one of the most important issues for firms. Lots of literatures about it have been emitted since 1960s. However, research on supplier selection under operational risks is limited. What’s more, the criteria used by most of them are independent, which usually does not correspond with the real world. Although the analytic network process (ANP) has been proposed to deal with the problems above, several problems make the method impractical. This study first integrates the fuzzy cognitive map (FCM) and fuzzy soft set model for solving the supplier selection problem. This method not only considers the dependent and feedback effect among criteria, but also considers the uncertainties on decision making process. Finally, a case study of supplier selection considering risk factors is given to demonstrate the proposed method’s effectiveness.     

基于ANP与模糊TOPSIS-CRITIC方法的不确定多属性决策模型

随着多属性决策问题的日益流行,处理决策问题的复杂程度也逐渐增加,针对其中权重不确定,难以量化各影响因素主观权重与客观权重以及指标排序不精确的问题,提出了一种将网络层次分析法(ANP)与模糊指标相关性的指标权重确定法(CRITIC)、逼近理想解排序法(TOPSIS)相结合的不确定多属性决策模型首先,在分别使用ANP方法和CRITIC法确定各影响因素的主观、客观权重的基础上计算指标的综合权重,然后应用模糊TOPSIS方法对备选方案进行贴进度排序最后,通过一个实例将计算结果同其他三种决策方法进行对比分析,验证了所提出的ANP与模糊TOPSIS-CRITIC方法的可靠性和有效性.     

基于ANP权重确定的AHP-DEA模型及其在逆向物流服务供应商选择中的应用

实际系统评价问题中,输入输出指标之间往往并非独立,而是存在着复杂的影响与反馈关系.传统AHP-DEA模型直接采用层次分析法(AHP)构造约束锥,容易使指标权重产生较大偏差甚至出现错误.通过网络分析法(ANP)确定了权重,反向构造了AHP约束锥,并与DEA模型结合,建立了AHP-DEA模型.同时,开展了逆向物流服务供应商选择应用研究,应用结果表明,基于ANP权重确定的AHP-DEA模型能够有效解决多个决策单元均有效而无法区分优劣的问题.     

A hybrid model for selection of an outsourcing provider

Outsourcing is a good strategy for firms that need to reduce operating costs and improve competitiveness and it is important that firms scientifically select appropriate outsourcing providers. Some efforts have been made to find systematic ways to deal with outsourcing problems, but these efforts incorrectly assumed that the criteria used in the decision process are independent, which is not true in the real world. In this study, we propose a new hybrid multiple criteria decision-making (MCDM) model, which addresses the dependent relationships between the various criteria. The relations-structure among the criteria is built with the aid of the decision-making trial and evaluation laboratory (DEMATEL) method. Decision-makers tend to hold diverse opinions about their preferences due to incomplete information and knowledge, or inherent conflict between various departments. We further used the fuzzy preference programming and the analytic network process (ANP) to form a model for the selection of partners for outsourcing providers. The proposed model can help practitioners improve their decision making process, especially when criteria are numerous and inter-related. The method is demonstrated using data from a Taiwanese airline.     

Bi-criteria evolution strategy in estimating weights from the AHP ratio-scale matrices

The problem of deriving weights from ratio-scale matrices in an analytic hierarchy process (AHP) is addressed by researchers worldwide. There are various ways to solve the problem that are generally grouped into simple matrix and optimization methods. All methods have received criticism regarding the accuracy of derived weights, and different criteria are in use to compare the weights obtained from different methods. Because the set of Pareto non-dominated solutions (weights) is unknown and for inconsistent matrices is indefinite, a bi-criterion optimization approach is proposed for manipulating such matrices. The problem-specific evolution strategy algorithm (ESA) is implemented for a robust stochastic search over a feasible indefinite solution space. The fitness function is defined as a scalar vector function composed of the common error measure, i.e. the Euclidean distance and a minimum violation error that accounts for no violation of the rank ordering. The encoding scheme and other components of the search engine are adjusted to preserve the imposed constraints related to the required normalized values of the weights. The solutions generated by the proposed approach are compared with solutions obtained by five well-known prioritization techniques for three judgment matrices taken from the literature. In these and other test applications, the prioritization method that uses the entitled weights estimation by evolution strategy algorithm (WEESA) appears to be superior to other methods if only two, the most commonly used methods, are applied: the Euclidean distance and minimum violation exclusion criteria.