A fuzzy analytic hierarchy process approach in modular product design

Product development stages. The analytic hierarchy process (AHP), which breaks down a complex problem into simple hierarchical decision-making processes, can be incorporated with fuzzy logic to suggest the relative strength of the factors in the corresponding criteria, thereby enabling the construction of a fuzzy judgement matrix to facilitate decision-making. This paper proposes a fuzzy AHP approach in modular product design complemented with a case example to validate its feasibility in a real company. Test findings indicate that the approach is helpful for providing key decision support information in terms of product module selection during product development stages. The significance of the contribution of this paper is the suggestion of a novel approach in modular product design, embracing a combination of computational intelligence and traditional techniques, thereby providing more alternatives and ideas for those researchers who are interested in this field of study.     

Group multi-criteria design concept evaluation using combined rough set theory and fuzzy set theory

Design concept evaluation is a critical stage in the product development which has significant impact on the downstream process in product development thus on success of new product. Design concept evaluation is widely recognized as a complex multi-criteria decision-making (MCDM) problem involving various decision criteria and large amount of data which are usually imprecise and subjective. This paper proposes a new decision-making method to evaluate product design concepts based on the distance between interval vectors each alternative and positive and negative ideal reference vectors. Rank of design concepts is obtained by calculating interval-based relative closeness index for each alternative. In this method, to deal with uncertainty and vagueness of data in the primary phases of product design, performance of design concepts with respect to quantitative and qualitative criteria are concurrently evaluated using rough set and fuzzy set. The weights of criteria used in the evaluation are obtained using the extent analysis method on fuzzy AHP. The efficacy of the method is demonstrated with a numerical example and the results are compared to TOPSIS method. In final, the conclusions of our method are represented and some future directions are proposed to improve the model.     

Evaluating new product development performance by fuzzy linguistic computing

New product development (NPD) is indeed the cornerstone for companies to maintain and enhance the competitive edge. However, developing new products is a complex and risky decision-making process. It involves a search of the environment for opportunities, the generation of project options, and the evaluation by different experts of multiple attributes, both qualitative and quantitative. To perceive and to measure effectively the capability of NPD are real challenging tasks for business managers. This paper presents a 2-tuple fuzzy linguistic computing approach to deal with heterogeneous information and information loss problems during the processes of subjective evaluation integration. The proposed method which is based on the group decision-making scenario to assist business managers to measure the performance of NPD manipulates the heterogeneous integration processes and avoids the information loss effectively. Finally, its feasibility is demonstrated by the result of NPD performance evaluation for a high-technology company in Taiwan.     

A decision support system for product design in concurrent engineering

Compared with the traditional sequential design method, concurrent engineering is a systematic approach to integrate concurrent design of products and their related processes. One of the key factors to successfully implement concurrent engineering is information technology. In order to design a product and its manufacturing process simultaneously, information on product features, manufacturing requirements, and customer demands must be processed while the design is concurrently going on. There is an increased understanding of the importance of the correct decisions being made at the conceptual design and development stages that involve many complex evaluation and decision-making tasks. In order to promote the efficiency in concurrent product development, appropriate evaluation and decision tools need to be provided. In this paper, the characteristics of fuzzy, multi-stage evaluation and decision making in concurrent product development process are analyzed and a decision support system for product design in concurrent engineering is presented. An example is given to illustrate the application of the system.     

Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets

In this paper, we present a new method to deal with fuzzy multiple attributes group decision-making problems based on ranking interval type-2 fuzzy sets. First, we propose a new method for ranking interval type-2 fuzzy sets. Then, we propose a new method for fuzzy multiple attributes group decision-making based on the proposed ranking method of interval type-2 fuzzy sets. We also use some examples to illustrate the fuzzy multiple attributes group decision-making process of the proposed method. The proposed method is simpler than the methods presented in [Chen and Lee, 2010a] and [Lee and Chen, 2010b] for fuzzy multiple attributes group decision-making based on interval type-2 fuzzy sets. It provides us with a useful way for dealing with fuzzy multiple attributes group decision-making problems based on interval type-2 fuzzy sets.     

考虑风险偏好的区间直觉模糊软集型多属性决策方法

文章针对多方参与、评价指标有差异且存在模糊性的多属性方案优选问题,提出了一种考虑风险偏好的区间直觉模糊软集决策方法.对这类区间直觉模糊软集的多属性决策问题,提出了一个三阶段的决策方法,且按照决策者三种不同风险偏好,给出了相应的决策原则.在此基础上,文章选择了其中一种情形提出了具体的决策算法.最后,文章通过数值算例验证了该方法的可行性和有效性,同时采用综合得分值对该方法的结果进行了一致性的验证和讨论.该方法不仅可以很好地解决这类多属性方案优选问题,也能进一步推广到多属性方案的排序问题中.     

A fuzzy-logic-based approach for new product Go/NoGo decision at the front end

New product development (NPD) is both a complex process and a substantial business risk. It still requires 6.6 ideas to generate a successful product. Thus, researchers claim that inferior new products should be eliminated at the front end. Limited by both the nature and the timing of NPD, managers often perform screening in uncertain environments and based on incomplete information. Furthermore, the conventional evaluation approaches, which encapsulate or merely discard the ambiguity and multiplicity of possible concerns, make a screening economically sound but dysfunctional as well. Since most assessments are described subjectively by linguistic terms, a comprehensive method for new product screening using fuzzy logic is proposed, in which the criteria ratings and their corresponding importance are assessed in linguistic terms described by fuzzy numbers, and fuzzy weighted average is employed to aggregate these fuzzy numbers into a fuzzy-possible-success rating (FPSR) of the product. Finally, the FPSR is translated back into linguistic terms to derive at a new product screening decision. Furthermore, a case study is cited to illustrate the performance within an actual decision process. The result shows that this approach can efficiently aid managers dealing with ambiguity and complex environments in achieving relatively realistic and informative results, as well as give managers a high degree of flexibility in decision-making. In addition, the variations in linguistic values and levels of linguistic variables have an effect on the ranges of the FPSR which ultimately, affect the selection of the NPD project.     

Comparison of fuzzy numbers based on preference

Ranking fuzzy numbers plays an important role in a fuzzy decision-making process. However, fuzzy numbers may not be easily ordered into one sequence due to the overlap between them. A new approach is introduced to detect the overlapped fuzzy numbers based on the concept of similarity measure, incorporating the preference of the decision-maker into the fuzzy ranking process. Numerical examples and comparisons with other methods are presented to evaluate the new method. The computational process of the proposed method is straightforward and is practically capable of comparing similar fuzzy numbers. The proposed method is an absolute ranking and no pairwise comparison of fuzzy numbers is necessary. Furthermore, through some examples discussed in this work, it is proved that the proposed method possesses several good characteristics compared to other methods examined in this work.     

An interval-valued intuitionistic fuzzy permutation method with likelihood-based preference functions and its application to multiple criteria decision analysis

This paper presents an interval-valued intuitionistic fuzzy permutation method with likelihood-based preference functions for managing multiple criteria decision analysis based on interval-valued intuitionistic fuzzy sets. First, certain likelihood-based preference functions are proposed using the likelihoods of interval-valued intuitionistic fuzzy preference relationships. Next, selected practical indices of concordance/discordance are established to evaluate all possible permutations of the alternatives. The optimal priority order of the alternatives is determined by comparing all comprehensive concordance/discordance values based on score functions. Furthermore, this paper considers various preference types and develops another interval-valued intuitionistic fuzzy permutation method using programming models to address multiple criteria decision-making problems with incomplete preference information. The feasibility and applicability of the proposed methods are illustrated in the problem of selecting a suitable bridge construction method. Moreover, certain comparative analyses are conducted to verify the advantages of the proposed methods compared with those of other decision-making methods. Finally, the practical effectiveness of the proposed methods is validated with a risk assessment problem in new product development.     

An optimum design selection approach for product customization development

Consumer preferences and information on product choice behavior can be of significant value in the development processes of innovative products. In this paper, product customization evaluation and selection model is introduced to support imprecision inherent of qualitative inputs from customers and designers in the decision making process. Focusing on customer utility generation, an optimum design selection approach based on fuzzy set decision-making is proposed, where design attributes priority is identified from customer preferences using an analytical hierarchy process. A multi-attribute analysis diagram is developed to visualize the preference of each attribute from the expert’s group decision. Conjoint analysis is used in the product customization to focus on customer utility generation in terms of multiple criteria. The use of the decision-making method is illustrated with a case example that highlights the utility of the proposed method.     

Nonlinear fusion method for multistage product design decision-making using plant growth simulation algorithm

Design decision-making is a vital activity for selecting an optimal scheme for product development. Owing to the uncertainty and ambiguity of design requirements and constraints, several product design phases are often deployed for concept refinement, which makes multistage product design decision-making (MPDDM) and the effective fusion of MPDDM data indispensable. However, few existing methods have considered the nonlinear relationships among the MPDDM information. Therefore, a nonlinear fusion method for MPDDM was proposed in this study. This method applies a three-parameter interval grey number to depict decision-makers’ judgement about product design schemes. Based on converting linguistic judgements into interval scales, an interval analytic hierarchy process (AHP) method was employed to calculate the weights of the design criteria, decision-makers, and decision-making stages. Considering the advantage of integrating multiple matrices without requiring external control parameters, a multistage decision-making fusion process using a plant growth simulation algorithm (PGSA) was proposed to aggregate multistage decision-making data for product design. A case study was conducted to collect multistage decision-making data, and the PGSA was developed. Through comparison with the extant method, the effectiveness and feasibility of the fusion of MPDDM was verified. The results indicate that (1) uncertainty perceived by decision-makers at three stages accounted for 96.7%, 95%, and 97.2%, respectively. The “center of gravity” of a three-parameter interval grey number, which reflects the largest possibility of decision-makers’ preferences, is not always equally distant from its maximum or minimum value (73.9%). (2) The optimization model using interval AHP to calculate the weights of decision-making indicators and stages is conducive to reducing the decision-maker’s uncertainty. (3) The global search mechanism of the PGSA can effectively realize the nonlinear fusion of MPDDM.     

Extension of the VIKOR method for group decision making with extended hesitant fuzzy linguistic information

Group decision-making approaches are very important due to the complexity and uncertainty of many real-world decision-making problems. Some of the decision-making problems are defined in qualitative frameworks. Extended hesitant fuzzy linguistic term set (EHFLTS) is proposed as a new and powerful tool for elicitation of hesitant qualitative information in group decision-making process. In this paper, we first introduced the comparison laws and a family of distance and similarity measures for extended hesitant fuzzy linguistic terms (EHFLTs) and EHFLTSs, respectively. Next, we developed the extended hesitant fuzzy linguistic (EHFL)-VIKOR method as a qualitative multi-attributes group decision-making approach based on the EHFLTS distance measures to deal with the qualitative hesitancy in group decision making. Finally, we presented an application example about selection of suitable telecommunications service provider of small- and medium-sized enterprises to verify applicability and validation of proposed method in the process of qualitative group decision making.

     

考虑外部环境变化的分阶段动态群决策方法

研究决策者权重部分未知的概率犹豫模糊分阶段动态群决策问题.针对外部环境的变化,结合符号距离测度,提出考虑外部环境变化的分阶段群决策方法.首先,基于元素的方差及个数差异定义概率犹豫模糊元的犹豫度公式,并在此基础上定义概率犹豫模糊元的符号距离公式.然后,根据外部环境的变化会导致每个时序阶段获得的信息存在差异的特点,构建动态决策模型以确定各个时序阶段的决策者权重,进而分阶段集结信息,形成决策过程方案链.最后,通过算例分析验证了所提出方法的有效性与合理性.     

Group fuzzy AHP approach to embed relevant data on “communicating material”

The amount of data output into our environment is increasing each day, and the development of new technologies constantly redefines how we interact with information. In the context of product life cycle management, it is not uncommon to use intelligent products to ensure an information continuum throughout the product life cycle (e.g., for traceability purposes). Integrating intelligence and information into products themselves is now possible through numerous technologies (RFID, communicating materials). However, these technologies currently have low memory capacities (several kilobytes or megabytes), whereas to product databases are becoming larger and larger (several gigabytes or terabytes). As a result, a data dissemination process is required to determine the relevant information that should be stored on the product. This paper proposes a multiple-criteria decision-making (MCDM) method based on a fuzzy Analytical Hierarchy Process (fuzzy AHP). This method is context-aware and supports the aggregation of opinions from a group of experts. An application is proposed to embed context-sensitive information in a “communicating textile”.     

A new type-2 fuzzy set of linguistic variables for the fuzzy analytic hierarchy process

The fuzzy analytic hierarchy process (FAHP) has been used to solve various multi-criteria decision-making problems where trapezoidal type-1 fuzzy sets are utilized in defining decision-makers’ linguistic judgment. Previous theories have suggested that interval type-2 fuzzy sets (IT2 FS) can offer an alternative that can handle vagueness and uncertainty. This paper proposes a new FAHP characterized by IT2 FS for linguistic variables. Differently from the typical FAHP, which directly utilizes trapezoidal type-1 fuzzy numbers, this method introduces IT2 FS to enhance judgment in the fuzzy decision-making environment. This new model includes linguistic variables in IT2 FS and a rank value method for normalizing upper and lower memberships of IT2 FS. The proposed model is illustrated by a numerical example of work safety evaluation. Comparable results are also presented to check the feasibility of the proposed method. It is shown that the ranking order of the proposed method is consistent with the other two methods despite difference in weight priorities.     

考虑时间序列的动态大群体应急决策方法

针对专家权重和属性权重未知、阶段权重未知且与时间序列有关的动态大群体应急决策问题,提出一种考虑时间序列的动态大群体应急决策方法.首先,提出一个考虑区间直觉模糊数犹豫度的距离公式,定义区间直觉模糊数贴近度,综合考虑贴近度和相似度,用模糊聚类法对大群体专家偏好信息进行聚类;其次,基于现有区间直觉模糊熵公式的不足,提出一个新的区间直觉模糊熵公式,基于此公式考虑专家之间知识水平的差异和各个阶段偏好信息不具遗传性等特点,计算得出专家在不同属性下的权重和属性在各阶段下的权重;再次,考虑时间序列对各阶段权重的影响,构建相对熵模型,对阶段权重进行合理确定,进而利用加权平均算子得到整个决策过程中各方案的综合决策偏好;然后,利用区间直觉模糊数的得分函数和精确函数对方案进行排序,选出最优方案;最后,通过与以往文献的方法对比分析验证所提出方法的有效性和优越性.     

Applying fuzzy hierarchy multiple attributes to construct an expert decision making process

The main objective of this paper is to propose an approach within the AHP framework for tackling the uncertainty and imprecision of service evaluations during pre-negotiation stages, where the expert’s comparison judgments are represented as fuzzy triangular numbers. A fuzzy prioritization method, which derives crisp priorities from consistent and inconsistent fuzzy comparison matrices, is described. The fuzzy analytic hierarchy process (FAHP)-based decision-making method can provide decision makers or buyer a valuable reference for evaluating software quality. A case study demonstrates the effectiveness of the proposed scheme. Importantly, the proposed scheme can assist decision makers in assessing the feasibility of digital video recorder system to management public space, making it highly applicable for academic and commercial purposes.     

Fuzzy analytical hierarchy process and multi-segment goal programming applied to new product segmented under price strategy

New product development (NPD) is becoming an important competitive advantage in the marketing strategies of current businesses. Developing a new product will incur fixed and variable costs, which then determine the product prices. Although this is a fundamental issue of marketing theory and practice, only a few papers on marketing models deal with price levels. The objective of this paper is proposing a model based on fuzzy analytical hierarchy process (AHP) and multi-segment goal programming (MSGP) to help decision makers to select the best pricing strategy for NPD. A case study of NPD under market selection strategy in multiple segment pricing levels for a Taiwan-based Watch Company is presented to illustrate the proposed methodology. The proposed method will guide the product development team to select the best market strategy by taking into account the price level and product/market segmentation.     

Intelligent multi-criteria fuzzy group decision-making for situation assessments

Organizational decisions and situation assessment are often made in groups, and decision and assessment processes involve various uncertain factors. To increase efficiently group decision-making, this study presents a new rational–political model as a systematic means of supporting group decision-making in an uncertain environment. The model takes advantage of both rational and political models and can handle inconsistent assessment, incomplete information and inaccurate opinions in deriving the best solution for the group decision under a sequential framework. The model particularly identifies three uncertain factors involved in a group decision-making process: decision makers’ roles, preferences for alternatives, and judgments for assessment-criteria. Based on this model, an intelligent multi-criteria fuzzy group decision-making method is proposed to deal with the three uncertain factors described by linguistic terms. The proposed method uses general fuzzy numbers and aggregates these factors into a group satisfactory decision that is in a most acceptable degree of the group. Inference rules are particularly introduced into the method for checking the consistence of individual preferences. Finally, a real case-study on a business situation assessment is illustrated by the proposed method.     

Using a hybrid MCDM methodology to identify critical factors in new product development

With increased global competition, businesses now face a more complex and dynamic competitive environment and need to develop more innovative products with higher quality and shorter product life cycles. New product development (NPD) is the keys to the success of a business, where both critical success factors (CSF) and key performance indicators (KPI) will affect the outcome of a NPD project. This study utilizes both CSF and KPI to examine the critical factors of NPD with the methodology of multiple criteria decision making and investigates the correlations of critical factors with fuzzy decision-making trial and evaluation laboratory and establishes the weights affecting NPD in the criteria with the fuzzy analytic hierarchy process (FAHP) approach. The results assist managers in selecting suitable competitive strategies, making the optimal allocation of limited resources, achieving the greatest increase in benefits, and promoting the overall success of NPD. The findings show that there are various correlations between the critical factors, where ‘products and customers’ have the most influence on other dimensions. Moreover, the top five critical factor criteria weights, constructed by FAHP, are ‘quality standard of new products’, ‘complete quality management system’, ‘consumer satisfaction’, ‘excellent planning and control’, and ‘support of top managers’.