A hybrid approach to concept selection through fuzzy analytic network process

Evaluating conceptual design alternatives in a new product development (NPD) environment has been one of the most critical issues for many companies which try to survive in the fast-growing world markets. Therefore, most companies have used various methods to successfully carry out this difficult and time-consuming evaluation process. Of these methods, analytic hierarchy process (AHP) has been widely used in multiple-criteria decision-making (MCDM) problems. But, in this study, we used analytical network process (ANP), a more general form of AHP, instead of AHP due to the fact that AHP cannot accommodate the variety of interactions, dependencies and feedback between higher and lower level elements. Furthermore, in some cases, due to the vagueness and uncertainty on the judgments of a decision-maker, the crisp pairwise comparison in the conventional ANP is insufficient and imprecise to capture the right judgments of the decision-maker. Therefore, a fuzzy logic is introduced in the pairwise comparison of ANP to make up for this deficiency in the conventional ANP, and is called as fuzzy ANP. In short, in this paper, a fuzzy ANP-based approach is proposed to evaluate a set of conceptual design alternatives developed in a NPD environment in order to reach to the best one satisfying both the needs and expectations of customers, and the engineering specifications of company. In addition, a numerical example is presented to illustrate the proposed approach.     

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.     

A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment

Quality function deployment (QFD) is an important tool in product planning that could contribute to increase in customer satisfaction and shorten product design and development time. During the QFD process, determination of the importance weights of customer requirements is a crucial and essential step. The analytic hierarchy process (AHP) has been used in weighting the importance. However, due to the vagueness and uncertainty existing in the importance attributed to judgement of customer requirements, the crisp pairwise comparison in the conventional AHP seems to be insufficient and imprecise to capture the degree of importance of customer requirements. In this paper, fuzzy number is introduced in the pairwise comparison of AHP. An AHP based on fuzzy scales is proposed to determine the importance weights of customer requirements. The new approach can improve the imprecise ranking of customer requirements which is based on the conventional AHP. Finally, an example of bicycle splashguard design is used to illustrate the proposed approach.     

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.     

Make-to-order or make-to-stock decision by a novel hybrid approach

This paper presents a strategic decision-making structure to determine whether a particular product should be produced under make-to-order (MTO) or make-to-stock (MTS) strategy. A novel hybrid methodology consisting of strengths, weaknesses, opportunities and threats (SWOT) analysis and analytic hierarchy process (AHP) is proposed. Utilizing AHP in SWOT analysis diminishes one of the main deficiencies of this strategic approach that is the restriction of employing quantitative calculation to evaluate the factors that affect the decision. AHP method improves the quantitative information basis of strategic decision-making processes. The traditional AHP requires exact judgments, but due to the complexity and uncertainty involved in real world decision problems, it is sometimes unrealistic or even impossible to require exact judgments. Consequently, a commonly used decision-making method, fuzzy analytic hierarchy process (FAHP) is suggested. FAHP’s connection to SWOT yields a novel hybrid method for partitioning of MTO/MTS products. At the end, the proposed fuzzy AHP-SWOT methodology is validated through a real case study.     

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.     

Combining fuzzy AHP and fuzzy Kano to optimize product varieties for smart cameras: A zero-one integer programming perspective

In an era of global customization, dominating the majority market with a single product has become increasingly difficult and almost impossible for most companies. In contrast, they must provide various product varieties that attract diverse customers, particularly when acquiring distinct market segments. In practice, however, most companies cannot effectively reduce the gap between customer requirements and design characteristics, although this impacts the profitability and future growth of companies. Meanwhile, companies often get stuck in the trade-offs between enhancing product varieties and controlling manufacturing costs. Accordingly, this paper proposes a hybrid framework that combines fuzzy analytical hierarchy process (AHP), fuzzy Kano model with zero-one integer programming (ZOIP) to incorporate customer preferences and customer perceptions into the decision-making process of product configuration. Specifically, fuzzy AHP is used to extract customer preferences for core attributes while fuzzy Kano model is utilized to elicit customer perceptions of optional attributes. Finally, by virtue of ZOIP, the optimal product varieties (smart cameras) for distinct segments are determined by maximizing overall customer utility (OCU) and taking a firm's pricing policy into account.     

A combined fuzzy AHP-simulation approach to CAD software selection

In this paper, a combined approach, where the fuzzy analytic hierarchy process (AHP) and simulation come together, is presented to select the best computer-aided design (CAD) software out of the available options in the market. The fuzzy AHP is used due to the vagueness and uncertainty of the judgements of a decision maker(s), because the crisp pair-wise comparison in the conventional AHP seems to be insufficient and imprecise to capture the right judgements of the decision maker(s). In this study, first the fuzzy AHP is used to reduce a possible number of alternatives for the CAD system to an acceptable level for further study, simulation analysis. Secondly, a simulation generator as an integrated part of the fuzzy AHP is used to try the remaining alternatives, on the generated model of a real-life product organisation in which the final alternative will be used. The results of simulation experiments are obtained, and then evaluated to reach to the ultimate CAD alternative.     

A two phase multi-attribute decision-making approach for new product introduction

This study aims at improving the quality and effectiveness of decision-making in new product introduction. New product development has long been recognized as one of the corporate core functions to be competitive on an increasingly competitive global market. However, developing new products is a process involving risk and uncertainty. In order to solve this stochastic problem, companies need to evaluate their new product initiatives carefully and make accurate decisions. For this reason, a systematic decision process for selecting more rational new product ideas is proposed. Basically, two stages of decision-making are described: the identification of nondominated new product candidates and the selection of the best new product idea. These stages are composed of an integrated approach based on a fuzzy heuristic multi-attribute utility method and a hierarchical fuzzy TOPSIS method. Finally, an application is given to demonstrate the potential of the methodology.     

An integrated fuzzy multi criteria group decision making approach for ERP system selection

This paper aims to ease group decision-making by using an integration of fuzzy AHP (analytic hierarchy process) and fuzzy TOPSIS (technique for order preference by similarity to ideal solution) and its application to software selection of an electronic firm. Firstly, priority values of criteria in software selection problem have been determined by using fuzzy extension of AHP method. Fuzzy extension of AHP is suggested in this paper because of little computation time and much simpler than other fuzzy AHP procedures. Then, the result of the fuzzy TOPSIS model can be employed to define the most appropriate alternative with regard to this firm's goals in uncertain environment. Fuzzy numbers are presented in all phases in order to overcome any vagueness in decision making process. The final decision depends on the degree of importance of each decision maker so that wrong degree of importance causes the mistaken result. The researchers generally determine the degrees of importance of each decision maker according to special characteristics of each decision maker as subjectivity. In order to overcome this subjectivity in this paper, the judgments of decision makers are degraded to unique decision by using an attribute based aggregation technique. There is no study about software selection using integrated fuzzy AHP-fuzzy TOPSIS approach with group decision-making based on an attribute based aggregation technique. The results of the proposed approach and the other approaches are compared. Results indicate that our methodology allows decreasing the uncertainty and the information loss in group decision making and thus, ensures a robust solution to the firm.     

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.     

面向企业集成的群体决策支持系统设计与实现

针对企业集成方案决策的需求，基于AHP和模糊决策方法，设计开发了一个基于Web的群体决策支持系统(EI—GDSS)，该系统的主要特点是：决策模型的构建可通过调用参考评价指标体系库中已定义的建造块进行定制，设计了一种模糊评价算法来计算权值向量，将决策过程分为中层评价和高层决策两个阶段，综合考虑了主、客观指标的不同处理方法和个体／群体一致性判别。     

A design model of FBS based on interval-valued Pythagorean fuzzy sets

The FBS (Function-Behaviour-Structure) model is a research model that stimulates creative thinking of designers in the design process. In order to reduce the influence of user requirement ambiguity on design results in the product design process and improve the accuracy of user requirements in the function-behavior-structure (FBS) design model, this paper proposes an interval-valued Pythagorean fuzzy set-based FBS model integrating AHP and HOQ methods. Firstly, the design model will use IVPF-AHP method to study user requirements and use interval-valued Pythagorean linguistic terms to replace the traditional scoring method of AHP to get the weight of each user requirement. Secondly, the conversion between user requirements and functions will be realized by IVPF-HOQ method, which converts customer requirements into functional characteristics and calculates the weights of each functional characteristic. Finally, the design focus will be filtered according to the order of importance of the functional characteristics, which will be used as functions to guide the development of the FBS model. In this paper, the feasibility and effectiveness of the proposed method will be verified by an application example of a hand-held fluorescence spectrometer. The results show that the proposed FBS model can effectively reduce the subjectivity and ambiguity in the decision-making process, improve the accuracy and information richness of user requirements, and effectively highlight the focus of the design study. The innovation of the proposed method is to provide a more objective and accurate innovative design method for user requirements through the integration of AHP, HOQ and FBS to effectively explore and analyze user requirements. The use of IVPFS to deal with fuzzy information in the design process in a more flexible manner can reduce the ambiguity of requirements when user data is small, and effectively improve the limitations of the FBS design model which is more subjective.     

聚类分析法在产品族设计中的应用研究

研究应用聚类分析的手段解决模块化产品族设计中模块和核心平台（Product Platform)稳定的问题。通过分析产品开发过程以及部件关联矩阵，以部件之间关联的紧密程度作为聚类指标。应用聚类分析法，求得部件对不同类别的相对隶属度，从而为产品模块化和核心平台的划分提供科学分类依据。聚类分析法在产品族设计中的应用是模糊数学应用的新尝试，通过这种方法的运用，为产品族设计的数学分析和描述提价节定量的科学支持以及规范化的产品族设计过程。     

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.     

Design concept evaluation considering information reliability,uncertainty, and subjectivity: An integrated rough-Z-number-enhanced MCGDM methodology

Design concept evaluation plays a vital role in new product development due to its critical impact on successive design activities as well as the novelty and competence of the final product. Lots of decision-making techniques have been developed for the design concept evaluation under various contexts. However, existing methods mainly focus on the evaluation under precise environments or fuzzy set scenarios. Whereas in reality, most risk assessments are derived from experts’ subjective judgments. The reliability of the evaluation is rarely taken into consideration. To bridge this gap, this study proposes a rough-Z-number-enhanced MCGDM (multi-criteria group decision-making) to resolve the design concept evaluation considering information reliability, uncertainty, and subjectivity. A rough-Z-number is introduced to incorporate into the MCGDM framework to represent the individual evaluation and aggregate group risk assessments. A rough-Z-number-based AHP (analytic hierarchy process) is presented to determine the criteria weights. A rough-Z-number-based MABAC (multi-attributive border approximation area comparison) is proposed to rank the design concepts. Experiments and comparative analyses demonstrate the advantage of the rough-Z-number extended MCGDM in complex and uncertain decision-making environments. With the rough-Z-number-based enhancement, the information reliability, uncertainty, and subjectivity in design concept evaluation are well characterized.     

Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process

The basic concept in axiomatic design (AD) is the existence of design axioms. First of these axioms is the independence axiom and the second one is the information axiom. Information axiom proposes the selection of the best alternative that has minimum information. Analytic hierarchy process (AHP) is another multi-attribute method which is a decision-making method for selecting the best among a set of alternatives, given some criteria. The method has been extensively applied, especially in large-scale problems where many criteria must be considered and where the evaluation of alternatives is mostly subjective. Multi-attribute transportation company selection is a very important activity for effective supply chain. Selection of the best company under determined criteria (such as cost, time, damage/loss, flexibility and documentation ability) using both multi-attribute AD and AHP will be realized in this study. The fuzzy multi-attribute AD approach is also developed and it is compared by one of fuzzy AHP methods in the literature. The selection process has been accomplished by aiding a software that includes crisp AD and fuzzy AD.     

Customer-driven product design and evaluation method for collaborative design environments

Product concept generation and evaluation in a product development environment has been identified as the two major activities needed for obtaining an optimal design scheme. Product conceptual design is of critical importance in design through customer involvement for the systematic and simultaneous consideration on the impact of design decisions on manufacturing and assembly leads to repeated and excessive changes in design and processes. This paper introduces a novel knowledge support approach for the organization and ranking of design feature knowledge towards an integrated product model that incorporates a feature-based representation scheme targeted to evaluate the impact of design on subsequent activities in the conceptual design phase, taking into account the presence of design information and user preferences. An uncertain linguistic multi-attribute decision-making evaluation model is proposed and discussed for obtaining an optimal design scheme during the evaluation and selection of product design alternatives in conceptual design. The focus of this paper is on the development of a knowledge-intensive support design scheme and a comprehensive systematic fuzzy evaluation methodology for product conceptual design generation, evaluation, and selection. A case study and the corresponding scenario of knowledge support for design alternatives generation, evaluation and selection are provided for illustration.     

Function-oriented design of a friction stir welding robot

The paper starts with the analysis on the need for intelligent manufacturing systems, and proposes our view of the solution to the problem from a mechatronic point of view. It discusses issues related to the flexible modular systems that produce modular flexible products. The paper takes a novel welding technology called friction stir welding as a case study, and investigates issues related to the design of a machine that could automate the process. It presents a function-oriented approach that could provide the guidelines in development of such a system. Along with the various alternatives of the friction stir welding machines concepts, various types of welding configurations are discussed in detail thereby providing necessary details for function-oriented conceptual design of a general-purpose robotic system. The paper, then, presents the system model for an articulated robot manipulator, as suggested by functional design, followed by simulations to investigate its suitability for such an application. The proposed solution not only ensures the achievement of requirements for the intelligent manufacturing systems, but also provides the necessary guidelines in design and development of such systems, as the concluding remarks. The paper is finalized by future developments and suggestions.     

Using intelligent agents to manage fuzzy business processes

Businesses are undergoing a major paradigm shift, moving from traditional management into a world of agile organizations and processes. An agile corporation should be able to rapidly respond to market changes. For this reason, corporations have been seeking to develop numerous information technology (IT) systems to assist with the management of their business processes. Many of the coming new business processes may contain embedded intelligent agent-based systems. Agent technology looks set to radically alter not only the way in which computers are interacted, but also the way complex processes, e.g., product development, are conceptualized and built. The paper presents a fuzzy approach based on an intelligent agent framework to develop modular products. This approach aims to address the research issue: "How can modular design be carried out through intelligent agents to meet a customer's fuzzy requirements using modules that come from suppliers that are geographically separated and operate on differing computer platforms?" The proposed methodology is applied to a real-world case that involves module-based synthesis at one of largest distribution centers in the world