A hybrid approach to machine-tool selection through AHP and simulation

The selection process of a machine tool has been a critical issue for companies for years, because the improper selection of a machine tool might cause many problems having a negative effect on productivity, precision, flexibility, and a company's responsive manufacturing capabilities. Therefore, in this paper, to determine the best machine tool satisfying the needs and expectations of a manufacturing organization among a set of possible alternatives in the market, a hybrid approach is proposed, which integrates an analytic hierarchy process (AHP) with simulation techniques. The AHP as one of the most commonly used multiple criteria decision-making methods is used to narrow down all possible machine tool alternatives in the market by eliminating those whose scores (or weights) are smaller than a determined value obtained under certain circumstances. Then, a simulation generator is used first to automatically model a manufacturing organization, where the ultimate machine tool will be used, and second to try each alternative remaining from the AHP as a scenario on the generated model. Finally, the final alternative is selected by using the unit investment cost ratio, which is calculated by dividing the investment cost per year of each alternative by the additional number of produced units obtained from the simulation experiment of the relevant alternative.     

A multi-attribute selection of automated guided vehicle using the AHP/M-GRA technique

The objective of this research is to develop and implement a new methodology for multi-attribute selection of automated guided vehicle (AGV) for the material handling. The present paper proposes a modified grey relational analysis (M-GRA) method combined with analytical hierarchy approach (AHP). AHP method is used to assign the relative importance between AGV selection attributes and M-GRA method is applied to determine AGV selection utility index. A proposed AHP/M-GRA technique is more suitable for the decision-making in the presence of vagueness. The methodology is illustrated by means of an example. The study was followed by the validation and the sensitivity analysis of the AHP/M-GRA technique. The results of the study build confidence in suitability of AHP/M-GRA technique for selection of AGV alternatives.     

Application of analytical network process for the evaluation of sustainable business practices in an Indian relays manufacturing organization

Sustainable business strategies or technologies determine the market dynamism so as to withstand competitiveness. The evaluation of such strategies is a multi-criteria decision making (MCDM) problem. To deal with such problems, analytical hierarchy process (AHP) and analytical network process (ANP) could be deployed. AHP considers the distribution of goal amongst the elements being compared and judges the element which has the greater influence on the goal. In order to overcome the drawbacks with AHP, ANP could be used which is a holistic approach in which all attributes and alternatives involved are connected in a network system that accepts dependencies. Priorities which are established in ANP are the same way as they are in the AHP using pair wise comparisons and judgments. ANP is the most comprehensive framework for the analysis of societal, governmental and corporate decisions that are available today to the decision-maker. It is a process that allows one to include all the factors and criteria, tangible and intangible that has an effect on making a best decision. The paper reports a research study that is focused on the selection of the best alternative from the perspective of sustainability for an Indian relays manufacturing organization.     

Technical attribute prioritisation in QFD based on cloud model and grey relational analysis

Promptly development of new products can be achieved through quality function deployment (QFD) process, which is critical to companies’ survival. Since the multi-criteria decision-making problem involved in QFD, a novel method integrating cloud model and grey relational analysis is put forward in this paper. Taking into account the subjectivity and ambiguity in linguistic evaluations, some scholars utilise fuzzy theory, rough theory, interval-valued fuzzy-rough sets and MCDM methods to improve traditional QFD. However, much priori information requirements, inability to handle subjectivity and randomness, and lack of mechanism to overcome small sample size problem are some inevitable drawbacks in these methods. To solve these deficiencies, a hybrid methodology is proposed in this paper, integrating the fortes of cloud model in processing ambiguity and randomness, and the merits of grey relational analysis in overcoming small sample size error as well as revealing the inner correlations. The comparative analysis of different approaches as well as the sensitivity analysis of criteria weights is implemented to prove the stability of the novel method. The results obtained in this paper shows that the proposed method can be a practical tool for improving the efficiency and accuracy of traditional QFD in reality management.     

医疗设备使用风险评估中的FMECA新方法

基于FMECA的风险分析方法可以帮助医疗机构更加有效地评估、控制医疗设备的使用风险。根据医疗设备的使用特点,利用模糊数学与灰色关联理论对方法进行改进,可更为客观地得出各风险模式的模糊评价结果和优先控制顺序。新方法以C臂机的风险分析为实例,说明其改进和应用过程。新方法扩展了FMECA的应用领域,为医疗设备的科学管理提供了依据。     

Materials selection for cleaner production: An environmental evaluation approach

This paper demonstrates an integrated approach to the selection of materials for cleaner production, using binary dominance matrix and grey relational analysis to aid decision-making by engineers. The starting point of this study is to determine the environmental evaluation criteria and to assign weights reflecting their relative importance. In order to assist engineers in the application of the grey evaluation process, and to simplify the computational complexity, a calculating tool based on spreadsheet application has been developed. A case example is provided, in which five poly(vinyl chloride) (PVC) materials are subjected to environmental evaluation to determine their sustainability for handbag manufacture. The result of the grey relational analysis and the limitations of the approach are discussed, laying a foundation for further work.     

An integrated product service system modelling methodology with a case study of clothing industry

Product-service system (PSS) is a business model designed to help enterprises achieve servitisation. PSS not only considers service-oriented logic to increase product value but also supports enterprises with waste reduction. An increasing number of studies have emphasised PSS development. However, much of the literature remains focused on separate development phases, such as idea generation or a specific product or industry application. An integrative system modelling methodology designed to help companies formulate a complete scenario is absent. This study provides a methodology for developing a systematic and complete PSS strategy. It combines failure modes and effects and importance-performance analysis to identify potential customer needs. A service-product development matrix and a product-service life cycle analysis are developed. Following the service concepts generation, grey relational analysis and scenario analysis are utilised to evaluate the alternatives. A case study involving the clothing industry was conducted to test the proposed approach. Results indicate the methodology can help service providers identify existing problems and develop service alternatives systematically. These alternatives can then be evaluated and the scenario which achieves the highest profitability, is the most environmental friendly, and is the most social friendly can be identified.     

A fuzzy AHP-based simulation approach to concept evaluation in a NPD environment

The evaluation process of conceptual design alternatives in a new product development environment is a critical point for companies who operate in fast-growing markets. Various methods exist that are able to successfully carry out this difficult and time-consuming process. One of these methods, the Analytic Hierarchy Process (AHP) has been widely used to solve multiple-criteria decision-making problems (i.e., concept evaluation, equipment selection) in both academic research and in industrial practice. However, due to vagueness and uncertainty in the decision-maker's judgment, a crisp, pair-wise comparison with a conventional AHP may be unable to accurately capture the decision-maker's judgment. Therefore, fuzzy logic is introduced into the pair-wise comparison in the AHP to compensate for this deficiency in the conventional AHP. This is referred to as fuzzy AHP. In this paper, a fuzzy AHP method is used to reduce a set of conceptual design alternatives by eliminating those whose scores (or weights) are smaller than a predetermined constant value obtained under certain circumstances. Then, simulation analysis is integrated with the fuzzy AHP method, and the hybrid method is used to help the decision-makers (product engineers or managers) evaluate the remaining alternatives from the fuzzy AHP method. A real-life manufacturing system is used as the testbed for the proposed techniques. Finally, the results of both techniques, fuzzy AHP and simulation, are used for Preference Ratio analysis to reach to the final alternative.     

Group-based product scheme-screening decision method based on fuzzy AHP and evidential reasoning theory

Selecting the favourable product scheme is the first step to successful new product development (NPD). There are usually large numbers of uncertainties in product scheme evaluation and screening process of NPD due to lack of or incomplete reliable information. Considering fully the uncertainties and then conducting correct reasoning could guarantee reliability and rationality of scheme-screening results. As an extension of analytic hierarchy process (AHP), fuzzy AHP inherits multi-merits of the AHP approach and is capable of dealing with fuzzy information effectively, but it still has two weaknesses. One is the well-known ranking reversal problem. Although several researchers have analysed the reasons, we think the root cause for ranking reversal problem is due to the fact that AHP treats weights of attribute criteria and performance scores of alternatives in the same way. Therefore, we intend to deal with attribute weights and performance scores of alternatives separately and introduce evidential reasoning (ER) theory, which is good at uncertain reasoning, into fuzzy AHP to calculate the performance scores of alternatives. On the other hand, in view of the difficulty in resolution for fuzzy weights from fuzzy comparison matrix, a linear goal-programming model is proposed to calculate fuzzy weights, whose objective is to minimise the inconsistency degree of comparison matrix. By combining fuzzy AHP with ER, a group-based hybrid decision model FAHP-ER is developed. The hybrid model not only gets a great improvement in the capability of dealing with uncertainty, but also reflects the most real decision scenario and thinking process of the decision maker. Finally, a case study for schemes screening of the rotor and bearing system in the turbine generator is presented to demonstrate the application of the hybrid decision method.     

Modeling system behavior for risk and reliability analysis using KBARM

With advances in technology and the growing complexity of technological systems, the job of the reliability/system analyst has become more challenging as they have to study, characterize, measure and analyze the behavior of systems with the help of various traditional analytical (mathematical and statistical) techniques, which require knowledge of the precise numerical probabilities and component functional dependencies, information which is difficult to obtain. Even if data are available they are often inaccurate and are thus subject to uncertainty, i.e. historical records can only represent the past behavior and may be unable to predict the future behavior of the equipment. To cope with such situations, the knowledge‐based approximate reasoning methodologies (KBARMs) provide necessary help. Among them, the fuzzy and grey methodologies are the most viable and effective tools for coping with imprecise, uncertain and subjective information in a consistent and logical manner. In this paper, the authors present a methodological and structured approach (which makes use of both qualitative and quantitative techniques) to model, analyze and predict the failurebehavior of two units, namely the forming and press units of a paper machine, using KBARMs. Various system parameters such as repair time, failure rate, mean time between failures, availability and expected number of failures are computed to quantify the system behavior in terms of fuzzy, crisp and defuzzified values. Furthermore, a risk ranking approach based on fuzzy and grey relational analysis is discussed to prioritize various failure causes associated with the components in failure mode and effects analysis (FMEA). Copyright © 2007 John Wiley & Sons, Ltd.     

Decision making on power customer satisfaction and enterprise profitability analysis using the Analytic Hierarchy Process

The purpose of this paper is to investigate the customer satisfaction of power users using the Analytic Hierarchy Process (AHP) method. The objective is to safeguard the interests of electricity consumers and to increase the profitability of the energy distributer. Compared with previous work based on customer questionnaires describing the level of satisfaction, and where the solution is judged to be random due to the low significance of the studied sample, this paper develops a novel strategy. It is based on a global and transparent process regarding the reliability and economic criteria associated with alternatives, highlighting technical and organisational measures taken by the enterprise. The importance of reliability and economic criteria to the alternatives is processed using reliability indices analysis and cost benefit analysis methods. To analyse the customer's reactions to the decisions taken by the system's manager, and to validate the obtained results using the AHP method, we introduce economic criteria often used in the case of an uncertain future. The obtained results indicate the advantage of investment to improve customer satisfaction and enterprise profitability. It is also shown that reliability criteria are significant in the performance of a business and are an important asset for the justification of new projects.     

Quantifying uncertainty in multicriteria concept selection methods

Decision support for concept selection in engineering design ranges from simple pairwise comparison techniques to methods that consider multiple criteria. The Analytic Hierarchy Process, or AHP, is an example of a multicriteria selection tool with wide-spread industry application. It is recognized by responsible practitioners that AHP, like other decision support methods, is best used not as an optimization tool, but as a means of clarifying individual or group attitudes; the numerical rankings that are its output are not definitive. This paper offers a means to quantify how differently two alternatives must be ranked by AHP to instill confidence that one is truly better than the other, a question that is in practice always answered using intuition. The quantification of uncertainty in AHP relies on the extension of concepts from statistical hypothesis testing. The procedure is not stochastic in the same way that physical measurement is, so probability distributions are created over relevant parameters. The quantified uncertainty depends, as in all statistical analysis, upon the assumed distributions. The uncertainty in AHP is quantified from two distinct points of view. The first makes the assumption that AHP is structurally correct but subject to measurement “error” in the pairwise comparisons, while the second quantifies the uncertainties introduced by AHP’s failure to consider different level of compensation in trade-offs among criteria.     

Prioritising the types of manufacturing flexibility in an uncertain environment

Researchers have stressed that manufacturing system flexibility research requires a quantitative model allowing a manufacturing system to prioritize its flexibility dimension and promote the performance of the manufacturing system. A quantification model presented in the present research is demonstrated to assess the degree of environmental uncertainty and illustrates a method for delivering the requirement of flexibility improvement for the manufacturing system so that the company is able to prioritize the types of manufacturing flexibility which a manufacturing system requires in an uncertain environment. Quantitative approaches including quality function deployment (QFD), analytical hierarchy process (AHP), and grey relational analysis (GRA) have been employed to find a means for improving the flexibility of a manufacturing system to cope with environmental uncertainty. QFD is the focal approach for the deployment of the integrated structure of the research. AHP is applied to explore the relative weighted importance of environmental uncertainty factors, while GRA is used to find out the relationships between manufacturing flexibility and environmental uncertainty. A combination of these approaches reveals a useful tool for managers to prioritize the types of flexibility which a manufacturing system requires for coping with an uncertain environment. In particular, the present research studied the manufacturing flexibility requirements of a food company in Taiwan.     

Grey-fuzzy algorithm to optimise machining parameters in drilling of hybrid metal matrix composites

Metal matrix composites (MMCs) are difficult to machine due to their abrasive properties. With the projected widespread application of MMCs, it is necessary to develop an appropriate technology for their effective machining. The present investigation focuses on finding the optimal machining parameters setting in drilling of hybrid aluminium metal matrix composites using the grey-fuzzy algorithm. This proposed algorithm, coupling the grey relational analysis with the fuzzy logic, obtains a grey-fuzzy reasoning grade to evaluate the multiple performance characteristics according to the grey relational coefficient of each performance characteristics. The Taguchi method of experimental design is a widely accepted technique used for producing high quality products at low cost, therefore a L₂₇ 3-level orthogonal array is used for the experiments. The optimisation of multiple responses in complex processes is common; therefore, to reduce the degree of uncertainty during the decision making, fuzzy rule-based reasoning is integrated with the Taguchi’s method. The response table, response graph and analysis of variance (ANOVA) are used to find the optimal setting and the influence of machining parameters on the multiple performance characteristics. Experimental results have shown that the required performance characteristics in the drilling process are improved by using this approach.     

药品智能包装的模糊灰色故障树分析

目的对某款药品智能包装从系统层面进行可靠性分析。方法首先,通过失效分析建立药品智能包装的故障树;其次,利用专家判断知识和模糊数学理论,得到故障树底事件和顶事件的三角形式模糊失效率,以及采用模糊中值法进行底事件模糊重要度计算;最后,运用灰色关联分析求出表征各最小割集与顶事件相关行为关系的灰色关联度。结果定量分析计算显示,药品智能包装模糊失效率为(0.146 59,0.179 69,0.206 91),故障相关性最大的为包装材料本身的功能失效,其次为外界的振动和冲击以及包装尺寸误差等。结论模糊灰色关联故障树同时考虑包装系统的模糊性和灰色性,它可为预防药品智能包装失效、提高智能包装可靠性和安全性提供理论依据。     

An integrated fuzzy multi-criteria group decision-making approach for green supplier evaluation

Purchasing is one of the most vital functions within a company and supplier performance evaluation is one of the most important business processes of the purchasing function. Traditionally, companies have considered factors such as price, quality, flexibility, etc. while evaluating suppliers. However, environmental pressures urge them to consider green issues. This study proposes a decision model for supplier performance evaluation by considering various environmental performance criteria. An integrated, fuzzy group decision-making approach is adopted to evaluate green supplier alternatives. More precisely, a fuzzy analytic hierarchy process (AHP) is applied to determine the relative weights of the evaluation criteria and an axiomatic design (AD)-based fuzzy group decision-making approach is applied to rank the green suppliers. Finally, a case study is given to demonstrate the potential of the methodology.     

基于AHP-熵权法的电火花线切割机床设计

目的 为了研究并解决电火花线切割机床在设计中存在的问题,反映技术人员对机床设计的满意度。方法 通过AHP和熵权法对评价指标赋权,搭建模糊评价模型;通过调研和专家意见,从技术人员物理尺度、安全性、功能与操作、美学和操作人员期望等5个层面建立评价指标体系;运用AHP层次分析法进行主观权重分析,通过熵权法对指标进行客观权重分析,最后通过模糊综合评价法对设计满意度进行评价。结果 以机床操作人员的需求为基础,通过对专家和技术人员调研数据的分析,设计了较为完善的电火花线切割机床。结论 AHP-熵权法较大程度地保留了技术人员的客观评价和行业专家的主观判断,体现了主客观相结合赋权的科学性。评价模型为机床外观设计的改进提供了理论依据和量化指标,同时对提升技术人员操作满意度具有重要作用。     

A distinct decision model for the evaluation and selection of a supplier for a chemical processing industry

The supplier selection process has gained importance recently due to the considerable amount of revenue spent on purchasing. The intention of this work is to develop an appropriate hybrid model by integrating the analytical hierarchy process (AHP) and grey relational analysis (GRA) for supplier evaluation and selection, which comprises three stages. In Stage I, the most influential criteria are selected by mutual-information-based feature selection. Stage II focuses on the determination of the weights of the attributes using AHP, while Stage III is used for the determination of the best supplier using GRA. The proposed model is illustrated using the case study of an electroplating industry to highlight the effectiveness and flexibility of the model. The model effectively combines specialised knowledge, experience and quantitative data to select the best suppliers. This paper presents the model development, solution and application processes of the proposed hybrid model for supplier selection. The decision support software was implemented in Excel to automate supplier selection. The proposed hybrid model is applied to enhance the decision-making process in supplier selection and also helps decision makers to effectively select suppliers.     

Fuzzy hybrid decision model for supplier evaluation and selection

This paper proposes a structured, integrated decision model for evaluating suppliers by combining the fuzzy analytical hierarchy process (FAHP) and grey relational analysis (GRA). The qualitative and partially-known information is incorporated in this decision model using the fuzzy set theory. In this proposed methodology, the weights of the evaluation criteria are calculated by using FAHP, then the ranking of the suppliers is determined by using GRA. Finally to show the robustness of the model, a sensitivity analysis is also performed. In this study, the supplier selection problem of an electroplating industry in the southern part of India was investigated, demonstrating the effectiveness of this developed integrated model. This model can help in solving the complex decision in supplier selection practice. The results generated from the model are properly validated and finally a systematic solution with decision support is provided for decision makers. This model can be integrated with other decision support systems of similar kinds of industries.     

基于灰色系统理论的城市公共交通服务质量评估

以灰色理论为基础,通过灰色关联度分析设计关联矩阵,提出了一个分析城市公共交通服务质量的灰关联评估模型,该模型选取了线网密度等6个因素构成的集合作为评估指标集,并由此构成评估系列集。在构建模型的基础上,以我国北方某市2002年的资料为例进行了实际检验,结果表明,应用灰色关联分析方法,可以有效确定影响服务质量的因素对服务质量所产生影响的重要程度,从而为服务企业提供控制服务质量的决策依据,更有效地避免服务失误的发生。