Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives

Considering the high required capital outlay and moderate risk of a flexible manufacturing system (FMS) investment, economic justification techniques are insufficient by themselves since they cannot cope with the benefits such as flexibility and enhanced quality offered by advanced manufacturing technologies. A robust decision-making procedure for evaluating FMS requires the consideration of both economic and strategic investment measures. A distance-based fuzzy multicriteria decision-making (MCDM) framework based on the concepts of ideal and anti-ideal solutions is presented for the selection of an FMS from a set of mutually exclusive alternatives. The proposed method provides the means for integrating the economic figure of merit with the strategic performance variables. The multicriteria decision approach presented here enables us to incorporate data in the forms of linguistic variables, triangular fuzzy numbers and crisp numbers into the evaluation process of FMS alternatives. Linguistic variables are also used to indicate the criteria's importance weights assigned by the decisionmakers. A comprehensive example illustrates the application of the multicriteria decision analysis.     

An application of the fuzzy ELECTRE method for supplier selection

Supplier selection is vital to the success of a manufacturing firm. Supplier selection is a multi-criteria decision-making problem and is of strategic importance for most companies. As the conventional methods for supplier selection are inadequate for dealing with the imprecise or vague nature of linguistic assessment, a new method called the fuzzy technique for ELECTRE (ELimination Et Choix Traduisant la REalité) is proposed. The aim of this study is to compare and contrast crisp and fuzzy ELECTRE methods for supplier selection. The proposed methods are applied to a manufacturing company in Turkey. After determining the criteria that affect the supplier selection decisions, the results for both crisp and fuzzy ELECTRE methods are presented.     

Selecting suppliers using a new fuzzy multiple criteria decision model: the fuzzy balancing and ranking method

In this paper, we present a fuzzy multiple criteria decision making (FMCDM) model known as fuzzy balancing and ranking. In contrast to other MCDM models, our proposed model does not require the weights of decision making criteria. First, we appraise the performance of alternatives against criteria via linguistic variables which are expressed as triangular fuzzy numbers. The foregoing model obtains the alternative rankings through a four-stage process. Second, an outranking matrix is derived indicating that the frequency with which one alternative is superior to all other alternatives based on each criterion. Third, the outranking matrix is triangularised to obtain an implicit pre-ordering or provisional order of alternatives. Fourth, the provisional order of alternatives is subjected to various screening and balancing operations that require sequential application of a balancing principle to the so-called advantages–disadvantages table that combines the criteria with the pair-wise comparisons of alternatives. Additionally, to demonstrate the procedural implementation of the proposed model and its effectiveness, we apply it on a case study regarding the problem of supplier selection.     

An integrated linguistic MCDM approach for robot evaluation and selection with incomplete weight information

Nowadays selecting the most suitable robot is a difficult task for manufacturing firms due to increase in production demands and availability of various robot models. Robot evaluation and selection can be regarded as a multiple criteria decision-making (MCDM) problem and three key issues are the assessment of robots, the determination of criteria weights and the prioritisation of alternatives. This paper aims to propose an integrated model based on hesitant 2-tuple linguistic term sets and an extended QUALIFLEX approach for handling robot selection problems with incomplete weight information. The new model can not only manage uncertain and imprecise assessment information of decision-makers with the aid of hesitant 2-tuple linguistic term sets, but also derive the important weights of criteria objectively when the weight information is incompletely known. Moreover, based on the extended QUALIFLEX algorithm, the priority orders of robots can be clearly determined and a more reasonable and credible solution can be yielded in a particular industrial application. Finally, a robot selection case study is carried out, and comparative experiments indicate the practicality and effectiveness of the proposed integrated linguistic MCDM approach.     

Ranking alternatives via maximizing set and minimizing set based fuzzy MCDM approach

Abstract

This work suggests a maximizing set and minimizing set based fuzzy multiple criteria decision‐making (MCDM) model, where criteria are classified into cost and benefit criteria. The final fuzzy evaluation value of each alternative is developed based on the concept of subtracting the summation of weighted normalized benefit ratings from that of weighted normalized cost ratings. Using interval arithmetic of fuzzy numbers can develop the membership functions for the final fuzzy evaluation values. Chen's maximizing set and minimizing set is then applied to defuzzify all the final fuzzy numbers for ranking alternatives. Formulas for the membership functions and ranking procedure of the final fuzzy numbers are clearly presented. The suggested method provides an extension to the fuzzy MCDM techniques available. A numerical example demonstrates the computational process of the proposed method.     

Hesitant fuzzy programming technique for multidimensional analysis of hesitant fuzzy preferences

The linear programming technique for multidimensional analysis of preferences (LINMAP) is the most representative method for handling the multiple criteria decision making (MCDM) problems with respect to the preference information over alternatives. This paper utilizes the main structure of LINMAP to develop a novel hesitant fuzzy mathematical programming technique to handle MCDM problems within the decision environment of hesitant fuzzy elements (HFEs). Considering the hesitancy of the decision maker, both the pair-wise comparison preference information over alternatives and the evaluation information of alternatives with criteria are represented by the HFEs. Based on the incomplete pair-wise preference judgments over alternatives, we propose the concepts of the hesitant fuzzy consistency and inconsistency indices. Furthermore, we construct a hesitant fuzzy mathematical programming model to derive the weights of criteria and the positive-ideal solution. In this hesitant fuzzy programming model both the objective function and partial constraints’ coefficients take the form of HFEs, and an effective approach based on the ranking method of HFEs is further developed to solve the new derived model. To address the incomplete and inconsistent preference structures of criteria weights, we introduce several deviation variables and establish the bi-objective nonlinear programming model. At length, we employ a green supplier selection problem to illustrate the feasibility and applicability of the proposed technique and conduct a comparison analysis to validate its effectiveness.     

Using FANP approach on selection of competitive priorities based on cleaner production implementation: a case study in PCB manufacturer,Taiwan

The aim of this paper is to identify and discuss some of the important and critical decision criteria including cleaner production implementation of an efficient system to prioritize competitive priorities. Fuzzy analytic network process (FANP) based methodology is discussed to tackle the different decision criteria involved in the selection of competitive priorities in current business scenario. FANP is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different decision variables. The linguistic level of comparisons produced by the professionals and experts for each comparison are tapped in the form of triangular fuzzy numbers to construct fuzzy pairwise comparison matrices. The implementation of the system is demonstrated by a problem having four stages of hierarchy which contains different criteria, attributes and alternatives at wider perspective. The proposed model can provide a hierarchical framework for the cleaner production implemented organization to select on its competitive priorities.     

Application of fuzzy VIKOR and environmental impact analysis for material selection of an automotive component

Material selection is a complex process, since the process includes many criteria, determination of criteria weight and the most important factor is that the selection of appropriate criterion. The last factor indicates that the criterion must be selected in a manner, such that the selection based upon the known material parameters and the requirements of the application. Therefore the material selection can be done using MCDM (Multi Criterion Decision Making) methods. Since the inputs provided by the decision maker in linguistic manner, there is a possible chance of getting incomplete problems. So in order to overcome the problem, the inputs could be provided as fuzzy numbers. Since fuzzy set represents the uncertainty in human perceptions. In this paper, VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian, means Multicriteria Optimisation and Compromise Solution) has been used a MCDM tool for the selection of alternate material for instrument panel used in electric car and in order to evaluate this selection process in fuzzy environment, fuzzy based VIKOR is used. In addition to the fuzzy VIKOR method, the environmental impacts are also considered and compared for the four materials. The results achieved in both the assessment, showed that Polypropylene could be an alternate material for the instrument panel. The objective of this study is to develop a rational method to select the best material for an application based upon known material parameters and the requirements of the application.     

Application of fuzzy analytic network process for agile concept selection in a manufacturing organisation

Twenty-first century manufacturing organisations are capable of satisfying the varied requirements of modern customers called agile manufacturing (AM). AM enables an organisation to produce a variety of products within a short period of time in a cost-effective manner. Few researchers have brought out models for enabling organisations to improve their agility level. Total agile design system (TADS) is a model that is analogous to the technology integrated agile product development cycle. Concept selection is an important phase of TADS which is a multi-criteria decision-making (MCDM) problem. Selection of the best concept from agile perspective gains vital importance. Analytical network process (ANP) is a holistic approach that considers interactions and dependencies of various attributes and alternatives in a hierarchical framework used in MCDM problems. The concepts of fuzzy logic have been integrated with ANP in order to overcome the vagueness and uncertainty associated with opinions of the decision makers. Fuzzy ANP has been used to enable the selection of best concept. The case study has been carried out in an Indian traditional manufacturing organisation. The results of the validation indicate that fuzzy ANP is an effective approach for selecting best concept thereby improving agility of product development process.     

Global supplier selection: a fuzzy-AHP approach

Global supplier selection has a critical effect on the competitiveness of the entire supply chain network. Research results indicate that the supplier selection process appears to be the most significant variable in deciding the success of the supply chain. It helps in achieving high quality products at lower cost with higher customer satisfaction. Apart from the common criteria such as cost and quality, this paper also discusses some of the important decision variables which can play a critical role in case of the international sourcing. The importance of the political-economic situation, geographical location, infrastructure, financial background, performance history, risk factors, etc., have also been pointed out in particularly in the case of global supplier selection. Supplier selection problem related to the global sourcing is more complex than the general domestic sourcing and as a result it needs more critical analysis, which could not be found properly in past available literatures. This paper discusses the fuzzy based Analytic Hierarchy Process (fuzzy-AHP) to efficiently tackle both quantitative and qualitative decision factors involved in selection of global supplier in current business scenario. The fuzzy-AHP is an efficient tool to tackle the fuzziness of the data involved in deciding the preferences of the different decision variables involved in the process of global supplier selection. The triangular fuzzy numbers are used to transform the linguistic comparison of the different decision criteria, sub-criteria and performance of the alternative suppliers. The pairwise comparison matrices help in deciding the synthetic extent value of each comparison and finally, the priority weights of one alternative over another are decided in this paper. An example from a manufacturing industry searching for the global supplier for a critical component is used to demonstrate the effective implementation procedure of proposed fuzzy-AHP technique. The proposed model can provide the guidelines and directions for the decision makers to effectively select their global suppliers in the current competitive business scenario.     

Development of a component-based machining centre selection model using AHP

Machining centres are widely used in manufacturing companies all over the world. Since investments in machining centres are long-term and expensive, selection of the most appropriate machining centre is an important decision for manufacturing companies. There has been considerable effort spent in developing crisp and fuzzy multi-criteria decision-making (MCDM) models that use technical specifications provided by machining-centre manufacturers such as, axis size, power, spindle speed, tolerance, repeatability, cutting-tool change time, and number of cutting tools along with other economical and commercial factors. However, the technical specifications are directly taken from machining-centre manufacturers’ catalogues without checking their correctness, adequacies, or ability to represent the areas that are used for measurement. In such a case, one cannot be sure whether the outcomes are sound or not without a detailed check of the technical specifications, which can only be performed after actual usage of the machine itself. To overcome all such problems, an analytic hierarchy process (AHP) model that evaluates the machining centre components is developed in this paper. The new component-based AHP model is then compared with two MCDM models that use only technical specification values.     

Selection of materials using compromise ranking and outranking methods

Selection of proper materials for different components is one of the most challenging tasks in the design and development of products for diverse engineering applications. Materials play a crucial and important role during the entire design and manufacturing process. Wrong selection of material often leads to huge cost involvement and ultimately drives towards premature component or product failure. So the designers need to identify and select proper materials with specific functionalities in order to obtain the desired output with minimum cost involvement and specific applicability. This paper attempts to solve the materials selection problem using two most potential multi-criteria decision-making (MCDM) approaches and compares their relative performance for a given material selection application. The first MCDM approach is ‘Vlse Kriterijumska Optimizacija Kompromisno Resenje’ (VIKOR), a compromise ranking method and the other one is ‘ELimination and Et Choice Translating REality’ (ELECTRE), an outranking method. These two methods are used to rank the alternative materials, for which several requirements are considered simultaneously. Two examples are cited in order to demonstrate and validate the effectiveness and flexibility of these two MCDM approaches. In each example, a list of all the possible choices from the best to the worst suitable materials is obtained taking into account different material selection criteria. The rankings of the selected materials almost corroborate with those as obtained by the past researchers.     

模糊理想解法在多准则群决策中的应用

将模糊数学理论引入到不确定环境下的多准则群决策问题中,利用模糊理想解法进行方案排序.用三角模糊数来描述模糊准则权重和模糊准则属性值,给出了使用模糊理想解法进行群决策问题求解的一般步骤;通过供应商选择这一实例验证了本方法的有效性.     

A fuzzy logic based approach to reliability improvement estimation during product development

During early stages of product development process, a vast amount of knowledge and information is generated. However, most of it is subjective (imprecise) in nature and remains unutilized. This paper presents a formal structure for capturing this information and knowledge and utilizing it in reliability improvement estimation. The information is extracted as improvement indices from various design tools, experiments, and design review records and treated as fuzzy numbers or linguistic variables. Fuzzy reasoning method is used to combine and quantify the subjective information to map their impact on product reliability. The crisp output of the fuzzy reasoning process is treated as new evidence and incorporated into a Bayesian framework to update the reliability estimates. A case example is presented to demonstrate the proposed approach.     

An aggregation technique for optimal decision-making in materials selection

Materials selection is an onerous but very important activity in the design process. An inappropriate choice of material(s) can adversely affect the productivity and profitability and hence reputation of a manufacturing organization. The complexity of materials selection makes multi-criteria analysis an invaluable tool in the engineering design process. However, the application of various multi-criteria decision making (MCDM) methods can yield different results, especially when alternatives lead to similar performance. Therefore, an aggregation technique is proposed in this paper for optimal decision-making. In this approach, ranking orders obtained by various MCDM methods are used as the input of the suggested procedure and the outputs are aggregation rankings, which help designers and engineers to reach a consensus on materials selection for a specific application. An illustrative example is given to demonstrate the application of this procedure and its effectiveness in obtaining optimal materials selection.     

Effects and relationships of parameters in a rule–based fuzzy system

Hierarchies of criteria and fuzzy rule systems represent an approach which effectively portrays the problem of multiple objectives found in classic decision–making scenarios such as order sequencing or the evaluation of various corporate strategies. They further help the decision–maker to select a concrete course of action. The relevant decision criteria are formalized with the help of linguistic variables. A hierarchy of criteria is then used to relate them to one another and to combine them by rule–bases. Evaluation using fuzzy sets leads to an aggregated rating of the individual alternatives. A problem of such fuzzy systems is the large number of required parameters and their influence on the evaluation results. This article will use the example of a simple criteria hierarchy for a sequencing problem to analyze the effects of individual parameters and their relationships amongst one another in a fuzzy system.     

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.     

An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis

Selecting a proper machine tool is one of the important decisions a company has to make. Companies which fail to do so face many problems which negatively affect the firm's productivity, flexibility, precision and its responsiveness capabilities. Selection of a machine tool involves a lot of criteria to be simultaneously studied and so it requires a multi-criterion decision making (MCDM) method to solve it. Also the subjectivity involved in such decisions ask for the use of theories such as fuzzy and grey which are very effective in handling subjective inputs. This paper integrates the fuzzy analytic hierarchy process (AHP) and grey relational analysis approaches for the selection of a machine tool from a given set of alternatives. Fuzzy AHP is used to calculate the priority weights of the criteria. Subsequently grey relational analysis (GRA) is employed to rank the alternatives. A well known problem existing in literature has been picked up for the numerical illustration. The results obtained in this paper are better when compared with that existing in literature.     

Fuzzy estimation for manufacturing flexibility

This paper extends the definition of manufacturing flexibility by identifying new elements and developing a new way to model these elements as fuzzy and/or crisp numbers. Then the manufacturing flexibility of the system is aggregated by both crisp and fuzzy flexibility elements with different important weights. Numerical examples are provided to analyse the limitation of previous approaches and illustrate the proposed model.     

Application of new fuzzy-weighted average (NFWA) method to engineering design evaluation

Engineering design evaluation is characterized by imprecise (vague) importance and satisfaction levels of criteria, which are better treated as fuzzy variables rather than as subjective crisp variables. Nevertheless, operations on fuzzy numbers tend to increase unnecessarily the imprecision when they are combined for some applications, particularly, when a fuzzy-weighted average (FWA) is calculated. This paper proposes a novel method of operating on fuzzy numbers to obtain a fuzzy-weighted average of desirability levels during design evaluation. The method produces overall desirability levels less imprecise and more realistic than those of the conventional FWA. Furthermore, the balance points of these fuzzy numbers seem to be more credible than those of the FWA. An example is presented, which demonstrates the advantages of the method developed.