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.     

A novel approach for supplier selection based on the Kano model and fuzzy MCDM

This paper proposes a three-phase approach for supplier selection based on the Kano model and fuzzy Multi Criteria Decision-Making. Since the supplier selection problem involves different criteria, quality attributes have been assumed to denote the importance weight of the criteria for supplier selection. Furthermore, to consider the inherent vagueness of human thought, a fuzzy logic has been utilised. Initially, the importance weight of the criteria has been calculated using a fuzzy Kano questionnaire and fuzzy analytic hierarchy process. In the second phase, the Fuzzy TOPSIS technique has been used to screen out in capable suppliers. Finally, in the third phase, the filtered suppliers which are qualified, once again will be evaluated by the same approach for the final ranking. The proposed approach has also been examined in a case study.     

Maintenance supplier selection considering life cycle costs and risks: a fuzzy goal programming approach

In this study, we address a new variant of supplier selection problem named maintenance supplier selection problem faced by a manufacturer. The production system consists of different multi-component equipments whose maintenance activities require several components (parts) each of which could be provided by multiple suppliers. A multi-objective mathematical model is developed to decide about the supply base of each part as well as the purchasing quantity of each part from each selected supplier. The model accounts for the total life cycle costs of purchased parts and various risks threatening the candidate suppliers. A fuzzy/soft lexicographic goal programming approach with soft priorities between objectives is proposed to enable the decision-maker to make preferred trade-offs between objectives by which the effects of various risks in each phase of life cycle of procured parts are investigated. The capability and effectiveness of the proposed model is validated through a case study. Some sensitivity analyses are also carried out for investigating the impact of cost, risk and objectives’ priorities on the final preferred compromise solution. Finally, some managerial insights and concluding remarks are provided.     

Maintenance and Condition-Based Maintenance

1IntroductionSystem reliability and availability have been widely studied in the literature because of their preva-lence in industry systems. Maintaining a high or required level of reliability and availability is oftenan essential requisite.Up to the 1950′s a fix it when it breaks′philosophy (BM, break maintenance) was mostly imple-mented due to lack of engineering knowledge in certain areas. The regular inspection regime of time-based maintenance (PM, preventive maintenance) was later in…     

A new decision support system for performance measurement using combined fuzzy TOPSIS/DEA approach

It is apparent that there is a need for integrated criteria in the performance measurement of modern organisations when dealing with the performance measurement of manufacturing businesses. This paper addresses this issue in the context of measuring the performance of the 2nd Air Supply and Maintenance Center Command manufacturing/maintenance jobshops of Turkey by using a new framework which combines fuzzy TOPSIS (technique for order preference by similarity to ideal solution) for measuring qualitative performance with DEA (data envelopment analysis) for measuring quantitative performance. This proposed approach provides a comprehensive measure of performance incorporating both quantitative and qualitative attributes, which in general reflect efficiency and effectiveness of the manufacturing jobshops respectively.     

模糊多属性决策的维修服务质量评价

维修服务质量是不同属性的综合体，它包括许多很难被测量的不可感知属性。这种特性给被测者在接受问卷调查时带来了一些困难。为了避免上述情况的出现，运用模糊理论评价维修点的维修服务质量，建立了模糊多属性决策模型，并将模糊集理论引入不可感知属性指标值的测度中。用层次分析法获得准则的权重和用逼近理想解排序法进行维修服务质量的排序后，得到了满意的结果。     

An integrated fuzzy approach for the selection of outsourcing manufacturing partners in pharmaceutical R&D

The selection of the outsourcing manufacturing partners (OMPs) is an important issue for research and development (R&D) in the pharmaceutical industry. The selection process considers several main criteria, as well as a few sub-criteria for each main criterion. Such problems can be formulated as hierarchical structures and can usually be resolved with multi-criteria decision-making approaches. This paper presents an integrated fuzzy approach for selecting a suitable OMP in pharmaceutical R&D. In the integrated approach, fuzzy concepts are used for decision-makers’ subjective judgments to reflect the vague nature of the selection process. Fuzzy AHP and fuzzy TOPSIS are included in the integrated approach. Fuzzy AHP is used to determine the fuzzy weights of criteria and sub-criteria because it can effectively determine various criteria's weights in a hierarchical structure. Fuzzy TOPSIS aims to find the OMP with the best performance with respect to the sub-criteria. We exemplify the integrated approach using a numerical application to demonstrate its feasibility.     

Robot selection using a fuzzy regression-based decision-making approach

Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously considered in a robust selection study. In this study, a decision model based on fuzzy linear regression is presented for industrial robot selection. Fuzzy linear regression provides an alternative approach to statistical regression for modelling situations where the relationships are vague or the data set cannot satisfy the assumptions of statistical regression. The results obtained by employing fuzzy linear regression are compared with those of earlier studies applying different analytical methods to a previously reported robot selection problem.     

A systematic approach to determine the optimal maintenance policy for an automated manufacturing system

We propose a systematic approach to determine the optimal maintenance policy for an automated manufacturing system which includes a flexible manufacturing cell (FMC) and several automated machine shops. The systematic approach combines simulation, fractional factorial design, noise or outer array of Taguchi design, regression metamodelling, and classical queueing analysis. A useful expression of the fractional utilization of the manufacturing system is derived and incorporated into formulating and solving the corresponding decision problem. The systematic approach provides an effective implementation procedure to handle practical maintenance problems found in a complex manufacturing environment. © 1997 John Wiley & Sons, Ltd.     

Impact of the use-based maintenance policy on the performance of cellular manufacturing systems

In this paper, a multi-objective integer programming approach is developed to investigate the impact of the use-based preventive maintenance (UPM) policy on the performance of the cellular manufacturing system (CMS). Under the UPM policy a maintenance schedule is established which provides for the performance of preventive maintenance (PM) only after a predetermined number of operating hours of machine use. This research indicates how PM and failure repair (FR) actions affect the effective availability of the machines and accordingly the machine and inter/intra-cell material handling costs under the UPM policy. The objective is to minimise the machine cost, inter- and intra-cell material handling and PM/FR costs. The proposed model is solved by an interactive fuzzy programming (IFP) approach to determine the best compromise solution from the decision maker point of view. IFP assumes that each objective function has a fuzzy goal and focuses on minimising the worst upper bound to obtain an efficient solution which is close to the best lower bound of each objective function. Compromise solutions are prioritised by two efficiency criteria, i.e. grouping efficiency and system availability. The performance of the proposed model is verified by a comprehensive numerical example.     

Hesitant fuzzy integrated MCDM approach for quality function deployment: a case study in electric vehicle

Quality function deployment (QFD) is a product planning management instrument which has been used in a broad range of industries. However, the traditional QFD method has been criticised much for its deficiencies in acquiring experts’ opinions, weighting customer requirements (CRs) and ranking engineering characteristics (ECs). To overcome the limitations, an integrated analytical model is presented in this study for obtaining the importance ratings of ECs in QFD by integrating decision-making trial and evaluation laboratory (DEMATEL) technique and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method under hesitant fuzzy environment. In particular, the hesitant fuzzy DEMATEL is used to analyse the interrelationships among CRs and determine their weights, and the hesitant fuzzy VIKOR is utilised to prioritise ECs. Finally, the feasibility and practicality of the proposed method are verified by an example regarding the product development of electric vehicle.     

Leanness assessment using multi-grade fuzzy approach

The manufacturing organisations have been witnessing a transition from mass manufacturing to lean manufacturing. Lean operations are characterised by the elimination of obvious wastes occurring in the manufacturing process, thereby facilitating cost reduction. This paper reports a research carried out to assess the leanness of an organisation using multi-grade fuzzy approach. During this research, a leanness measurement model incorporated with multi-grade fuzzy approach was designed. This is followed by the substitution of the data gathered from a manufacturing organisation. After the computation of leanness index, the areas for leanness improvement have been identified. The approach contributed in this project could be used as a test kit for periodically evaluating an organisation's leanness.     

Supplier selection using fuzzy association rules mining approach

Owing to ill-structured, dynamic environments and the presence of multiple decision-makers with conflicting viewpoints, comprehension, analysis and support of the supplier evaluation process becomes more and more difficult. Moreover, with the complexities of issues such as the role of leadership, the influence of group formation, and analysis of disagreements, it cannot be predictable that there will ever exist a solution to cope with all imprecise, multi-criteria/multi-actor situations. A fuzzy association rules-based approach may be suited for the judgement of human subjects. In this paper, we develop an approach based on Fuzzy Association Rule Mining to support the decision makers by enhancing the flexibility in making decisions for evaluating suppliers with both tangibles and intangibles attributes. Also, by checking the fuzzy classification rules, the goal of knowledge acquisition can be achieved in a framework in which assessments could be established without constraints, and consequently checked and compared in several details. The efficacy and intricacy of the proposed model for finding fuzzy association rules from the database for supplier assessment is demonstrated with the help of numerical examples.     

Justification for the selection of manufacturing technologies: a fuzzy-decision-tree-based approach

In this paper, a developed model for the justification of alternative manufacturing technologies is presented. The approach, based on fuzzy decision trees, provides a methodology capable of identifying patterns within a technology case repository to support the evaluation of manufacturing systems. Experts are highly influential individuals in the decision process; they provide support and guidance when selecting investments. The experience-oriented task is founded on previous cases or an experts’ experience, and therefore difficult to express in a rational form. The concept is based on a number of characteristics of the case-based reasoning, rule induction and expert system theory. Structured around the fuzzy-decision-tree data-mining technique, the framework provides the ability of using regulated case information to act as structured experience for assisting in the decision process. Fuzzy induction extracts formal rules from a set of experience data, and the expert system philosophy computes the experience base of human expertise for problem-solving. A test case indicates the stability of the classification algorithm and verifies the applicability within the domain.     

Machine selection in flexible manufacturing cell: A fuzzy multiple attribute decision-making approach

Flexible manufacturing cells (FMC) have been used as a tool to implement flexible manufacturing processes to increase the competitiveness of manufacturing systems. In implementing an FMC, decision-makers encounter the machine selection problem including attributes, e.g. machine type, cost, number of machines, floor space and planned expenditures. This paper proposes a fuzzy multiple attribute decision-making (FMADM) model to assist the decision-maker to deal with the machine selection problem for an FMC realistically and economically. In addition, the membership functions of weights for those attributes are determined in accordance with their distinguishability and robustness when the ranking is performed.     

A genetic programming based fuzzy regression approach to modelling manufacturing processes

Fuzzy regression has demonstrated its ability to model manufacturing processes in which the processes have fuzziness and the number of experimental data sets for modelling them is limited. However, previous studies only yield fuzzy linear regression based process models in which variables or higher order terms are not addressed. In fact, it is widely recognised that behaviours of manufacturing processes do often carry interactions among variables or higher order terms. In this paper, a genetic programming based fuzzy regression GP-FR, is proposed for modelling manufacturing processes. The proposed method uses the general outcome of GP to construct models the structure of which is based on a tree representation, which could carry interaction and higher order terms. Then, a fuzzy linear regression algorithm is used to estimate the contributions and the fuzziness of each branch of the tree, so as to determine the fuzzy parameters of the genetic programming based fuzzy regression model. To evaluate the effectiveness of the proposed method for process modelling, it was applied to the modelling of a solder paste dispensing process. Results were compared with those based on statistical regression and fuzzy linear regression. It was found that the proposed method can achieve better goodness-of-fitness than the other two methods. Also the prediction accuracy of the model developed based on GP-FR is better than those based on the other two methods.     

Fuzzy approach to dependability performance evaluation

This paper presents a model for dependability performance evaluation by fuzzy sets utilization. Basic dependability indicators (reliability, maintainability and maintenance support) are used for the analysis of technical systems' conditions from the aspects of design, construction, maintenance and logistics. These indicators as well as associated dependability expressions itself are described by linguistic variables, which are characterized by a membership function to the defined classes. The proposed model is primarily appropriate for introduction, analysis and synthesis of information related to quality of systems in operation. Such data are often available only as experts' judgment and estimations. A practical engineering example (mechanical system at bucket wheel excavator) has been presented to demonstrate the proposed dependability analysis and synthesis model. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

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.     

Optimal process plan selection in networked based manufacturing using game-theoretic approach

This paper proposes a scheme for generating optimal process plans for multi jobs in a networked based manufacturing system. Networked manufacturing offers several advantages in the current competitive atmosphere such as reducing short manufacturing cycle time and maintaining the production flexibility, thereby achieving several feasible process plans. An N-person non-co-operative game with complete information is proposed and a mathematical model has been developed to generate the payoff functions. To be part of a game, we divided the game into two sub-games such as games to address sub-game (GASG) and games to solve sub-game (GSSG) which try to interact with each other and achieve the Nash equilibrium (NE). Consequently, a hybrid dynamic-DNA (HD-DNA) based evolutionary algorithm approach has been developed for more effective solutions of the game and also for finding the perfect NE points. The objective of this game is to generate the optimal process plans to minimise the makespan. Finally, three cases having different job complexities are presented to demonstrate the feasibility of the approach. The proposed algorithm is validated and results are analysed to benefit the manufacturer.