Proposing a graph ranking method for manufacturing system selection in high-tech industries

All manufacturing centers are looking for the solutions to reduce costs and increase their competitive advantages. One of the practical solutions for cost reduction is to select a suitable manufacturing system in order to optimize usage of limited resources. In high-tech industries, the manufacturing system selection is extremely difficult because of the complex features and structures of their products. Generally, selecting the best manufacturing system of high-tech products is a multiple-criteria decision-making (MCDM) problem. Graph ranking method is one of the most used techniques among MCDM methods, which is originated from combinatorial mathematics. Simple computational procedure, ability to consider relationships between criteria, etc., are some perfect characteristics of this method for modeling and solving decision-making problems with complexity. Therefore, this study attempted to determine the most suitable manufacturing system in high-tech industries using graph ranking method. Moreover, because of vagueness and imprecision in human judgments, fuzzy set theory is utilized in the evaluation procedure. The suggested approach was used to select the most appropriate system for LCD manufacturing at Sanam Electronic Company. Finally, Obtained results indicated the efficiency of the proposed approach and selection of a cloud-based manufacturing system as the most suitable manufacturing system in high-tech industries.

     

A web-based high-performance multicriteria decision support system for medical diagnosis

Multicriteria decision making (MCDM) methods can be powerful aids for evaluating patients' medical information in medical diagnostic systems. Technique ordered preference by similarity to the ideal solution (TOPSIS) is one of the more widely used MCDM methods in decision support systems. For the purpose of this work, the TOPSIS method is modified into a more suitable form and used for the implementation of a web-based medical diagnostic system. In our modified TOPSIS method, we have utilized fuzzy logic so that users can more accurately describe their symptoms. The data given to the modified TOPSIS method are often massive in proportions and may take a considerable amount of time to generate a ranking of alternatives. TOPSIS lends itself to parallel computation because it is virtually a combination of matrix computations. Therefore, computer parallelism is implemented so that a large amount of input data can be handled simultaneously, hence decreasing overall execution time. In addition, to make our MCDM system more accessible, we have designed our system to be web based. The web-based medical diagnosis system includes a dynamically generated web-based user interface, while the parallel implementation of the modified TOPSIS component, in conjunction with the Common Gateway Interface, forms the back end of the system. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1083–1099, 2007.     

Building a qualitative recruitment system via SVM with MCDM approach

Advances in information technology have led to behavioral changes in people and submission of curriculum vitae (CV) via the Internet has become an often-seen phenomenon. Without any technological support for the filtering process, recruitment can be difficult. In this research, a method combining five-factor personality inventory, support vector machine (SVM), and multi-criteria decision-making (MCDM) method was proposed to improve the quality of recruiting appropriate candidates. The online questionnaire personality testing developed by the International Personality Item Pool (IPIP) was utilized to identify the personal traits of candidates and both SVM and MCDM were employed to predict and support the decision of personnel choice. SVM was utilized to predict the fitness of candidates, while MCDM was employed to estimate the performance for a job placement. The results show the proposed system provides a qualified matching according to the results collected from enterprise managers.     

Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem

Evaluating and selecting a suitable supplier is a complex problem which involves a number of different criteria. In literature, there are various multi-criteria decision making (MCDM) methods available with their own characteristic features. The focus of this study is intuitionistic fuzzy (IF) MCDM methods which have attracted much attention from academics and practitioners in recent years. IF sets are widely used to tackle imprecise and uncertain decision information in decision making due to their capability of accommodating the hesitation in human decision processes. This study proposes a new integrated methodology that is used for the first time in the literature. This approach consists of intuitionistic fuzzy analytic hierarchy process (IFAHP), an MCDM technique, for determining the weights of supplier evaluation criteria, and the concept of intuitionistic fuzzy axiomatic design (IFAD) principles for ranking competing supplier alternatives with respect to their overall performance. Decision makers’ assessments and opinions are extended to the IF environment in this approach and furthermore, the group decision making (GDM) approach is utilized in order to overcome uncertainties and vagueness, minimize the partiality of decision process and to avoid bias. This study contributes to supplier selection and IF sets literature by providing a combined framework based on IFAHP and IFAD methodology for the first time. To assess the validity of the proposed integrated IF MCDM approach, a case study from Turkey is provided. This study can be useful to researchers in better understanding the supplier selection problem theoretically, as well as to organizations in designing better satisfying supplier evaluation systems.     

Production planning & scheduling: Computational feasibility of multi-criteria models of production, planning and scheduling

This paper surveys the applications of multi-criteria decision making (MCDM) methods to production planning, scheduling, and sequencing problems. The basic structure of the decision models are described by their objectives and the resulting models are classified by decision variables into the areas of Aggregate Production Planning, Disaggregate Production Planning, Production Scheduling, and Single Machine Sequencing. The problem sizes that have been solved are summarized to determine how practical it is to use MCDM.     

Combining prospect theory and fuzzy numbers to multi-criteria decision making

Many multi-criteria decision making (MCDM) methods have been proposed to handle uncertain decision making problems. Most of them are based on fuzzy numbers and they are not able to cope with risk in decision making. In recent years, some MCDM methods based on prospect theory to handle risk MCDM problems have been developed. In this paper, we propose a hybrid approach combining prospect theory and fuzzy numbers to handle risk and uncertainty in MCDM problems. So, it is possible to tackle more challenging MCDM problems. A case study involving oil spill in the sea illustrates the application of the novel method.     

A new method selection approach for fuzzy group multicriteria decision making

Fuzzy multicriteria decision making (MCDM) has been widely used in ranking a finite number of decision alternatives characterized by fuzzy assessments with respect to multiple criteria. In group decision settings, different fuzzy group MCDM methods often produce inconsistent ranking outcomes for the same problem. To address the ranking inconsistency problem in fuzzy group MCDM, this paper develops a new method selection approach for selecting a fuzzy group MCDM method that produces the most preferred group ranking outcome for a given problem. Based on two group averaging methods, three aggregation procedures and three defuzzification methods, 18 fuzzy group MCDM methods are developed as an illustration to solve the general fuzzy MCDM problem that requires cardinal ranking of the decision alternatives. The approach selects the group ranking outcome of a fuzzy MCDM method which has the highest consistency degree with its corresponding ranking outcomes of individual decision makers. An empirical study on the green bus fuel technology selection problem is used to illustrate how the approach works. The approach is applicable to large-scale group multicriteria decision problems where inconsistent ranking outcomes often exist between different fuzzy MCDM methods.     

Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems

The key to achieving optimum ship system reliability and safety is to have a sound maintenance management system in place for mitigating or eliminating equipment/component failures. Maintenance has three key elements; risk assessment, maintenance strategy selection and the process of determining the optimal interval for the maintenance task. The optimisation of these three main elements of maintenance is what constitute a sound maintenance management system. One of the challenges that marine maintenance practitioners are faced with is the problem of maintenance selection for each equipment item of the ship machinery system. The decision making process involves utilising different conflicting decision criteria in selecting the optimum maintenance strategy from among multiple maintenance alternatives. In tackling such decision making problems the application of a multi-criteria decision making (MCDM) method is appropriate. Hence in this paper two hybrid MCDM methods; Delphi-AHP and Delphi-AHP-PROMETHEE, are presented for the selection of appropriate maintenance strategies for ship machinery systems and other related ship systems. A case study of a ship machinery system maintenance strategy selection problem is used to demonstrate the suitability of the proposed methods.     

Risk and confidence analysis for fuzzy multicriteria decision making

Recent research has recognised that multicriteria decision making (MCDM) should take account of uncertainty, risk and confidence. This paper takes this research forward by using linguistic variables and triangular fuzzy numbers to model the decision maker’s (DM) risk and confidence attitudes in order to define a more complete MCDM solution. To illustrate the computation process and demonstrate the feasibility of the results we use a travel problem that has been used previously to assess MCDM techniques. The results show that the method is useful for tackling imprecision and subjectivity in complex, ill-defined and human-oriented decision problems.     

An expert system for multi-criteria decision making using Dempster Shafer theory

This paper outlines a new software system we have developed that utilises the newly developed method (DS/AHP) which combines aspects of the Analytic Hierarchy Process (AHP) with Dempster–Shafer Theory for the purpose of multi-criteria decision making (MCDM). The method allows a decision maker considerably greater level of control (compared with conventional AHP methods) on the judgements made in identifying levels of favouritism towards groups of decision alternatives. More specifically, the DS/AHP analysis allows for additional analysis, including levels of uncertainty and conflict in the decisions made, for example. In this paper an expert system is introduced which enables the application of DS/AHP to MCDM. The expert system illustrates further the usability of DS/AHP, also including new aspects of analysis and representation offered through using this method. The principal application used to illustrate this expert system is that of identifying those residential properties to visit (view), from those advertised for ales through a real estate brokerage firm.     

A model based on 2-tuple fuzzy linguistic representation and Analytic Hierarchy Process for supplier segmentation using qualitative and quantitative criteria

The literature on supply base segmentation has increasingly adopted multi-criteria decision making (MCDM) techniques into recently proposed models. However, most proposals segment the supply base from the standpoint of the purchased item, which prevents them from providing guidelines that are specific to each supplier. Some authors have attempted to overcome these limitations by putting forward portfolio models based on the relationship with suppliers. These approaches use fuzzy variables and MCDM methods that take qualitative judgements by experts as the only input for decision making. However, many companies have databases with historical data about the performance of past transactions with suppliers that should be considered by expert systems that aim to comprehensively evaluate suppliers’ performance. This paper seeks to address this gap by proposing a segmentation model based on the relationship with suppliers capable of aggregating quantitative and qualitative criteria. Analytic Hierarchy Process (AHP) was used to determine the relative importance of each criteria. Fuzzy 2-tuple, a prominent computing with word (CWW) approach, was used to evaluate suppliers with a mixture of historical quantitative data and qualitative judgements by purchasing experts. An illustrative application of the proposed model was carried out in the pharmaceutical supply center (PSC) of a teaching hospital. The proposed model can be viewed as a decision support system capable of aggregating the qualitative judgements of experts and quantitative historical performance measures, thus providing guidelines to improve the relationship between suppliers and the buyer firm.     

A new flexible and reliable interval valued fuzzy VIKOR method based on uncertainty risk reduction in decision making process: An application for determining a suitable location for digging some pits for municipal wet waste landfill

Facility location selection problem is one of the challenging and famous kinds of MCDM problems including both quantitative and qualitative criteria. For each Multiple Criteria Decision Making (MCDM) problem, when the ratings of alternatives with respect to the criteria and/or the values of criteria’s weights are presented by Interval Valued Fuzzy Numbers (IVFNs), the conventional fuzzy MCDM methods (Type-1 fuzzy MCDM methods) tend to be less effective. Therefore, the IVF-MCDM (Interval Valued Fuzzy MCDM) methods should be applied for solving such fuzzy MCDM problems. In this paper, we propose an IVF-VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method based on uncertainty risk reduction in decision making process. By using such method, the reliability of the captured decisions in an IVF decision making problem is significantly increased. The proposed method is applied for solving two numerical examples that the former of which is a real application problem related to selecting a suitable location for digging some pits for municipal wet waste landfill in one of the largest cities in Iran. The second numerical example is presented with an aim of comparing our method with the two other IVF-MCDM methods. As a result, we found out the proposed method is reliable and practical for the facility location selection problems and other MCDM problems. Moreover, the proposed method has a considerable accuracy and is flexible and easy to use.     

Comparison of three modern multicriteria decision-making tools

The requirements, conditions and values encompassing a decision-making situation determine the decision maker's feasible alternative solutions and circumscribe the best solution. Various approaches have been suggested in the literature to identify the best solution for a class of problems called multicriteria decision making (MCDM). MCDM problems may be deterministic or non-deterministic. These approaches employ different methodologies and usually produce dissimilar solutions. Therefore, it is worthwhile to examine and compare the available MCDM methods. This paper focuses mainly on deterministic MCDM situations and methods and only a brief reference is made to their non-deterministic counterparts. We introduce a new performance measurement method called operational competitiveness rating (OCRA) and discuss its use as a deterministic MCDM tool. We demonstrate OCRA's application to a process selection problem that is adapted from an actual situation that involves qualitative data. W e compare the results obtained by OCRA with the results of analytic hierarchy process and data envelopment analysis to understand their similarities and differences. The comparison of these three non-parametric performance measurement tools provides some useful insights into their behaviour in actual MCDM situations.     

Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies

Using the balanced scorecard approach based on sustainable development parameters is a powerful and useful methodology to evaluate the sustainable performance of organization or company. In this paper, a new approach based on sustainability balanced scorecard (SBSC) and multi criteria decision making (MCDM) approaches is developed for evaluating the performance of oil producing companies in Iran. For reflecting the interdependent relationships among factors influencing the problem under consideration, analytical network process (ANP), a branch of the MCDM techniques, is employed. However, using the ANP method for calculating the preference ratings of alternatives is a time-consuming and bothersome process; therefore, COPRAS (COmplex PRoportional ASsessment) technique is adopted to prioritize the feasible alternatives in terms of linguistic variables. Based on this study, the results demonstrate the effectiveness of the proposed model. The performance evaluation model proposed by using a combination of the MCDM methods and the SBSC approach helps authorities to make an attempt for achieving a competitive advantage.     

Pythagorean fuzzy VIKOR approaches based on TODIM for evaluating internet banking website quality of Ghanaian banking industry

With the rapid development of Internet banking technology in Ghana, the website quality evaluation is the essential core of the customer, which is regarded as a multi-criteria decision making (MCDM) problem. Due to the uncertainty of Internet banking, the evaluation of criteria may be measured by Pythagorean fuzzy numbers (PFNs). In addition, the customer usually does not exhibit complete rationality in the decision procedure. Based on the traditional VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) method of MCDM, this paper provides a new perspective of a compromised solution, which can handle the decision maker’s psychological behavior by inducing TODIM (a Portuguese acronym meaning Interactive Multi-Criteria Decision Making). By defining Pythagorean fuzzy entropy and cross-entropy measures, we study the determination of the weights of the criteria in advance. Then, considering the psychological behavior of the customer, we design two types of strategies for the combination between TODIM and VIKOR. Meanwhile, the corresponding methods have been developed, i.e., Approaches I and II. After that, a simulated example of ranking Internet banking websites in the Ghanaian banking industry is given to illustrate the validity and applicability of our proposed approaches. Finally, we utilize the Wilcoxon signed-rank test and then discuss the differences among VIKOR, TODIM and our proposed methods.     

Personnel selection based on computing with words and fuzzy MULTIMOORA

Personnel selection process is aimed at choosing the best candidate to fill the defined vacancy in a company. It determines the input quality of personnel and thus plays an important role in human resource management. Given the uncertain, ambiguous, and vague nature of personnel selection process, it requires the application of multi-criteria decision making (MCDM) methods for robust recruitment. This paper hence is aimed at extending the fuzzy MULTIMOORA for linguistic reasoning under group decision making. The fuzzy MULTIMOORA was further modified in this study. The fuzzy MULTIMOORA for group decision making (MULTIMOORA–FG) enables to aggregate subjective assessments of the decision-makers and thus offer an opportunity to perform more robust personnel selection procedures. A personnel selection problem illustrated the group decision-making procedure according to MULTIMOORA–FG: the enterprise has formed an executive committee consisting of four decision-makers to choose the best candidate from another four participants to fill the vacancy. The committee has decided to consider eight qualitative attributes expressed in linguistic variables. The numerical example exhibited possibilities for improvement of human resources management as well as any other business decision area by applying MULTIMOORA–FG.     

Extension of the ELECTRE method based on interval-valued fuzzy sets

Decision-making is the process of finding the best option among the feasible alternatives. In classical multiple criteria decision-making (MCDM) methods, the ratings and the weights of the criteria are known precisely. However, if decision makers cannot reach an agreement on the method of defining linguistic variables based on the fuzzy sets, the interval-valued fuzzy set theory can provide a more accurate modeling. In this paper, the interval-valued fuzzy ELECTRE method is presented aiming at solving MCDM problems in which the weights of criteria are unequal, using interval-valued fuzzy set concepts. For the purpose of proving the validity of the proposed model, we present a numerical example and build a practical maintenance strategy selection problem.     

A fuzzy Full Consistency Method-Dombi-Bonferroni model for prioritizing transportation demand management measures

The selection and prioritization of appropriate Transportation Demand Management (TDM) measures is a common problem faced by transport planners and decision makers. The problem involves many uncertainties due to changing economic conditions, uncertainty in project success, changes in mobility and population characteristics etc. In this study, the multi-criteria decision making (MCDM) based fuzzy Full Consistency Method-Dombi-Bonferroni (fuzzy FUCOM-D’Bonferroni) model is proposed for a case study in Istanbul’s urban mobility system. Istanbul’s historical peninsula is considered to be a pilot area for the implementation of TDM projects by the local government. The proposed model is compared with other well-known four MCDM methods in order to show its validity and consistency. The results show that public transport capacity improvements is the best alternative among the other TDM measures.     

Soft computing based on interval valued fuzzy ANP-A novel methodology

Analytic Network Process (ANP) is the multi-criteria decision making (MCDM) tool which takes into account such a complex relationship among parameters. In this paper, we develop the interval-valued fuzzy ANP (IVF-ANP) to solve MCDM problems since it allows interdependent influences specified in the model and generalizes on the supermatrix approach. Furthermore, performance rating values as well as the weights of criteria are linguistics terms which can be expressed in IVF numbers (IVFN). Moreover, we present a novel methodology proposed for solving MCDM problems. In proposed methodology by applying IVF-ANP method determined weights of criteria. Then, we appraise the performance of alternatives against criteria via linguistic variables which are expressed as triangular interval-valued fuzzy numbers. Afterward, by utilizing IVF-weights which are obtained from IVF-ANP and applying IVF-TOPSIS and IVF-VIKOR methods achieve final rank for alternatives. Additionally, to demonstrate the procedural implementation of the proposed model and its effectiveness, we apply it on a case study regarding to assessment the performance of property responsibility insurance companies.     

Linguistic Discriminative Aggregation in Multicriteria Decision Making

Several new aggregation operators are proposed in the context of multicriteria decision making (MCDM) in the linguistic domain. The proposed operators first infer the discrimination index, based on the extent of variability in the various linguistic evaluations against a criterion. This value is then utilized in the actual aggregation step to discriminate among the alternatives. Besides, the proposed operators also take into account the a priori weights associated with the criteria. The proposed concepts are illustrated through an example in group MCDM.