Multiple Attribute Decision Making Methods Research Articles

A Novel Method for Multiple Attribute Decision-Making of Continuous Random Variable under Risk with Attribute Weight Unknown

The extension of the fuzzy TOPSIS method based on the combination weight is presented to deal with multiple attribute decision-making problems under risk where the attribute value takes the form of the continuous random variable on the bounded intervals. First, the risk decision matrix is normalized by the transformation of the density function, and the variation coefficient method is used to determine the objective weights based on the expectations of the random variables. Subsequently, according to the maximizing rule of the weighted synthetic value of alternatives, the synthetic weight model is established. Then, the ideal solution and negative ideal solution is defined, the distances between the alternatives and the ideal/negative ideal solutions, and the relative closeness coefficients are calculated. In addition, the alternatives are ranked by the relative closeness coefficient of the alternatives. Finally, an illustrative example with the interval number is given to demonstrate the steps and the effectiveness of the proposed method.

Novel method for decision making in the manufacturing environment

Decision makers in the manufacturing sector frequently face the problem of assessing a wide range of alternative options and selecting one based on a set of conflicting attributes. There is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelationships and in making right decisions. This paper proposes a novel multiple-attribute decision-making (MADM) method for solving the deterministic decision-making problems of the manufacturing environment. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into quantitative attributes. Three examples are presented to illustrate the potential of the proposed method.

Selecting an Appropriate Change Management Model for Iranian Academic Libraries Using MADM Methods

Today's life is not insulated from different kinds of “change.” Change is also experienced in organizations as a somehow daily process. Academic libraries are organizations which need to be active in confrontation with “change” because of their essential role in education and research. These libraries could be more successful in meeting users' information needs, if they manage change in a planned manner. In this regard, Iranian academic libraries are not exceptional. They are faced with pressures from their internal and external environments, which make it essential for them to respond and implement necessary changes. In order to assist these libraries in overcoming challenges stemming from change and improving the change management capacity in libraries, this article aims to select an appropriate change management model for Iranian academic libraries. After reviewing the literature, finding and selecting basic change models including Kotter's Change Model, Penfold's Information Services Framework for Managing Change, and Curzon's Cycle of Change, a research questionnaire was designed and answered by a group of experts using the consensus method. The results were analyzed by Multiple Attribute Decision Making (MADM) techniques including Technique for Order- Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting Method (SAWM). Penfold's Information Services Framework for Managing Change received the highest scores from the application of both techniques. It is concluded that Penfold's Information Services Framework for Managing Change could be applied as an appropriate change model in the context of Iranian academic libraries.

Method for Multiple Attribute Decision-Making with Continuous Random Variable under Risk Based on Projection Model

A rank approach based on projection model is proposed to deal with multiple attribute decision-making[MADM] problems under risk and with attribute value as continuous random variable on bounded intervals. Firstly, risk decision matrix is normalized by density function, and weights of attributes are calculated based on exception value of random variable by using projection pursuit model and genetic algorithm. Next, through calculating weighted correlation coefficients between alternatives and ideal solutions, weighted grey correlation projection models on ideal solutions are developed by grey correlation projection method for every alternative. Furthermore, alternatives are ranked by grey correlation projection value. Finally, an MADM example with interval numbers is provided to demonstrate the steps and effectiveness of the proposed approach.

Deriving preference order of open pit mines equipment through MADM methods: Application of modified VIKOR method

In multiple attribute decision making (MADM) problem, a decision maker (DM) has to choose the best alternative that satisfies the evaluation criteria among a set of candidate solutions. It is generally hard to find an alternative that meets all the criteria simultaneously, so a good compromise solution is preferred. The VIKOR method was developed for multi-criteria optimization of complex systems. This method focuses on ranking and selecting from a set of alternatives in the presence of conflicting criteria. It introduces the multi-criteria ranking index based on the particular measure of “closeness” to the “ideal” solution. To deal with the uncertainty and vagueness from humans’ subjective perception and experience in decision process, this paper presents an evaluation model based on deterministic data, fuzzy numbers, interval numbers and linguistic terms. Combination of analytic hierarchy process (AHP) and entropy method was applied for attribute weighting in this proposed MADM method. To demonstrate the potential of the methodology, the proposed method is used for surface mine equipment selection problems.

Mobility management in ubiquitous environments

Ubiquitous computing is aiming at providing users with intelligent human-centric context-aware services at anytime anywhere. However, mobility increases dynamism and uncertainty conditions. This study therefore explores the management and uses of various contexts for automatically providing appropriate services to individual users. This issue is explored from an open framework perspective referred to as ubiquitous gate (U-gate). In this framework, a distributed context management architecture and a communication model based on standard protocols are proposed. To fit user requirements and to achieve complete mobility management, a context-aware path planning mechanism (UbiPaPaGo) and a context-aware handoff mechanism (UbiHandoff) are proposed based on context stored in an open and distributed context management server U-gate. Based on the path planning results of UbiPaPaGo, UbiHandoff derives a minimum access point (AP) handoff plan that satisfies multiple QoS requirements for individual users and services. The UbiHandoff mechanism includes multiple-attribute decision making method (MADM)---based handoff planning, referred to as MADM-based UbiHandoff, and genetic algorithm (GA)---based handoff planning, referred to as GA-based UbiHandoff. In the proposed MADM-based UbiHandoff, analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) provide efficient and seamless AP handoff to gain higher QoS performance. In GA-based UbiHandoff, genetic algorithm is employed to minimize handoff by finding appropriate APs along the path under QoS constraints. Finally, the effectiveness of the proposed mechanisms is evaluated through simulations. Numerical results demonstrate that both mechanisms minimize handoffs and ensure compliance with QoS requirements.

A subjective and objective integrated multiple attribute decision making method for material selection

The selection of an optimal material for an engineering design from among two or more alternative materials on the basis of two or more attributes is a multiple attribute decision making (MADM) problem. The selection decisions are complex, as material selection is more challenging today. There is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelations and in making right decisions. This paper proposes a novel MADM method for material selection for a considered design problem. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. Three examples are presented to illustrate the potential of the proposed method.

Application of Multiple Attribute Decision-Making (MADM) and Analytical Hierarchy Process (AHP) Methods in the Selection Decisions for a Container Yard Operating System

AbstractThis study examines and evaluates important parameters in container yard operating systems. The objective of this study is to set up a basis for decision-making to select the best scenario among other options. It examines the important attributes determined using a multiple attribute decision-making (MADM) method. The MADM method is suited to the study of complex problems and allows the consideration of qualitative attributes expressed in linguistics terms and quantitative attributes illustrated in financial and throughput measures in container terminals. An analytical hierarchy process (AHP) technique is employed for solving the MADM problems. The AHP and principal eigenvector weighting techniques have been proposed in this study as weighting tools because they allow decomposition of a decision problem into a hierarchical order and enable a pair-wise comparison of the attributes with an acceptable level of consistency. The analysis assures that both qualitative and quantitative aspects of the decision are incorporated into the process. The results obtained from this study may be used to develop a basis for pair-wise comparison, judgment and selection of the best container yard operation option for the purpose of this study. For the first time, this study proposes the application of MADM and AHP for selection decisions in container terminals.

A multiple attributes decision making method using individual and collaborative attribute data in a fuzzy environment

Although multiple attribute decision making (MADM) problems with both individual attribute data of a single alternative and collaborative attribute data of pairwise alternatives exist in the real world, they have seldom been a focus of research. This paper proposes a MADM method using individual and collaborative attribute data in a fuzzy environment, in which experts use linguistic variables to express their opinions. In the method, first, the evaluation matrix of individual attributes date and the judgment matrix of collaborative attributes data are constructed. Then, the central dominance of one alternative outranking other all alternatives is defined for aggregating the collaborative data. From this, an integrated decision matrix incorporating individual and collaborative attribute data is constructed. Further, based on an extended TOPSIS, the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS) are determined, and the relative closeness of each alternative to the FPIS and FNIS is calculated to determine the ranking order of all alternatives. Finally, two examples are used to illustrate the applicability of the proposed method.

An Extension of Multi-Response Optimization in MADM View

Multiresponse surface (MRS) is the useful way to satisfy more than one output characteristic. It’s originated from statistical approach to estimate relations between inputs and output variable called Response surface methodology (RSM). In this study we review important multiresponse optimization (MRO) methods and use multiple attribute decision making (MADM) methods such as VIKOR, PROMETHEE II, ELECTRE III and TOPSIS in converting multiresponse to single response in order to analyze the robust experimental design. Finally numerical results and comparison of MADM methods are shown.

Fuzzy quality attributes for evaluating Internet marketing system performance

Electronic commerce (EC) is an important issue both for research and in the real business world. In addition, the Internet is a new and rapidly growing marketing channel for companies developing B2B (Business to Business) or B2C (Business to Customers) electronic commerce. To obtain rewards from EC, dot-com companies should develop their marketing performance effectively to attract customers' attention toward purchasing goods and to provide satisfying service for customers on the Internet. But not all the dot-com companies are successful, many factors will affect the enterprise operation. Thus, the purpose of this paper is to establish a multiple attribute decision-making (MADM) framework for assessing and evaluating the marketing performance of Internet bookstore companies in Taiwan. Based on the hierarchical structure, a mixed MADM method with non-additive and additive fuzzy measurement is used to show the relative importance of quality attributes. Seven real cases are then used as illustrative the alternatives to demonstrate the synthesis decisions under the application of a mixed mode of both MADM methods. The study shows that the safety of the payment mechanism is the most important quality attribute to develop marketing performance. Furthermore, several practical implications and recommendations are also discussed.

Flow pattern recognition in tray columns with MADM (multiple attribute decision making) method

In studying the dynamic and steady behavior of distillation columns, identifying the liquid flow pattern is very important. The type of liquid flow pattern on each tray depends on design parameters such as column diameter and tray spacing and operating variables such as liquid and vapor flow rates. The innovative technique presented in this paper can help us to recognize the tray flow pattern type with MADM method. A case study is presented to illustrate the method.

Evaluation of alternatives applying TOPSIS method in a fuzzy environment

Abstract The paper analyses the problem of multiple attribute decision‐making (MADM) under fuzzy environment. In some cases the crisp value is inadequate to model real‐life situations. For this reason some fuzzy MADM methods have been developed. The extended TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) to fuzzy environment is presented in the current paper. Weights and ratings of each criterion are described in triangular fuzzy numbers. The relative closeness to the ideal solution of each alternative is calculated applying different approaches that were presented in different scientific papers. A computational experiment is presented to compare the results of a multiple attribute analysis that uses three modifications of fuzzy TOPSIS method in a particular situation.

EVALUATION OF ALTERNATIVES APPLYING TOPSIS METHOD IN A FUZZY ENVIRONMENT

The paper analyses the problem of multiple attribute decision‐making (MADM) under fuzzy environment. In some cases the crisp value is inadequate to model real‐life situations. For this reason some fuzzy MADM methods have been developed. The extended TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) to fuzzy environment is presented in the current paper. Weights and ratings of each criterion are described in triangular fuzzy numbers. The relative closeness to the ideal solution of each alternative is calculated applying different approaches that were presented in different scientific papers. A computational experiment is presented to compare the results of a multiple attribute analysis that uses three modifications of fuzzy TOPSIS method in a particular situation.

A method using weight restrictions in data envelopment analysis for ranking and validity issues in decision making

In this article we introduce a comprehensive yet efficient approach based on data envelopment analysis (DEA) with restricted multipliers for accountable and understandable multiple attribute decision making (MADM). Information system (IS) appraisals are motivated and used for illustrating the proposed methodology. Results show that the given DEA based approach can easily and significantly increase the information frame of the decision maker by identifying disparate rankings and by affirming the stability and validity of ranking outcomes. The given validity concept is contrary to the directions given in the main body of research and can also be used to question ranking outcomes of classic MADM methods.

Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method

The simulation model is a proven tool in solving nonlinear and stochastic problems and allows examination of the likely behavior of a proposed manufacturing system under selected conditions. However, it does not provide a method for optimization. A practical problem often embodies many characteristics of a multiresponse optimization problem. The present paper proposes to solve the multiresponse simulation-optimization problem by a multiple-attribute decision-making method—a technique for order preference by similarity to ideal solution (TOPSIS). The method assumes that the control factors have discrete values and that each control factor has exactly three control levels. Taguchi quality-loss functions are adapted to model the factor mean and variance effects. TOPSIS is then used to find the surrogate objective function for the multiple responses. The present paper predicts the system performances for any combination of levels of the control factors by using the main effects of the control factors according to the principles of a robust design method. The optimal design can then be obtained. A practical case study from an integrated-circuit packaging company illustrates the efficiency and effectiveness of the proposed method. Finally, constraints of the proposed method are addressed.

An Approach for Solving Fuzzy MADM Problems

An approach using defuzzifying methods is proposed for the fuzzy multiple attribute decision-making (MADM) problems. The computing effectiveness of the proposed defuzzifying methods combined with the simple additive weighting (SAW) method and the technique for order preference by similarity to ideal solution (TOPSIS) method are evaluated based on a comparison to the improved fuzzy weighted average (IFWA) followed by a ranking method. Both SAW and TOPSIS methods are two of classic MADM methods. The purpose of this application is to make the method easier to program and data easier to manipulate. This results in a more practical method for fuzzy decisions. A numerical example and experiment are discussed to demonstrate the implementation of the methods in different input conditions.

A general multi-level evaluation process for hybrid MADM with uncertainty

Based on an evidential reasoning framework, a general multilevel evaluation process is developed in this paper for dealing with a multiple attribute decision making (MADM) problem with both quantitative and qualitative attributes. In this new process, a qualitative attribute may be evaluated by uncertain subjective judgments through multiple levels of factors and each of the judgments may be assigned by single or multiple experts in any rational way within the evidential reasoning framework. The qualitative attributes can then be quantified by means of general evaluation analysis and evidential reasoning. A few evaluation analysis models and the corresponding evidential reasoning algorithms are explored for parallel combination and hierarchical propagation of factor evaluations. With all the qualitative attributes being quantified by this rational process, the MADM problem represented by an extended decision matrix is then transformed into an ordinary decision matrix, which can be dealt with using a traditional MADM method. This new general evaluation process and the hybrid decision making procedure are demonstrated using a multiple attribute motor cycle evaluation problem with uncertainty. >

An evidential reasoning approach for multiple-attribute decision making with uncertainty

A new evidential reasoning based approach is proposed that may be used to deal with uncertain decision knowledge in multiple-attribute decision making (MADM) problems with both quantitative and qualitative attributes. This approach is based on an evaluation analysis model and the evidence combination rule of the Dempster-Shafer theory. It is akin to a preference modeling approach, comprising an evidential reasoning framework for evaluation and quantification of qualitative attributes. Two operational algorithms have been developed within this approach for combining multiple uncertain subjective judgments. Based on this approach and a traditional MADM method, a decision making procedure is proposed to rank alternatives in MADM problems with uncertainty. A numerical example is discussed to demonstrate the implementation of the proposed approach. A multiple-attribute motor cycle evaluation problem is then presented to illustrate the hybrid decision making procedure.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Computer aided robot selection: the ‘multiple attribute decision making’ approach

Abstract This paper presents an efficient approach for a computer-based solution to the problem of selection of an ‘optimum robot’ specifically to aid industries. The approach breaks new ground by applying Multiple Attribute Decision Making (MADM) ideology to equipment selection (robots) which is of consequence because selection is an implicit aspect of design. Indeed, the approach is also applicable to a robot design problem. The robot selection procedure allows rapid convergence from a very large number to a manageable shortlist of potentially suitable robots using an ‘elimination search’ routine based on a few pertinent attributes of the robots. Subsequently, the selection procedure proceeds to rank the alternatives in the shortlist by employing a MADM method termed TOPS1S (Technique for Order Preference by Similarity to Ideal Solution). An expert system has been developed as part of the software package to assist an inexperienced user to establish priorities, and to ‘oversee’ the selection process at various stages. The procedure is also illustrated by an example.