Multiple Criteria Decision Making Approach Research Articles

Providing hierarchical approach for measuring supply chain performance using AHP and DEMATEL methodologies

Article history: Received 15 April 2010 Received in revised form 15 May 2010 Accepted 16 May 2010 Available online 17 May 2010 Measuring the performance of a supply chain (SC) is normally of a function of various parameters. Such a problem often involves in a multiple criteria decision making (MCDM) problem where different criteria need to be defined and calculated, properly. During the past two decades, Analytical hierarchy procedure (AHP) and DEMATEL have been some of the most popular MCDM approaches for prioritizing various attributes. The study of this paper uses a new methodology which is a combination of AHP and DEMATEL to rank various parameters affecting the performance of the supply chain. The DEMATEL is used for understanding the relationship between comparison metrics and AHP is used for the integration to provide a value for the overall performance. © 2010 Growing Science Ltd. All rights reserved.

Fuzzy MCDM approach to R&D project evaluation in Taiwan's public sectors

PurposeEstablishing a performance‐oriented evaluation in public sectors is the key to successful administrations. However, because of lacking relative comparable measuring standards, it is difficult to measure the relative performance of one unit while comparing to other units with regard to the multiple criteria decision making (MCDM) of performance evaluation. This paper aims to focus on the performance ranking of research and development (R&D) projects in Taiwan's public sectors.Design/methodology/approachThe algorithm in this paper is based on the concept of fuzzy set theory and the hierarchical structure analysis. The analyzing method adopts the methods of standard normal distribution, linear transformation, and fuzzy MCDM, carrying on the analysis of multiple criteria of the performance evaluation.FindingsThis paper constructs linguistic values to the subjective judgments and analyzes the ranking results of the performance evaluation with respect to 45 R&D projects of one of Taiwan's electric power companies. Thus, the paper demonstrates a successful way of evaluating R&D projects in the public sector.Originality/valueIn this paper, a decision algorithm based on the fuzzy set theory is proposed to solve the performance evaluation of R&D projects in public sectors. In order to solve the difficulties of measuring one unit of the relative performance of quantitative criteria comparing to the other units, the method of standard normal distribution is adopted while measuring the quantitative criteria. The concept of linguistic values and fuzzy numbers are used in this paper since they could easily be used to describe the subjective measurement of the appropriateness of alternatives and the importance weightings of criteria.

Trade credit evaluation for Taiwan's broadband communications equipment manufacturers

Selecting trade credit instruments has profound impacts not only on the sellers/exporters but also on the buyers/importers. In this paper, we propose a fuzzy multiple-criteria-decision-making (MCDM) structure to trade credit evaluation. By considering the telegraphic transfer (T/T) advance, the letter of credit (L/C), the documentary collection, and the open account (O/A), a fuzzy MCDM approach is applied for the first time to investigate the trade credit offer. We use Taiwan's broadband communications equipment manufacturers in business in 2007 as an example. The results of our investigations differ according to three scenarios. This study provides an aggregate, comprehensive, and scientific framework on trade credit evaluation. This framework not only includes three objectives (risk, cost, and firm policy), but also four alternatives (T/T advance, LC, documentary collection and O/A) for the decision maker to use when considering strategic trade credit offers.

A novel hybrid MCDM approach for outsourcing vendor selection: A case study for a semiconductor company in Taiwan

Outsourcing manufacturing is a trend in today’s global business operations. Vendor selection is an essential factor affecting outsourcing operations performance and has been determined as a multiple criteria decision making (MCDM) problem. The purpose of this paper is to propose a novel hybrid MCDM technique to cope with the complex and interactive vendor evaluation and selection problem which can determine the structural relationships and the interrelationships amongst all the evaluation’s dimensions and support the Analytic Network Process (ANP) method to arrange appropriate weightings to each dimension and criterion in the evaluation model by summarizing the opinions of the experts. Finally, the overall performance ranking of all alternatives can be obtained to assist the decision making. Taiwan semiconductor industry is the largest provider in worldwide market. It is vertically disintegrated and outsourcing is a main stream practice. Wafer-testing is a critical manufacturing step in the semiconductor supply chain that distinguishes whether the IC can meet the specifications. Consequently, it’s crucial to establish a thorough model for selecting the wafer-testing vendors in order to achieve the success of the outsourcing operation. A case study of a Taiwan semiconductor company in selecting its wafer-testing outsourcing vendor by using the proposed MCDM technique is demonstrated to enhance the decision making quality. The results and proposed solution can be referred to by not only the semiconductor companies, but also other industries so that they can improve the vendor selection process in order to achieve a higher performance and satisfaction level.

An integrated fuzzy multi-criteria approach for the performance evaluation of multiple manufacturing plants

The performance evaluation is regarded as a multiple criteria decision making (MCDM) problem and has a significant impact on the operations of the enterprise. This paper develops an integrated MCDM approach that combines the voting method and the fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method to evaluate the performance of multiple manufacturing plants in a fuzzy environment. Fuzzy TOPSIS helps decision-makers carry out analysis and comparisons in ranking their preference of the alternatives with vague or imprecise data. Since the evaluation result is often greatly affected by the weights used in the evaluation process, the voting method is used in this study to determine the appropriate criteria weights. A case study demonstrating the applicability of the proposed model is presented. The case company is the world’s largest manufacturer of power supplies. It has three primary manufacturing bases located in Wujiang, Dongguan, and Tianjin, China. The proposed approach is used to evaluate the performance of the company’s five manufacturing plants in Wujiang, which produce switch power, telecom power, DC/DC converters, uninterruptible power systems (UPS) and AC/DC adapters.

Multi-Criteria Decision-Making Selection Model with Application to Chemical Engineering Management Decisions

Chemical industry project management involves complex decision making situations that require discerning abilities and methods to make sound decisions. Chemical engineers as project managers are faced with decision environments and problems in chemical industry projects that are complex. Multiple-criteria decision making (MCDM) approaches are major parts of decision theory and analysis. This paper presents all of MCDM approaches for use in chemical engineering management decisions. In this work, case study is Research and Development (R&D) project selection in chemical industry. The ability to make sound decisions is very important to success of R&D projects. It is hoped that this work will provide a ready reference on MCDM and this will encourage the application of the MCDM in chemical engineering management.

Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education

Increasing numbers of Taiwanese higher education institutes are pursuing innovation operation. However, these institutes generally rely greatly on academic research to evaluate innovation performance. Nevertheless, the performance of innovation may be affected by numerous factors that are often beyond the scope of a single academic study. Thus, to address this concern, this paper constructs an innovation support system (ISS) for Taiwanese higher education institutes to comprehensively evaluate their innovation performance. Previous research often evaluates performance by independently considering a number of criteria. However, this assumption of independence does not model the so-called “real world”; thus, we present a novel conjunctive multiple criteria decision-making (MCDM) approach that addresses dependent relationships among each measurement criteria. As such, we utilize a decision-making trial and evaluation laboratory (DEMATEL), a fuzzy analytical network process (FANP), and a technique for order preference by similarity to an ideal solution (TOPSIS) forming order to develop an innovation support system (ISS) that considers the interdependence and the relative weights of each measurement criterion.

Eliciting Customer Preferences for Products From Navigation Behavior on the Web: A Multicriteria Decision Approach With Implicit Feedback

The goal of raising customer loyalty in electronic commerce requires an emphasis on one-to-one marketing and personalized services. To this end, it is essential to understand individual customer preferences for products. In this paper, we present a method for identifying customer preferences and recommending the most appropriate product. The identification and recommendation of such products are all based on the use of customer's real-time web usage behavior, including activities such as viewing, basket placement, and purchasing of products. Therefore, in this approach, we do not force a customer to explicitly express his or her preference information for particular products but rather capture his or her preferences from data that result from such activities. Information on the web usage behavior for the products determines the ordinal relationships among the products, which express that certain product is preferred to other products across the multiple aspects. The ordinal relationships among the products and the multiple aspects of products lead to the consideration of a multiple-criteria decision-making approach. Thus, the problem eventually results in the identification of weights attached to the multiple criteria in the multidimensional preference space constructed by the ordinal relationships among the products. The derived weights are then used for the prioritization of products that are not included in the navigation behavior due to factors such as time pressure, cognitive burden, and the like.

In search of best alternatives: a TOPSIS driven MCDM procedure for neural network modeling

Multiple criteria decision making (MCDM) is an approach to rank the alternatives with respect to the different attributes. Several MCDM approaches were used to select the best alternatives of meta-heuristic modeling under the soft-computing domain where the true best alternative is not known. Alternatives are artificial neural network models, selection of which is difficult based on many conflicting performance measures. This paper addresses two new methods for MCDM, using the concept of Minkowski distance and based on technique for order preference by similarity to ideal solution philosophy. The performances of these two methods are compared with four other methods considering real-life data and simulated experiments.

Developing a fuzzy TOPSIS approach based on subjective weights and objective weights

Multiple criteria decision making (MCDM) is widely used in ranking one or more alternatives from a set of available alternatives with respect to multiple criteria. Inspired by MCDM to systematically evaluate alternatives under various criteria, we propose a new fuzzy TOPSIS for evaluating alternatives by integrating using subjective and objective weights. Most MCDM approaches consider only decision maker’s subjective weights. However, the end-user attitude can be a key factor. We propose a novel approach that involves end-user into the whole decision making process. In this proposed approach, the subjective weights assigned by decision makers (DM) are normalized into a comparable scale. In addition, we also adopt end-user ratings as an objective weight based on Shannon’s entropy theory. A closeness coefficient is defined to determine the ranking order of alternatives by calculating the distances to both ideal and negative-ideal solutions. A case study is performed showing how the propose method can be used for a software outsourcing problem. With our method, we provide decision makers more information to make more subtle decisions.

Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models

Two-sided assembly lines are especially used at the assembly of large-sized products, such as trucks and buses. In this type of a production line, both sides of the line are used in parallel. In practice, it may be necessary to optimize more than one conflicting objectives simultaneously to obtain effective and realistic solutions. This paper presents a mathematical model, a pre-emptive goal programming model for precise goals and a fuzzy goal programming model for imprecise goals for two-sided assembly line balancing. The mathematical model minimizes the number of mated-stations as the primary objective and it minimizes the number of stations as a secondary objective for a given cycle time. The zoning constraints are also considered in this model, and a set of test problems taken from literature is solved. The proposed goal programming models are the first multiple-criteria decision-making approaches for two-sided assembly line balancing problem with multiple objectives. The number of mated-stations, cycle time and the number of tasks assigned per station are considered as goals. An example problem is solved and a computational study is conducted to illustrate the flexibility and the efficiency of the proposed goal programming models. Based on the decision maker's preferences, the proposed models are capable of improving the value of goals.

Multiobjective combinatorial optimization: some approaches

AbstractThere have been many developments in multiple criteria decision‐making (MCDM) during the last 50 years. Researchers from different areas have also recognized the multiple‐criteria nature of problems in their application areas and tried to address these issues. Unfortunately, there has not always been sufficient information flow between the researchers in the MCDM area and the researchers applying MCDM to their problems. More recently, multiobjective combinatorial optimization (MOCO) and multiobjective metaheuristic areas have been enjoying substantial developments. These problems are hard to solve. Many researchers addressed the problem of finding all nondominated solutions. This is a difficult task for MOCO problems. This difficulty limits many of the studies to concentrate on bicriteria problems. In this paper, I review some MCDM approaches that aim to find only the preferred solutions of the decision maker (DM). I argue that this is especially important for MOCO problems. I discuss several of our approaches that incorporate DM's preferences into the solution process of MOCO problems and argue that there is a need for more work to be done in this area. Copyright © 2009 John Wiley & Sons, Ltd.

Flexible decision modeling of reverse logistics system: A value adding MCDM approach for alternative selection

The most favorable reverse manufacturing alternative arriving to collection centers has always been a key strategic consideration of any product recovery system. The nature of these decisions usually is considered to be multidimensional, interdisciplinary, complex, and unstructured due to lack of certainty in environment and information regarding time, quantity and quality of returns, etc. Fuzzy decision methodology provides an alternative framework to handle these reverse logistics system (RLS) complexities and to determine the decision strategies for best alternative selection for reprocessing. Designing a decision-making model for the same requires quantitative and qualitative evaluation based on criteria such as cost/time, legislative factors, environmental impact, quality, market, etc. Performance must be considered on the basis of these criteria to determine a suitable reverse manufacturing option depending on the expert opinion in this domain. In this paper, we propose a multiple criteria decision-making (MCDM) model based on fuzzy-set theory. The proposed model can help in designing effective and efficient flexible return policy depending on the various criteria. Further, companies can use this analysis as a strategic decision-making tool to develop fresh reprocessing facilities or efficiently use the already exiting facility. Finally, an example has been illustrated to highlight the procedural implementation of the proposed model. Further, this paper also makes an attempt to bring fuzzy-based flexible MCDM and reverse logistics together as a well-suited group decision support tool for alternative selections.

A hybrid fuzzy MCDM approach to machine tool selection

The selection of the appropriate machine tools for a manufacturing company is one of the important points to achieving high competitiveness in the market. Besides, an appropriate choice of machine tools is very important as it helps to realize full production quickly. Today’s market offers many more choices for machine tool alternatives. There are also many factors one should consider as part of the appropriate machine tool selection process, including productivity, flexibility, compatibility, safety, cost, etc. Consequently evaluation procedures involve several objectives and it is often necessary to compromise among possibly conflicting tangible and intangible factors. For these reasons, multiple criteria decision making (MCDM) has been found to be a useful approach to solve this kind of problem. Most of the MCDM models are basically mathematical and ignore qualitative and often subjective considerations. The use of fuzzy set theory allows incorporating qualitative and partially known information into the decision model. This paper describes a fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based methodology for evaluation and selection of vertical CNC machining centers for a manufacturing company in Istanbul, Turkey. The criteria weights are calculated by using the fuzzy AHP (analytical hierarchy process).

Selecting distribution centre location using an improved fuzzy MCDM approach

A distribution centre (DC) is usually supplied by manufacturing factories and in turn it supplies the consumers. To reduce transportation costs and enhance operation efficiency, selecting a suitable DC location has become a very important issue for both manufacturing and distribution industries. Recently, Chen [1] proposed a fuzzy MCDM (multiple criteria decision-making) approach to selecting the location of a distribution centre. Despite its merits, two limitations were found in his works, i.e. follows: (1) the final fuzzy evaluation value Pi was treated as a triangular fuzzy number. This is not correct because it may not produce a triangular shape and (2) the difference of two final fuzzy evaluation values, i.e. Pi−Pj, was regarded as a triangular fuzzy number. This is also incorrect. Obviously, Pi−Pj may not produce triangular shape because Pi and Pj may not be triangular fuzzy numbers. To resolve the above limitations and enhance the applicability of the fuzzy MCDM to the DC location selection problem, an improved fuzzy MCDM approach is proposed. In this work, the membership functions of Pi and Pi−Pj are developed to construct an improved fuzzy preference relation, which is further employed with the Chen ranking procedure to complete the proposed method. Finally, a numerical example demonstrates the feasibility of the proposed method.

An interval arithmetic based fuzzy MCDM approach for technology transfer strategy selection in biotechnology

This work suggests an interval arithmetic based fuzzy multiple criteria decision making (MCDM) approach for technology transfer strategy selection in biotechnology, where the ratings of various strategies versus different criteria and the importance weights of different criteria are assessed in fuzzy numbers and/or linguistic terms represented by fuzzy numbers. The membership function for the final fuzzy evaluation value of eac technology transfer strategy can be developed. The ranking method of an area between the centroid point and original point is applied to help complete the proposed model. Detailed analysis for the multiplication of two positive triangular fuzzy numbers is also presented. A numerical example demonstrates the computational process of the proposed method.

Evaluating sustainable fishing development strategies using fuzzy MCDM approach

In actual environmental investment for industry, the stakeholders are often required to evaluate the investment strategies according to their own subjective preferences in terms of numerical values from various criteria, such as economic effectiveness, technique feasibility and environmental regulation. Thus, this situation can be regarded as a fuzzy multiple criteria decision-making (MCDM) problem, so the fuzziness and uncertainty of subjective perception should be considered. This paper proposes an alternative approach, the non-additive fuzzy integral, to cope with evaluation of fuzzy MCDM problems particularly while there is dependence among considered criteria. To illustrate the proposed procedure, the sustainable development strategy for aquatic product processors in Taiwan is investigated. In this paper we employ triangular fuzzy numbers to represent the decision makers’ subjective preferences on the considered criteria, as well as for the criteria measurements to evaluate a sustainable development planning case for industry. Firstly, in this study we employ factor analysis to extract four independent common factors from those criteria. Secondly, we construct the evaluation frame using AHP composed of the above four common factors with twelve evaluated criteria, and then derive the relative weights with respect to considered criteria. Thirdly, the synthetic utility value corresponding to each sustainable development strategy is aggregated by the fuzzy weights with fuzzy performance values, and the best investment strategies can then be decided. Through this study, we successfully demonstrate that the non-additive fuzzy integral is an effective evaluation and appears to be more appropriate than the traditional simple additive weighted method, especially when the criteria are dependent.

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

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.

Fuzzy multiple-criteria decision-making approach for industrial green engineering.

This paper describes a fuzzy hierarchical analytic approach to determine the weighting of subjective judgments. In addition, it presents a nonadditive fuzzy integral technique to evaluate a green engineering industry case as a fuzzy multicriteria decision-making (FMCDM) problem. When the investment strategies are evaluated from various aspects, such as economic effectiveness, technical feasibility, and environmental regulation, it can be regarded as an FMCDM problem. Since stakeholders cannot clearly estimate each considered criterion in terms of numerical values for the anticipated alternatives/strategies, fuzziness is considered to be applicable. Consequently, this paper uses triangular fuzzy numbers to establish weights and anticipated achievement values. By ranking fuzzy weights and fuzzy synthetic utility values, we can determine the relative importance of criteria and decide the best strategies. This paper applies what is called a lambda fuzzy measure and nonadditive fuzzy integral technique to evaluate the synthetic performance of green engineering strategies for aquatic products processors in Taiwan. In addition, we demonstrate that the nonadditive fuzzy integral is an effective evaluation and appears to be appropriate, especially when the criteria are not independent.

The marketing of industrial real estate: application of Taguchi loss functions

AbstractThe marketing of industrial real estate is a resource‐consuming endeavour for all parties involved consisting of many objectives that, in many cases, may be in conflict with one another. One method of minimizing resource requirements, especially time, while increasing the probability of a successful match is to select properties for presentation that maximizes buyer utility. Zionts (1992) indicated one area for future research in multiple criteria decision‐making (MCDM) is in the development of ‘Eclectic Approaches’ using old ideas in a new way to help develop MCDM approaches. In this paper Taguchi loss functions, a procedure commonly used in quality control, is proposed as a tool that can be used by industrial real estate professionals to more efficiently determine the property that most closely matches the buyer's needs. Copyright © 2001 John Wiley & Sons, Ltd.