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About this book

This book presents 27 methods of the Multiple Attribute Decision Making (MADM), which are not discussed in the existing books, nor studied in details, using more applications. Nowadays, decision making is one of the most important and fundamental tasks of management as an organizational goal achievement that depends on its quality. Decision making includes the correct expression of objectives, determining different and possible solutions, evaluating their feasibility, assessing the consequences, and the results of implementing each solution, and finally, selecting and implementing the solution. Multiple Criteria Decision Making (MCDM) is sum of the decision making techniques. MCDM is divided into the Multiple Objective Decision Making (MODM) for designing the best solution and MADM for selecting the best alternative. Given that the applications of MADM are mostly more than MODM, wide various techniques have been developed for MADM by researchers over the last 60 years, and the current book introduces some of the other new MADM methods.


Table of Contents

Frontmatter

Chapter 1. SMART Method

Abstract
The Simple Multi-Attribute Rating Technique (SMART) was introduced by Winterfeldt and Edwards in 1986 (Edwards and Barron in Org Behav Hum Decis Processes 60:306–325, 1994 [1]; Lootsma in Eur J Oper Res 64:467–476, 1996 [2]), in which a limited number of alternatives are examined based on a limited number of attributes. The present method aimed to rank the alternatives by a combination of quantitative and qualitative attributes. This is a convenient technique because of its ease of use, which is used in many cases such as evaluation of nuclear waste disposal sites (Otway and Edwards in Application of a simple multi-attribute rating technique to evaluation of nuclear waste disposal sites: a demonstration. IIASA Research Memorandum, Laxenburg, Austria, 1977 [3]) and ERP system selection (Haddara in Procedia Technol 16:394–403, 2014 [4]).
Alireza Alinezhad, Javad Khalili

Chapter 2. REGIME Method

Abstract
The REGIME method , initially introduced by Hinloopen, Nijkamp, and Rietveld in 1983 (Hinloopen and Nijkamp in Qual Quant 24:37–56, 1990 [6]; Hinloopen and Nijkamp in Kwantitatieve Methoden 7:61–78, 1986 [7]), is a multiple attribute qualitative method which solves the problem using the REGIME matrix, and a final ranking of the alternatives is done. In the final ranking, the weight of attributes, introduced by the decision maker, is important and can influence the results. This technique is used for ranking the sawability of ornamental and building stones (Kamran et al. in J Eng Res 5:125–149, 2017 [8]) and evaluation and ranking of coastal areas (Hinloopen et al. in Essays and surveys on multiple criteria decision making. Springer, Berlin, pp. 146–155 [9]) due to its features.
Alireza Alinezhad, Javad Khalili

Chapter 3. ORESTE Method

Abstract
The ORESTE method was initially introduced by Roubens at a conference in 1980 (Mercier et al. Appl Math Comput 54:183–196, 1993 [10]; Pastijn and Leysen in Math Comput Model 12:1255–1268, 1989 [11]; Roubens in Eur J Oper Res 10:51–55, 1982 [12]) and then was expanded in an article in 1980. ORESTE is used when the decision maker provides an analyst with an initial ranking of the attributes for decision making. Also, the best alternative is selected among the various alternatives, which is accompanied by different qualitative and quantitative attributes. This technique is used in many cases such as ranking of Web design firms (Adali and Isik in Ege Akad Bakis 17:243–253, 2017 [13]), material selection (Chatterjee and Chakraborty in Mater Des 35:384–393, 2012 [14]), and insurance company selection (Isik in Alphanumeric J 4:55–68, 2016 [15]).
Alireza Alinezhad, Javad Khalili

Chapter 4. VIKOR Method

Abstract
The VIKOR method was introduced by Opricovic in 1998 (Alinezhad and Esfandiari in J Optim Ind Eng 5:29–34, 2012 [16]; Amini and Alinezhad in Iran J Optim 8:111–122, 2016 [17]; Opricovic and Tzeng in Eur J Oper Res 178:514–529, 2007 [18]; Opricovic and Tzeng in Comput Aided Civ Infrastruct Eng 17:211–220, 2002 [19]. This technique is one of the compromising methods in compensatory models, as the closest alternative to the ideal solution is preferred in this subgroup. Generally, the technique focuses on the alternatives ranking and selecting an alternative with a set of contradictory attributes, and ultimately, provide a compromise solution, contributing the decision maker to reach the final solution. The VIKOR has been abundantly applied in decision making to select the ideal alternative since its introduction and has been used in analyzing the logistic outsourcing (Kumar et al. in J Manuf Technol Manage 23:885–898, 2012 [20]), selection of suppliers (Guo and Zhang in Selection of suppliers based on rough set theory and VIKOR algorithm. Shanghai, China, pp. 49–52, 2008 [21]), and airport location selection (Sennaroglu and Celebi in Transp Res Part D: Transp Environ 59:160–173, 2018 [22].
Alireza Alinezhad, Javad Khalili

Chapter 5. PROMETHEE I-II-III Methods

Abstract
The Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) methods was first introduced by Brans, Vincke, and Mareschal in 1986 (Brans and De Smet in Multiple criteria decision analysis, Springer, New York, 2016 [25]; Brans and Mareschal in Multiple criteria decision analysis: state of the art surveys, Springer, New York, pp. 163–186, 2005 [26]; Greco et al. in Multiple criteria decision analysis, 2016 [27]) which has been widely used so far. As the name indicates, the providers of this technique have sought to find a basic solution to improve decision-making evaluation. Therefore, it is recognized as an efficient method. The PROMETHEE I method only examines the obtained output and input flows and ranks alternatives partially.
Alireza Alinezhad, Javad Khalili

Chapter 6. QUALIFLEX Method

Abstract
The QUALIFLEX method was introduced by Paelinck in 1975 (Qualitative and quantitative mathematical economics. Springer, Dordrecht, pp. 217–266, 1982 [35]; Econ Lett 1:193–197, 1978 [36]; The multi-criteria method QUALIFLEX: past experiences and recent developments. Netherlands Economic Institute, 1979 [37]), which is rooted in the permutation method, introduced by Jacquet Lagreze (J Optim Industrial Eng 5:29–34, 2012 [16]; Multiple criteria decision analysis. Springer, Berlin, 2016 [27]). In QUALIFLEX , each possible ranking of existing m alternative is evaluated. In other words, the ranking of alternatives is evaluated to the number of m! permutation, and finally, the most appropriate ones are selected for the final ranking.
Alireza Alinezhad, Javad Khalili

Chapter 7. SIR Method

Abstract
Superiority and Inferiority Ranking (SIR) method was introduced by Xu in 2001 (Am J Sci Res 1:21–35, 2011 [42]; Int J Inf Technol Decis Making 12:395–423, 2013 [43]; Am J Sci Res 28:71–86, 2011 [44]). The basis of this technique is the utilization of superiority and inferiority values, by determining the type of the preference function, similar to the PROMETHEE method. Then, the net flow is calculated using the weight matrix, similar to simple additive weighting (SAW) method and technique of order preference by similarity to the ideal solution (TOPSIS) . Finally, the optimal solution is chosen among the solutions obtained from the superiority and inferiority matrix.
Alireza Alinezhad, Javad Khalili

Chapter 8. EVAMIX Method

Abstract
The EVAluation of MIXed data (EVAMIX) method, introduced in 1982 by Voogd (Multiple Criteria Decision Making 11:36–5, 2016 [49]; Am J Oper Res 3:542–569, 2013 [50]; Multicriteria evaluation for urban and regional planning. Pion Ltd., London, 1983 [51]), with two completely different approaches to the quantitative and qualitative attributes and attributes should be independent.
Alireza Alinezhad, Javad Khalili

Chapter 9. ARAS Method

Abstract
The Additive Ratio ASsessment (ARAS) method was introduced by Zavadskas and Turskis in 2010 (Econ Comput Econ Cyb 46:65–87, 2012 [56]; J Civ Eng Manag 16:257–266, 2010 [57]; Arch Civ Mech Eng 10:123–141, 2010 [58]), which aims to select the best alternative based on a number of attributes and the final ranking of alternatives is made by determining the utility degree of each alternative.
Alireza Alinezhad, Javad Khalili

Chapter 10. Taxonomy Method

Abstract
The taxonomy method was introduced by Adanson in 1763 and expanded by a group of mathematicians from Poland in 1950. In 1968, Zyegnant Hellwing from the Wroclaw high school introduced this method as a means of classifying and determining the degree of development (Jurkowska in Oecon Copernic 5:49–73, 2014 [65]; Bienkowska in Activities of local authorities in promoting entrepreneurship in Poland. Jelgava, Latvia, pp. 26–31, 2013 [66]). This method is very appropriate for grading, classifying, and comparing different activities with respect to their advantages and utility degree from studied attributes.
Alireza Alinezhad, Javad Khalili

Chapter 11. MOORA Method

Abstract
The Multi-Objective Optimization Ratio Analysis (MOORA) method was introduced by Brauers in 2004 (Brauers in Ann Oper Res 206:39–58, 2013 [72]; Brauers and Zavadskas in Control Cybern 35:445–469, 2006 [73]; Brauers and Zavadskas in Technol Econ Dev Econ 15:352–375, 2009 [74]; Brauers et al. in Citizens and governance for sustainable development, pp. 156–161, 2006 [75]; Brauers et al. in Simulation and optimisation in business and industry, pp. 131–135, 2006 [76]), which is considered as an objective (non-subjective) method. Moreover, desirable and undesirable criteria are used simultaneously for ranking to select a superior or higher alternative among different alternatives. This technique has a large number of applications such as contractor selection (Brauers in J Bus Econ Manag 4:245–255, 2008 [77]; Brauers et al. in J Bus Econ Manag 9:245–255, 2008 [78]), optimization of machinery process parameters (Chaturvedi et al. in Int J Res Eng Appl Sci 4:1–10, 2014 [79]; Khan and Maity in Int J Eng Res Afr 20:19–40, 2016 [80]; Shihab et al. in Manag Sci Lett 8:241–246, 2018 [81]), and supplier selection (Karande and Chakraborty in IUP J Oper Manag 11:6–18, 2012 [82]).
Alireza Alinezhad, Javad Khalili

Chapter 12. COPRAS Method

Abstract
The COmplex PRoportional ASsessment (COPRAS) method was introduced by Zavadskas, Kaklauskas, and Sarka in 1994 (Podvezko in Inz Ekon-Eng Econ 22:134–146, 2011 [84]; Zagorskas et al. in Ekologija 53:55–63, 2007 [85]; Zavadskas et al. in Balt J Road Bridge Eng 2:193–203, 2007 [86]; Zavadskas et al. in J Civ Eng Manag 14:85–93, 2008 [87]). This method is used to assess the maximizing and minimizing index values, and the effect of maximizing and minimizing indexes of attributes on the results assessment is considered separately. The COPRAS method is applied in some areas such as risk assessment (Alinezhad et al. in J Money Econ 10:87–121, 2015 [88]; Valipour et al. in J Civ Eng Manag 23:524–532, 2017 [89]), investment project selection (Popovic et al. in Serb J Manag 7:257–269, 2012 [90]), and material selection (Xia et al. in Appl Mech Mater 707:505–508, 2015 [91]).
Alireza Alinezhad, Javad Khalili

Chapter 13. WASPAS Method

Abstract
The Weighted Aggregates Sum Product Assessment (WASPAS) method was introduced by Zavadskas, Turskis, Antucheviciene, and Zakarevicius in 2012 (Zavadskas et al. in Technol Econ Dev Econ 1:131 (2013) [93], Zavadskas et al. in Sustainability 7:15923 (2015) [94], Zavadskas et al. in Acta Montan Slovaca 21:85 (2016) [95], Zavadskas et al. in Arch Civ Mech Eng 16(1):76 (2016) [96]). This method is a combination of Weighted Sum Model (WSM) and Weighted Product Model (WPM) (Zavadskas et al. in Elektron Elektrotech 122:3 (2012) [97]) . Thus, the relative importance of each attribute is simply determined, and then, the alternatives are evaluated and prioritized. This technique is applied in the personal selection (Bagocius et al. in J Civ Eng Manag 20:590 [98], Karabasevic et al. in BizInfo (Blace) J Econ Manag Inf 7:1 (2016) [99], Urosevic et al. in Cybern Stud Res 51:75 (2017) [100]), analysis of machining processes (Madic et al. in J Prod Eng 17:1 (2014) [101], Prasad et al. in Cogent Eng 5:1 (2018) 102], and material selection (Petkovic et al. in Appl Mech Mater 809:1468 (2015) [103], Yazdani et al. in Eng Econ 27:382 (2016) 104]).
Alireza Alinezhad, Javad Khalili

Chapter 14. SWARA Method

Abstract
The Stepwise Weight Assessment Ratio Analysis (SWARA) method was introduced by Kersuliene, Zavadskas, and Turskis in 2010 (Ghorshi Nezhad et al. in Econ Res 28:1111 (2015) [105], Juodagalviene et al. in Eng Struct Technol 9:117 (2017) [106], Khalili and Alinezhad in 3rd international conference on industrial & system engineering (2017) [107], Zolfani et al. in Econ Res 26:153 (2013) [108]). In this method, done by the weighting method, the relative importance and the initial prioritization of alternatives for each attribute are determined by the opinion of the decision maker, and then, the relative weight of each attribute is determined.
Alireza Alinezhad, Javad Khalili

Chapter 15. DEMATEL Method

Abstract
The DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) method was introduced by Fonetla and Gabus in 1971 (Cheng et al. in Int J Hosp Manag 31:1155 (2015) [114], Sumrit and Anuntavoranich in J Eng Manag Appl Sci Technol 4:81 (2013) [115], Wu and Tsai Appl Math Comput 218:2334 (2011) [116], Yamazaki et al. in Rep Fac Eng 48:25 (1997) [117]), mainly used to study very complex global issues. The DEMATEL method is applied to construct a network relation design in order to examine the internal relation among the attributes. This technique is successfully applied in many situations such as the analysis of barriers of waste recycling (Chauhan et al. in J Air Waste Manag Assoc 68:100 (2018) [118]), project selection (Alinezhad and Simiari in J Ind Manag Stud 11:41 (2013) [119], Ho et al. in Expert Syst Appl 38:16 (2011) 120]), and evolution of e-learning programs (Tzeng et al. in Expert Syst Appl 32:1028 (2007) [121]).
Alireza Alinezhad, Javad Khalili

Chapter 16. MACBETH Method

Abstract
The Measuring Attractiveness by a Categorical Based Evaluation TecHnique (MACBETH) method, introduced by Bana e Costa and Vansnick in 1990 [123–125], examines the alternatives with multi-attributes and opposite objectives. In fact, this interactive method is appropriate for examining and ranking of alternatives with respect to a wide range of qualitative and quantitative attributes. Therefore, the MACBETH method is applied in many cases such as performance analysis of online bookstores [126], selection of manufacturing systems [127], and evaluation of supplier [128, 129].
Alireza Alinezhad, Javad Khalili

Chapter 17. ANP Method

Abstract
Analytic Network Process (ANP) method was introduced by Saaty in 1996 [132–137]. In this method, a decision-making problem is analyzed into several different levels, and the sum of these decision-making levels forms a hierarchy.
Alireza Alinezhad, Javad Khalili

Chapter 18. MAUT Method

Abstract
The Multi-Attribute Utility Theory (MAUT) method was introduced by Keeney and Raiffa in 1976 [27, 142–144]. The simplicity in solving multiple attribute decision-making problems is one of the advantages of this technique, and it gives abundant freedom of action to the decision makers to make the result more accurate and realistic. This method is applicable in areas such as assessment of industry firms [145] and selecting a project portfolio [146].
Alireza Alinezhad, Javad Khalili

Chapter 19. IDOCRIW Method

Abstract
The Integrated Determination of Objective CRIteria Weights (IDOCRIW) method was introduced by Zavadskas and Podvezko in 2016 [149–151]. This technique benefits from the Entropy and Criterion Impact LOSs (CILOS) methods to determine a relative impact loss as well as the weight of attributes in a combination with two methods. Given the presentation of the IDOCRIW method in recent years, it is applied in areas such as analysis of rotor systems [152], assessing the performance of the construction sectors [153].
Alireza Alinezhad, Javad Khalili

Chapter 20. TODIM Method

Abstract
The TODIM method was introduced by Gomes and Lima in 1992 [155–159]. The main idea is to measure the dominance degree of each alternative over the other alternatives using the overall value, and then, the alternatives are evaluated and ranked according to the following features.
Alireza Alinezhad, Javad Khalili

Chapter 21. EDAS Method

Abstract
The Evaluation based on Distance from Average Solution (EDAS) method was introduced by Keshavarz Ghorabaee, Zavadskas, Olfat, and Turskis in 2015 [165–168]. This method is very practical in conditions with the contradictory attributes, and the best alternative is chosen by calculating the distance of each alternative from the optimal value.
Alireza Alinezhad, Javad Khalili

Chapter 22. PAMSSEM I & II

Abstract
The PAMSSEM methods were introduced by Martel, Kiss, and Rousseau in 1996 [172–175]. This method patterns the preferences of decision maker to choose the best alternative using an outranking approach, according to the ordinal or cardinal of each attribute. Also, only the entering and leaving flows are examined and alternatives are ranked partially. However, in the PAMSSEM II method , the net flow is determined as final values and alternatives are ranked completely.
Alireza Alinezhad, Javad Khalili

Chapter 23. ELECTRE I–II–III Methods

Abstract
ELimination Et Choix Traduisant la REalite (ELECTRE) method was first introduced by Roy in 1990 [34, 180–182], which evaluates all alternatives using outranking comparisons, and ineffective and eliminates low-attractive alternatives.
Alireza Alinezhad, Javad Khalili

Chapter 24. EXPROM I & II Method

Abstract
The EXtension of the PROMethee (EXPROM) methods were first introduced by Diakoulaki and Koumoutsos in 1991 [190–192] and seek to find a solution for evaluating alternatives and rank the alternatives more accurately using widely available information.
Alireza Alinezhad, Javad Khalili

Chapter 25. MABAC Method

Abstract
The Multi-Attributive Border Approximation area Comparison (MABAC) method was introduced by Pamucar and Cirovic in 2015 [198–201].
Alireza Alinezhad, Javad Khalili

Chapter 26. CRITIC Method

Abstract
The CRiteria Importance Through Intercriteria Correlation (CRITIC) method , which was proposed by Diakoulaki, Mavrotas, and Papayannakis in 1995 (Houqiang and Ling in J Hubei Inst Technol 32:83 (2012) [208], Wang and Zhao in Int J Adv Manuf Technol 84:2381 (2016) [209], Xie et al. in J Saf Environ 14:122 (2014) [210], Zhao et al. in J Liq Chromatogr Relat Technol 34:2008 (2011) [211]), is mainly used to determine the weight of attributes. In the present method, the attributes aren’t in contradiction with each other, and the attributes weights are determined using the decision matrix. It is used for the automatic areal feature matching (J Korean Soc Surv Geod Photogr Cartogr 29:113 (2011) [212], Kim and Yu in Int Arch Photogr Remote Sens Spat Inf Sci 40:75 [213]), medical quality assessment (Ping in Value Eng 1:200 (2011) [214]), and ranking of machining processes (Madic and Radovanovic in UPB Sci Bull Ser D 77:193 [215]).
Alireza Alinezhad, Javad Khalili

Chapter 27. KEMIRA Method

Abstract
The KEmeny Median Indicator Ranks Accordance (KEMIRA) method was introduced by Krylovas, Zavadskas, Kosareva, and Dadelo in 2014 (Krylovas et al. in Econ Res29:50 (2016) [217], Krylovas et al. in Rom J Econ Forecast 19:19 (2016) [218]). In this method, the final ranking of alternatives is done after determining the priority and weight of attributes in two different groups and in the form of the decision matrix specified by the experts. It is used for the personal evaluation and selection (Kosareva et al. in Int J Comput Commun Control 11:51 (2017) [219], Krylovas et al. in Int J Inf Technol Decis Making 16:1183 (2017) [220]), and evaluating the sustainability of transportation systems (Oses et al. in J Urban Plan Dev 144:1 (2017) [221]).
Alireza Alinezhad, Javad Khalili

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