A Complex MCDM Procedure for the Assessment of Economic Development of Units at Different Government Levels

Studies on the economic development of government units are among the key challenges for authorities at different levels and an issue often investigated by economists. In spite of a considerable interest in the issue, there is no standard procedure for the assessment of economic development level of units at different levels of government (national, regional, sub-regional). This assessment needs a complex system of methods and techniques applicable to the various types of data. So, adequate methods must be used at each level. This paper proposes a complex procedure for a synthetic indicator. The units are assessed at different government levels. Each level (national, regional, and sub-regional) may be described with a particular type of variables. Set of data may include variables with a normal or near-normal distribution, a strong asymmetry or extreme values. The objective of this paper is to present the potential behind the application of a complex Multi-Criteria Decision Making (MCDM) procedure based on the tail selection method used in the Extreme Value Theory (EVT), i.e., Mean Excess Function (MEF) together with one of the most popular MCDM methods, namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assess the economic development level of units at different government levels. MEF is helpful to identify extreme values of variables and limit their impact on the ranking of local administrative units (LAUs). TOPSIS is suitable in ranking units described with multidimensional data set. The study explored the use of two types of TOPSIS (classical and positional) depending on the type of variables. These approaches were used in the assessment of economic development level of LAUs at national, regional and sub-regional levels in Poland in 2017.

[1]  L. Hubert,et al.  A general statistical framework for assessing categorical clustering in free recall. , 1976 .

[2]  Jiangjiang Wang,et al.  Review on multi-criteria decision analysis aid in sustainable energy decision-making , 2009 .

[3]  Aleksandra Łuczak,et al.  The positional MEF-TOPSIS method for the assessment of complex economic phenomena in territorial units , 2020 .

[4]  Adiel Almeida Filho,et al.  Sorting with TOPSIS through boundary and characteristic profiles , 2020, Comput. Ind. Eng..

[5]  Krishnendu Shaw,et al.  Evaluation and selection of mobile health (mHealth) applications using AHP and fuzzy TOPSIS , 2019, Technology in Society.

[6]  Adel Hatami-Marbini,et al.  An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange , 2017, Appl. Soft Comput..

[7]  Andrew Errington,et al.  Measuring the determinants of relative economic performance of rural areas , 2009 .

[8]  Daniela Fuchs-Hanusch,et al.  A framework for water loss management in developing countries under fuzzy environment: Integration of Fuzzy AHP with Fuzzy TOPSIS , 2016, Expert Syst. Appl..

[9]  Krzysztof Palczewski,et al.  The fuzzy TOPSIS applications in the last decade , 2019, KES.

[10]  B. B. Zaidan,et al.  Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017 , 2019, Comput. Oper. Res..

[11]  T. Stewart A CRITICAL SURVEY ON THE STATUS OF MULTIPLE CRITERIA DECISION MAKING THEORY AND PRACTICE , 1992 .

[12]  Yen-Ting Chen,et al.  Applying Fuzzy AHP and TOPSIS Method to Identify Key Organizational Capabilities , 2020, Mathematics.

[13]  Laura A. Reese,et al.  Local Economic Development in the United States and Canada , 2004 .

[14]  Cengiz Kahraman,et al.  A novel fuzzy TOPSIS method using emerging interval-valued spherical fuzzy sets , 2019, Eng. Appl. Artif. Intell..

[15]  Erkan Celik,et al.  An Integrated Best-Worst and Interval Type-2 Fuzzy TOPSIS Methodology for Green Supplier Selection , 2018, Mathematics.

[16]  Hui Han,et al.  A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms , 2018, Expert Syst. Appl..

[17]  Zeshui Xu,et al.  An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation , 2018, Inf. Sci..

[18]  Morteza Yazdani,et al.  A state-of the-art survey of TOPSIS applications , 2012, Expert Syst. Appl..

[19]  Dipankar Deb,et al.  Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms , 2020 .

[20]  Xinwang Liu,et al.  An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information , 2018, Inf. Sci..

[21]  Edmundas Kazimieras Zavadskas,et al.  Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014 , 2015, Expert Syst. Appl..

[22]  Hadi Shirouyehzad,et al.  A MCDM Approach for Prioritizing Production Lines: A Case Study , 2011 .

[23]  Jiafu Su,et al.  A combined fuzzy DEMATEL and TOPSIS approach for estimating participants in knowledge-intensive crowdsourcing , 2019, Comput. Ind. Eng..

[24]  Jing Li,et al.  Sustainable supplier selection based on SSCM practices: A rough cloud TOPSIS approach , 2019, Journal of Cleaner Production.

[25]  Hongliang Ren,et al.  A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments , 2020, Appl. Soft Comput..