VIKOR Technique:A Systematic Review of the State of the Art Literature on Methodologies and Applications

The main objective of this paper is to present a systematic review of the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method in several application areas such as sustainability and renewable energy. This study reviewed a total of 176 papers, published in 2004 to 2015, from 83 high-ranking journals; most of which were related to Operational Research, Management Sciences, decision making, sustainability and renewable energy and were extracted from the “Web of Science and Scopus” databases. Papers were classified into 15 main application areas. Furthermore, papers were categorized based on the nationalities of authors, dates of publications, techniques and methods, type of studies, the names of the journals and studies purposes. The results of this study indicated that more papers on VIKOR technique were published in 2013 than in any other year. In addition, 13 papers were published about sustainability and renewable energy fields. Furthermore, VIKOR and fuzzy VIKOR methods, had the first rank in use. Additionally, the Journal of Expert Systems with Applications was the most significant journal in this study, with 27 publications on the topic. Finally, Taiwan had the first rank from 22 nationalities which used VIKOR technique

A Textual Data-Oriented Method for Doctor Selection in Online Health Communities

As doctor–patient interactive platforms, online health communities (OHCs) offer patients massive information including doctor basic information and online patient reviews. However, how to develop a systematic framework for doctor selection in OHCs according to doctor basic information and online patient reviews is a challenged issue, which will be explored in this study. For doctor basic information, we define the quantification method and aggregate them to characterize relative influence of doctors. For online patient reviews, data analysis techniques (i.e., topics extraction and sentiment analysis) are used to mine the core attributes and evaluations. Subsequently, frequency weights and position weights are respectively determined by a frequency-oriented formula and a position score-based formula, which are integrated to obtain the final importance of attributes. Probabilistic linguistic-prospect theory-multiplicative multiobjective optimization by ratio analysis (PL-PT-MULTIMOORA) is proposed to analyze patient satisfactions on doctors. Finally, selection rules are made according to doctor influence and patient satisfactions so as to choose optimal and suboptimal doctors for rational or emotional patients. The designed textual data-driven method is successfully applied to analyze doctors from Haodf.com and some suggestions are given to help patients pick out optimal and suboptimal doctors.National Natural Science Foundation of China (NSFC) 72171182 71801175 71871171 72031009Project of Service Science and Innovation Key Laboratory of Sichuan Province KL2105Project of China Scholarship Council 202107000064 202007000143Andalusian government B-TIC-590-UGR20FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades P20 00673 PID2019-103880RB-I00MCIN/AEI/10.13039/50110001103

Fuzzy Techniques for Decision Making 2018

Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches

Improving residential housing project purchase by using integrated multi-attribute decision making and sentiment analysis technique

The residential house purchase decision making is highly complex due to reasons such as conflicting criteria which is hard to model, infrequent type of decisions, uncertain and irreversible decision outcomes, high investment, and long-term financial burden. Unlike many other types of purchasing, housing purchase decision-making is riskier and sometimes even ‘traumatic’. It is often associated with feeling of regret and the possibility of loss among homebuyers. Typically, the Multi Attribute Decision Making (MADM) models are used to systematically assist and structure residential housing project selection decision making. However, the MADM models impose deficiencies in the evaluation process due to insufficient knowledge of homebuyers, ignorance of public opinions and limited sources of information. Furthermore, the application of MADM models requires homebuyer to rely on their evaluation experience which potentially led to an imprecise decision. Hence, this study developed an improved model by integrating MADM and three approaches of Sentiment Analysis to capture and rank criteria from public opinions through online reviews. Properties online forums and google reviews were selected to extract public opinions through online reviews. Three high-rise residential projects located in Malaysia were used as case projects for demonstrating the model development and validation of the proposed framework. Three Sentiment Analysis approach were considered; Lexicon, Machine Learning and hybrid. Based on the ranking established by the models, it shows that location, facility, and house attributes are the most important criteria in residential housing purchase decision making. In addition, classification using a hybrid MADM Sentiment Analysis approach outperforms the Lexicon approach with better accuracy. The developed model can assist homebuyer in making decision for the current practice. Moreover, it can be generalised to other related multi-criteria applications with the use of online public opinions as reference

A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making

In the realm of multi-criteria decision-making (MCDM) problems, the selection of a weighting method holds a critical role. Researchers from diverse fields have consistently employed MCDM techniques, utilizing both traditional and novel methods to enhance the discipline. Acknowledging the significance of staying abreast of such methodological developments, this study endeavors to contribute to the field through a comprehensive review of several novel weighting-based methods: CILOS, IDOCRIW, FUCOM, LBWA, SAPEVO-M, and MEREC. Each method is scrutinized in terms of its characteristics and steps while also drawing upon publications extracted from the Web of Science (WoS) and Scopus databases. Through bibliometric and content analyses, this study delves into the trend, research components (sources, authors, countries, and affiliations), application areas, fuzzy implementations, hybrid studies (use of other weighting and/or ranking methods), and application tools for these methods. The findings of this review offer an insightful portrayal of the applications of each novel weighting method, thereby contributing valuable knowledge for researchers and practitioners within the field of MCDM.WOS:0009972313000012-s2.0-85160203389Emerging Sources Citation IndexarticleUluslararası işbirliği ile yapılan - EVETHaziran2023YÖK - 2022-2

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

A Resilience Modelling Approach for Oil Terminal Operations Under High Uncertainties

Oil terminals are complex infrastructures due to their diverse operational activities. They are exposed to diverse risks because they usually operate in a dynamic environment in which safety barriers are sometime overwhelmed, leading to the disruption of operations due to a high level of uncertainty. However, the ability of oil terminals to minimise vulnerability and maximise resilience depends on the availability of the correct anticipated information at the right time for a decision-making process. An important finding from the reviewed literature revealed that uncertainties and the unpredictability of the convergent effect of several hazardous factors have the potential to cause major disruptions such as fire, explosion and transit accidents. The consequences of these disruptions can lead to infrastructure damage and loss of life. The common operational threats to oil terminal operations (OTOs) substantiates the need for a holistic resilience model for operations in offshore/onshore terminals such as berthing/unberthing, vessel manoeuvring, loading and offloading, storage, etc. Due to the uncertainties associated with these operations and the cases of reported incidents/accidents, this research focuses more on the aspect of loading and offloading operations at ship/terminal interface. An emphasis on a resilience modelling approach provides a flexible yet robust model for OTOs to address disruption proactively, particularly with constantly evolving hazards and threats. This thesis introduces an innovative approach towards resilience modelling based on a developed novel framework. The key aspect of the framework was supported using three proposed models: (1) the integration of Utility Theory and Swiss Cheese Model (UtiSch_+), to evaluate the relative importance of the identified hazard factors (HFs), (2) a Bayesian network (BN), to calculate the overall probability that a specific hazard is present and, (3) an Analytical Hierarchical Process (AHP) - Prospect Theory (PT) approach, as an important model for a strategic decision selection method. An empirical study was conducted to test the validity the proposed models, using case studies and Sensitivity Analysis (SA). The result obtained demonstrated that the models are effective techniques to obtain the relative weight of the identified Hazard Factors (HFs) in order to prioritise them, for dynamic hazards probability evaluation and to prioritise suggested resilience strategies in order of importance to mitigate hazard/risk level. Evidently, the result revealed appears reasonable and appropriate for investment, in order to support a strategic decision for the selection of a resilience strategy for resilience improvement in OTOs