Modified PROMETHEE Approach for Solving Multi-Criteria Location Problems with Complex Criteria Functions

The specific problem that occurs in multi-criteria decision-making (MCDM) processes is ranking a number of alternatives using complex criteria functions (the hierarchical structure of criteria) whose values must consider the impacts of all-important characteristics and parameters of alternatives. The problem becomes more complex by increasing the number of levels of sub-criteria functions (degree of decomposition). This paper proposes an extended procedure based on the mean values conversion of the net outranking flow of sub-criterion functions obtained by modified PROMETHEE methods. The actual value of criterion functions is used only at the last level, and transformed values of the net outranking flow for generating a final rank of alternatives are introduced at other levels. This procedure provides a more objective comparison of the impact of various individual criteria to rank the alternatives and easier making of unique solution, where the impact of decision-maker (DM) experience and subjective estimation is minimised in the selection. Applicability and practicability of the presented procedure for solving the selection problem of a logistics warehouse location are demonstrated in the analysis of a case study example

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

Group decision-making models for venture capitalists: the PROMETHEE with hesitant fuzzy linguistic information

Venture capitalists (VCs) have long been preoccupied by the issue of selecting a promising start-up firm, whereas, ranking the available start-up firms is an effective way to solve this issue. In this paper, the PROMETHEE is chosen to be the fundamental ranking method. Also, the hesitant fuzzy linguistic term set is a suitable tool to simulate VCs’ evaluation information. Additionally, as the deepening of social division of labor and specialization of individuals, group decision making is famous for improving decision-making quality. Moreover, in the decision-making process, VCs exhibit behavioral characteristics which is depicted well by prospect theory that VCs are risk averse for gains and risk seeking for losses and rely on the transformed probability to make their decisions rather than unidimensional probability. Thus, a group prospect PROMETHEE with hesitant fuzzy linguistic information is constructed for VCs to make a better decision. Then, the proposed method is applied to rank start-up firms and the comparative analyses are made as well. It confirms that the group prospect PROMETHEE is better in describing the common behavioral characteristics of VCs and in enhancing the quality of evaluation

Combining Multi-Criteria Decision Making (MCDM) Methods with Building Information Modelling (BIM): A Review

Integrating building information to support decision-making has been a key challenge in the Architecture, Engineering, and Construction (AEC) industry. The synergy of Building Information Modelling (BIM) and Multi-Criteria Decision Making (MCDM) is expected to improve information integration and decision-making. The aim of this paper is to identify strategies to improve the synergy between MCDM and BIM. From the earliest literature (2009) to the present, this study examines 45 articles combining MCDM with BIM. We find that the five major application domains are sustainability, retrofit, supplier selection, safety, and constructability. Five established strategies for improving the synergy between MCDM and BIM were discussed and can be used as a benchmark for evaluating the application of decision techniques in practice. This study points out gaps of combining MCDM and BIM in the current literature. It also sheds new light into combining MCDM with BIM for practitioners, as to promote integrated decision-making

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 generalized PROMETHEE III with risk preferences on losses and gains

[[abstract]]This study aims to generalize the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) III model by introducing risk preferences of decision makers. The risk preferences are expressed by an S-shaped value function with gain and loss parts. This study then illustrates an environmental evaluation of waste treatment plants for waste electrical and electronic equipment (WEEE) in Taiwan. Sensitivity analysis and the rank test demonstrate that the proposed model is rather stable. The PROMETHEE methods have been involved in various applications, especially in environmental management. One core process of PROMETHEE is to establish a preference difference function with two types of thresholds. The range of the slope lines of the linear preference is within the interval of [0, 1]. Working from the concept of the prospect theory, we extend its S-shaped function to the interval range of [−1, 1] so as to express risk preferences that occur in two quadrants. This research assesses a project on 15 local WEEE treatment plants to promote their recycling capability and technology competitiveness. According to the five aspects, the performance measures of the plants are obtained from a field study. The proposed model has an advantage on rank invariance by changing the thresholds in our case with sensitivity analysis demonstrating the robustness of the model. The generalized PROMETHEE III with risk preferences indeed provides an extension for making a decision in an uncertain environment.[[notice]]補正完

Three essays in corporate governance and corporate finance : international evidence

This thesis presents three original research frameworks, two in corporate governance and one in corporate finance, distributed in three empirical chapters, respectively. Specifically, in Chapter 1, a novel multi-criteria decision analysis (MCDA) approach is developed not only to quantify an aggregate quality of corporate governance at firm level, but also to overcome the limitations of the existing measures (i.e., corporate governance indices) mainly with respect to full compensatory structures and industry-wide heterogeneity. Furthermore, the empirical approach, using PROMETHEE methods and econometric analysis of panel data, provides a strong inverse relationship between firm performance and corporate governance quality. The results rely on outranking relationships (over five million pair comparisons) among companies (1,203 US listed firms during 2002 to 2014) across various corporate governance criteria, comparing the aggregate quality against a well-known corporate governance index (ASSET4 ESG in Datastream).In Chapter 2, the theory of system reliability is used to model the behaviour of companies in terms of their corporate governance practices and mechanisms. Particularly, machine-learning techniques are proposed to assess a corporate governance system. The mapping of its inputs or specific indicators (e.g., corporate social responsibility, average number of board meetings, compensation policy, auditing independency and independent board) as components (either in operating or failed state), along with firm-specific conditions (i.e., age, size, risk, growth), into a reliability system aims to determine an approximate structure function that models the behaviour of the system. The proposed approach is applied to another data sample set of 1,109 US listed companies during 2002 to 2014, the financial and non-financial indicators are modelled as components of the corporate governance system, and returns on assets is defined as the system output. The results show that growth opportunities matter for the proper functioning of the system, and suggest that if companies are more transparent (i.e., components show a low probability of failure) both the trustworthiness of the companies and the system reliability improves.In Chapter 3, a research framework to analyse failure in mergers and acquisitions (M&A) reveals that not only deal characteristics (i.e., deal attitude, means of payments, deal size, ownership), but also acquirers’ and targets’ firm size, acquirers’ economic freedom, and targets’ accounting returns significantly explain the likelihood of deal failure. To this aim, a large dataset of 137,116 worldwide M&A deals (during 1977–2014 on more than 140 countries) and novel specifications of logit regression models are analysed. This chapter contributes and expands the literature in M&A deals and business research by evaluating how incumbents’ specific information can constrain the firms’ assets movement (efficiency perspective).Regarding the implications, the findings in Chapter 1 are of particular interest to both scholars and decision makers (e.g., managers, shareholders, investor, policy makers) including rating agencies, who want to assess advantages and disadvantages of corporate governance indices. Chapter 2’s findings are useful mainly for board of directors for detecting what corporate governance components are more line up with the most successful companies, or for quantifying firm reliability. The results in Chapter 3 suggest to bidders to be aware of not only deal characteristics, but also firm size discernments, economic freedom outlooks, and accounting figures when considering the exit option of a deal withdrawal

Multi-Criteria Decision Making under Uncertain Evaluations

Multi-Criteria Decision Making (MCDM) is a branch of operation research that aims to empower decision makers (DMs) in complex decision problems, where merely depending on DMs judgment is insufficient. Conventional MCDM approaches assume that precise information is available to analyze decision problems. However, decision problems in many applications involve uncertain, imprecise, and subjective data. This manuscripts-based thesis aims to address a number of challenges within the context of MCDM under uncertain evaluations, where the available data is relatively small and information is poor. The first manuscript is intended to handle decision problems, where interdependencies exist among evaluation criteria, while subjective and objective uncertainty are involved. To this end, a new hybrid MCDM methodology is introduced, in which grey systems theory is integrated with a distinctive combination of MCDM approaches. The emergent ability of the new methodology should improve the evaluation space in such a complex decision problem. The overall evaluation of a MCDM problem is based on alternatives evaluations over the different criteria and the associated weights of each criterion. However, information on criteria weights might be unknown. In the second manuscripts, MCDM problems with completely unknown weight information is investigated, where evaluations are uncertain. At first, to estimate the unknown criteria weights a new optimization model is proposed, which combines the maximizing deviation method and the principles of grey systems theory. To evaluate potential alternatives under uncertain evaluations, the Preference Ranking Organization METHod for Enrichment Evaluations approach is extended using degrees of possibility. In many decision areas, information is collected at different periods. Conventional MCDM approaches are not suitable to handle such a dynamic decision problem. Accordingly, the third manuscript aims to address dynamic MCDM (DMCDM) problems with uncertain evaluations over different periods, while information on criteria weights and the influence of different time periods are unknown. A new DMCDM is developed in which three phases are involved: (1) establish priorities among evaluation criteria over different periods; (2) estimate the weight of vectors of different time periods, where the variabilities in the influence of evaluation criteria over the different periods are considered; (3) assess potential alternatives