A comparative study of multiple-criteria decision-making methods under stochastic inputs

This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative

Water Absorption Process Parametric Selection For Natural Composites Using The PROMETHEE Method And Analytical Hierarchy Process For Objective Weights For Ship’s Hull Application

The purpose of this study is to establish the importance of parameters in a water absorption process for natural particulate composite for ship’s hull applications. To attain useful and reliable outcomes, the subjective evaluation of the assessor and weights of inputs are combined in a PROMETHEE and analytical hierarchy process (AHP) approach. The PROMETHEE serves the goal of ranking while the AHP is deployed to establish the objective weighing. It was found that time is the heading parameter for the natural particulate thermoset composite solutions, compared with thickness and length. By integrating PROMETHEE and AHP, it was proved that this approach offers a higher level of confidence to composite developers than initiative practices that currently dominate choices of parameters. It is particularly useful for natural particulate water absorption parametric selection since it is an innovative and scientific choice approach involving multicriteria analysis

Life cycle sustainability assessment for multi-criteria decision making in bridge design: A review

Sustainable design of infrastructures has become a major matter of study since the recent establishment of the Agenda 2030. This paper provides a systematic literature review on the use of multi-criteria decision making techniques used so far for the sustainable design of bridges. Special attention is put as well on how the reviewed studies assess the sustainable performance of bridge designs along their life cycle from the economic, the environmental and the social perspective. Although SAW and AHP are recurrently used in the sustainable assessment of bridges, the analysis of the most recent articles show that the application of TOPSIS and PROMETHEE techniques are gaining increasing relevance for such purpose. Most of the studies focus on the research of the construction and the maintenance stage of bridges. However, a need for further analysis is identified when it comes to the assessment of the impacts resulting from the End of Life cycle stage of bridges from a sustainable point of view. The use of intuitionistic and neutrosophic logic have been detected as emerging alternatives to the fuzzy approach of decision making problems

Type-2 neutrosophic number based multi-attributive border approximation area comparison (MABAC) approach for offshore wind farm site selection in USA.

The technical, logistical, and ecological challenges associated with offshore wind development necessitate an extensive site selection analysis. Technical parameters such as wind resource, logistical concerns such as distance to shore, and ecological considerations such as fisheries all must be evaluated and weighted, in many cases with incomplete or uncertain data. Making such a critical decision with severe potential economic and ecologic consequences requires a strong decision-making approach to ultimately guide the site selection process. This paper proposes a type-2 neutrosophic number (T2NN) fuzzy based multi-criteria decision-making (MCDM) model for offshore wind farm (OWF) site selection. This approach combines the advantages of neutrosophic numbers sets, which can utilize uncertain and incomplete information, with a multi-attributive border approximation area comparison that provides formulation flexibility and easy calculation. Further, this study develops and integrates a techno-economic model for OWFs in the decision-making. A case study is performed to evaluate and rank five proposed OWF sites off the coast of New Jersey. To validate the proposed model, a comparison against three alternative T2NN fuzzy based models is performed. It is demonstrated that the implemented model yields the same ranking order as the alternative approaches. Sensitivity analysis reveals that changing criteria weightings does not affect the ranking order

Multi criteria risk analysis of a subsea BOP system

The Subsea blowout preventer (BOP) which is latched to a subsea wellhead is one of several barriers in the well to prevent kicks and blowouts and it is the most important and critical equipment, as it becomes the last line of protection against blowout. The BOP system used in Subsea drilling operations is considered a Safety – Critical System, with a high severity consequence following its failure. Following past offshore blowout incidents such as the most recent Macondo in the Gulf of Mexico, there have been investigations, research, and improvements sought for improved understanding of the BOP system and its operation. This informs the need for a systematic re-evaluation of the Subsea BOP system to understand its associated risk and reliability and identify critical areas/aspects/components. Different risk analysis techniques were surveyed and the Failure modes effect and criticality analysis (FMECA) selected to be used to drive the study in this thesis. This is due to it being a simple proven cost effective process that can add value to the understanding of the behaviours and properties of a system, component, software, function or other. The output of the FMECA can be used to inform or support other key engineering tasks such as redesigning, enhanced qualification and testing activity or maintenance for greater inherent reliability and reduced risk potential. This thesis underscores the application of the FMECA technique to critique associated risk of the Subsea BOP system. System Functional diagrams was developed with boundaries defined, a FMECA were carried out and an initial select list of critical component failure modes identified. The limitations surrounding the confidence of the FMECA failure modes ranking outcome based on Risk priority number (RPN) is presented and potential variations in risk interpretation are discussed. The main contribution in this thesis is an innovative framework utilising Multicriteria decision making (MCDA) analysis techniques with consideration of fuzzy interval data is applied to the Subsea BOP system critical failure modes from the FMECA analysis. It utilised nine criticality assessment criteria deduced from expert consultation to obtain a more reliable ranking of failure modes. The MCDA techniques applied includes the technique for order of Preference for similarity to the Ideal Solution (TOPSIS), Fuzzy TOPSIS, TOPSIS with interval data, and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE). The outcome of the Multi-criteria analysis of the BOP system clearly shows failures of the Wellhead connector, LMRP hydraulic connector and Control system related failure as the Top 3 most critical failure with respect to a well control. The critical failure mode and components outcome from the analysis in this thesis is validated using failure data from industry database and a sensitivity analysis carried out. The importance of maintenance, testing and redundancy to the BOP system criticality was established by the sensitivity analysis. The potential for MCDA to be used for more specific analysis of criteria for a technology was demonstrated. Improper maintenance, inspection, testing (functional and pressure) are critical to the BOP system performance and sustenance of a high reliability level. Material selection and performance of components (seals, flanges, packers, bolts, mechanical body housings) relative to use environment and operational conditions is fundamental to avoiding failure mechanisms occurrence. Also worthy of notice is the contribution of personnel and organisations (by way of procedures to robustness and verification structure to ensure standard expected practices/rules are followed) to failures as seen in the root cause discussion. OEMs, operators and drilling contractors to periodically review operation scenarios relative to BOP system product design through the use of a Failure reporting analysis and corrective action system. This can improve design of monitoring systems, informs requirement for re-qualification of technology and/or next generation designs. Operations personnel are to correctly log in failures in these systems, and responsible Authority to ensure root cause analysis is done to uncover underlying issue initiating and driving failures

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 Comparative Study of Outranking Methods for Multi-Criteria Optimization of Electromechanical Modules

The paper presents a comparative assessment of the procedures for applying two outranking methods in solving multi-criteria optimization tasks. The conducted study compares the fundamental PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) method, which is widely proposed to support selection of the best compromise alternative in multi-criteria tasks, with the newly developed RAZOR (Ranking of Alternatives by Z-score Operation Ratings) method. The paper describes the ranking methods and provides demonstrative numerical examples for existing electromechanical modules. The results of the numerical examples from the conducted multi-criteria optimization on a number of given criteria are presented. The study demonstrated that the calculation procedure in PROMETHEE method demands certain level of preliminary knowledge, but provides fine setting of preferences by the decision-maker. The RAZOR method, on the other hand, demands no preliminary knowledge and it is easier to visualize graphically

Synergetic Application of Multi-Criteria Decision-Making Models to Credit Granting Decision Problems

Although various algorithms have widely been studied for bankruptcy and credit risk prediction, conclusions regarding the best performing method are divergent when using different performance assessment metrics. As a solution to this problem, the present paper suggests the employment of two well-known multiple-criteria decision-making (MCDM) techniques by integrating their preference scores, which can constitute a valuable tool for decision-makers and analysts to choose the prediction model(s) more properly. Thus, selection of the most suitable algorithm will be designed as an MCDM problem that consists of a finite number of performance metrics (criteria) and a finite number of classifiers (alternatives). An experimental study will be performed to provide a more comprehensive assessment regarding the behavior of ten classifiers over credit data evaluated with seven different measures, whereas the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) techniques will be applied to rank the classifiers. The results demonstrate that evaluating the performance with a unique measure may lead to wrong conclusions, while the MCDM methods may give rise to a more consistent analysis. Furthermore, the use of MCDM methods allows the analysts to weight the significance of each performance metric based on the intrinsic characteristics of a given credit granting decision problem

Ranqueamento de sistemas de produtos baseado na avaliação da sustentabilidade do ciclo de vida: tomada de decisão estocástica baseada em múltiplos critérios

Purpose – Life cycle sustainability assessment (LCSA) provides useful and comprehensive information on product system performance. However, it poses several challenges for decision-making process due to (i) multidimensional indicators, (ii) conflicting objectives and (iii) uncertainty associated with the performance assessment. This research proposes an approach able to account uncertain life cycle sustainability performances through multiple criteria decision analysis (MCDA) process to support decision-making.Design/methodology/approach – Our method is structured in three phases: i) assessing the uncertainty of LCSA performances, ii) propagating LCSA uncertainty into MCDA methods and iii) interpreting the stochastic results. The approach is applied on an illustrative case study, ranking four alternatives to biodiesel supply.Findings –The recommendation generated by this approach provides an information about the confidence the decision maker can have in a given result (ranking of solutions) under the form of a probability, providing a better knowledge of the risk (in this case due to the uncertainty of the preferred solution). As such, stochastic results, if appropriately interpreted, provide a measure of the robustness of the rankings generated by MCDA methods, overcoming the limitation of the overconfidence of deterministic rankings.Originality/value – The fundamental contributions of this paper are to (i) integrate LCSA uncertainty into decision-making processes through MCDA approach; (ii) provide a sensitivity analysis about the MCDA method choice, (iii) support decision-makers’ preference choices through a transparent elicitation process and (iv) provide a practical decision-making platform that accounts simultaneously uncertain LCSA performances with stakeholders’ value judgments.Propósito – A avaliação de sustentabilidade do ciclo de vida (LCSA) fornece informações úteis e abrangentes sobre o desempenho de um sistema de produtos. Entretanto, existem alguns desafios associado ao processo de tomada de decisão envolvendo esses resultados: (i) indicadores multidimensionais, (ii) objetivos conflitantes e (iii) incerteza associada à avaliação de desempenho. Esta pesquisa propõe uma abordagem que considera a incerteza do desempenho em termos de sustentabilidade do ciclo de vida através do processo de análise de decisão baseado em múltiplos critérios (MCDA) para apoiar a tomada de decisão.Metodologia – Nosso método está estruturado em três fases: i) avaliação da incerteza do desempenho obtido por meio da LCSA, ii) propagação da incerteza da LCSA nos métodos MCDA e iii) interpretação dos resultados estocásticos. A abordagem foi aplicada em um estudo de caso ilustrativo, classificando quatro alternativas de fornecimento de biodiesel.Resultados –  A recomendação gerada por esta abordagem fornece uma informação sobre a confiança que o tomador de decisão pode ter em um determinado resultado (classificação de soluções) sob a forma de uma probabilidade, proporcionando um melhor conhecimento do risco (neste caso devido à incerteza da solução preferida). Assim, os resultados estocásticos, se interpretados de forma adequada, fornecem uma medida da robustez dos rankings gerados pelos métodos MCDA, superando a limitação do excesso de confiança dos rankings determinísticos.Originalidade – As contribuições fundamentais deste artigo são (i) integrar a incerteza da LCSA nos processos de tomada de decisão por meio da abordagem MCDA; (ii) fornecer uma análise de sensibilidade sobre a escolha do método MCDA, (iii) apoiar as escolhas de preferência dos tomadores de decisão por meio de um processo de elicitação transparente e (iv) fornecer uma plataforma de tomada de decisão prática que contabiliza simultaneamente os desempenhos das performances LCSA incertas com julgamentos de valor das partes interessadas