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

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

Prioritization of patients' access to health care services

L'accès aux services de santé et les longs délais d'attente sont l’un des principaux problèmes dans la plupart des pays du monde, dont le Canada et les États-Unis. Les organismes de soins de santé ne peuvent pas augmenter leurs ressources limitées, ni traiter tous les patients simultanément. C'est pourquoi une attention particulière doit être portée à la priorisation d'accès des patients aux services, afin d’optimiser l’utilisation de ces ressources limitées et d’assurer la sécurité des patients. En fait, la priorisation des patients est une pratique essentielle, mais oubliée dans les systèmes de soins de santé à l'échelle internationale. Les principales problématiques que l’on retrouve dans la priorisation des patients sont: la prise en considération de plusieurs critères conflictuels, les données incomplètes et imprécises, les risques associés qui peuvent menacer la vie des patients durant leur mise sur les listes d'attente, les incertitudes présentes dans les décisions des cliniciens et patients, impliquant l'opinion des groupes de décideurs, et le comportement dynamique du système. La priorisation inappropriée des patients en attente de traitement a une incidence directe sur l’inefficacité des prestations de soins de santé, la qualité des soins, et surtout sur la sécurité des patients et leur satisfaction. Inspirés par ces faits, dans cette thèse, nous proposons de nouveaux cadres hybrides pour prioriser les patients en abordant un certain nombre de principales lacunes aux méthodes proposées et utilisées dans la littérature et dans la pratique. Plus précisément, nous considérons tout d'abord la prise de décision collective incluant les multiples critères de priorité, le degré d'importance de chacun de ces critères et de leurs interdépendances dans la procédure d'établissement des priorités pour la priorisation des patients. Puis, nous travaillons sur l'implication des risques associés et des incertitudes présentes dans la procédure de priorisation, dans le but d'améliorer la sécurité des patients. Enfin, nous présentons un cadre global en se concentrant sur tous les aspects mentionnés précédemment, ainsi que l'implication des patients dans la priorisation, et la considération des aspects dynamiques du système dans la priorisation. À travers l'application du cadre global proposé dans le service de chirurgie orthopédique à l'hôpital universitaire de Shohada, et dans un programme clinique de communication augmentative et alternative appelé PACEC à l'Institut de réadaptation en déficience physique de Québec (IRDPQ), nous montrons l'efficacité de nos approches en les comparant avec celles actuellement utilisées. Les résultats prouvent que ce cadre peut être adopté facilement et efficacement dans différents organismes de santé. Notamment, les cliniciens qui ont participé à l'étude ont conclu que le cadre produit une priorisation précise et fiable qui est plus efficace que la méthode de priorisation actuellement utilisée. En résumé, les résultats de cette thèse pourraient être bénéfiques pour les professionnels de la santé afin de les aider à: i) évaluer la priorité des patients plus facilement et précisément, ii) déterminer les politiques et les lignes directrices pour la priorisation et planification des patients, iii) gérer les listes d'attente plus adéquatement, vi) diminuer le temps nécessaire pour la priorisation des patients, v) accroître l'équité et la justice entre les patients, vi) diminuer les risques associés à l’attente sur les listes pour les patients, vii) envisager l'opinion de groupe de décideurs dans la procédure de priorisation pour éviter les biais possibles dans la prise de décision, viii) impliquer les patients et leurs familles dans la procédure de priorisation, ix) gérer les incertitudes présentes dans la procédure de prise de décision, et finalement x) améliorer la qualité des soins.Access to health care services and long waiting times are one of the main issues in most of the countries including Canada and the United States. Health care organizations cannot increase their limited resources nor treat all patients simultaneously. Then, patients’ access to these services should be prioritized in a way that best uses the scarce resources, and to ensure patients’ safety. In fact, patients’ prioritization is an essential but forgotten practice in health care systems internationally. Some challenging aspects in patients’ prioritization problem are: considering multiple conflicting criteria, incomplete and imprecise data, associated risks that threaten patients on waiting lists, uncertainties in clinicians’ decisions, involving a group of decision makers’ opinions, and health system’s dynamic behavior. Inappropriate prioritization of patients waiting for treatment, affects directly on inefficiencies in health care delivery, quality of care, and most importantly on patients’ safety and their satisfaction. Inspired by these facts, in this thesis, we propose novel hybrid frameworks to prioritize patients by addressing a number of main shortcomings of current prioritization methods in the literature and in practice. Specifically, we first consider group decision-making, multiple prioritization criteria, these criteria’s importance weights and their interdependencies in the patients’ prioritization procedure. Then, we work on involving associated risks that threaten patients on waiting lists and handling existing uncertainties in the prioritization procedure with the aim of improving patients’ safety. Finally, we introduce a comprehensive framework focusing on all previously mentioned aspects plus involving patients in the prioritization, and considering dynamic aspects of the system in the patients’ prioritization. Through the application of the proposed comprehensive framework in the orthopedic surgery ward at Shohada University Hospital, and in an augmentative and alternative communication (AAC) clinical program called PACEC at the Institute for Disability Rehabilitation in Physics of Québec (IRDPQ), we show the effectiveness of our approaches comparing the currently used ones. The implementation results prove that this framework could be adopted easily and effectively in different health care organizations. Notably, clinicians that participated in the study concluded that the framework produces a precise and reliable prioritization that is more effective than the currently in use prioritization methods. In brief, the results of this thesis could be beneficial for health care professionals to: i) evaluate patients’ priority more accurately and easily, ii) determine policies and guidelines for patients’ prioritization and scheduling, iii) manage waiting lists properly, vi) decrease the time required for patients’ prioritization, v) increase equity and justice among patients, vi) diminish risks that could threaten patients during waiting time, vii) consider all of the decision makers’ opinions in the prioritization procedure to prevent possible biases in the decision-making procedure, viii) involve patients and their families in the prioritization procedure, ix) handle available uncertainties in the decision-making procedure, and x) increase quality of care

Methodology to predict construction contractors’ performance using non-price measures

Despite being one of the largest industry sectors in the world, construction continues to suffer from underperformance. Contractors are the driving force behind built assets, and selecting high-performing contractors is crucial to the success of construction projects. However, the industry lacks a systematic and purpose-driven method of assessing contractors’ performance using objective metrics. Furthermore, contractors do not have a systematic way to gauge their own performance in the pursuit of continuous improvement. Although there are numerous approaches to the measurement of contractors’ performance, the literature suggests that most are complicated and highly dependent on data that are difficult to attain. The research presented in this thesis addresses this knowledge gap by creating a model for predicting construction contractors’ performance based on directly attributable measures that are quantitatively measurable and easily accessible. The findings of this research make a number of contributions to theory and practice. The developed performance model—the Contractors’ Performance Index (CPIx) provides a performance score based on seven non-price CMoPs. As the CPIx is based on factors that are within the control of the contractor, it provides a fair and independent assessment of performance that is not influenced by other factors. In an industry significantly driven by pricebased decisions that are solely based on non-price measures, the CPIx shifts the focus towards other aspects such as quality, health and safety, sustainability and productivity when evaluating performance, leaving price based measures for commercial considerations. Contractors can use the CPIx to self-evaluate their levels of project and organisational performance. If implemented as a sector-based performance evaluator, it can then be used to develop industry benchmarks for different categories of construction. The CPIx is presented as a prototype mobile application that can be conveniently used by various stakeholders to track performance within the construction industry

Studies on Risk and Occupational Health Hazards in Industrial Context: Some Case Research

This work articulates few case empirical studies on some aspects of risk management and occupational health hazards in the context of Indian Industries. Empirical research is research using empirical evidence. It is a way of gaining knowledge by means of direct and indirect observation or experience. The study focuses on five important domains investigating (i) the interrelationships among critical risk factors associated with software engineering project, (ii) risk management for IT outsourcing, (iii) risk management in metropolitan construction project, (iv) health hazard risk management, and (v) appropriate safety measure system selection for improving workers’ safety in an underground coal mining industry. In this research, an ISM approach has been applied to understand the significant interrelationships among the twenty three identified risk factors associated with the software engineering projects. In relation to IT outsourcing project, a hierarchical risk-breakdown structure has been proposed comprising sixty eight risk influencing factors under eleven risk dimensions. A case study has been conducted in a famous IT sector located at the eastern part of India. An improved fuzzy based decision making approach has been proposed for assessing overall IT outsourcing project risks. The degree of risk of identified risk factors have been shown in crisp values rather than the fuzzy numbers. A logical risk categorization framework has been proposed to categorize the risk factors into different risk levels. A unique action requirement plan has been suggested for effectively controlling the risks towards IT outsourcing project success. In the later part, total twenty one occupational health hazards have been identified and assessed their risk extent based on the exposure assessment procedure. Consequently, a constructive control measure plan has been suggested for different health hazards in view of their risk extent level. A novel risk-based decision making framework has been proposed for selecting the appropriate safety measure system in an underground coal mining industry. In addition to this, a case study has been conducted using twenty potential risk factors associated with five risk dimensions for assessing metropolitan construction project risks. Decision-makers’ risk bearing attitude has also been considered in this study. This study also explores the concept of risk matrix for categorizing the risk factors in different risk levels which would provide guidelines towards controlling risks for enhancing the overall project performance. Risk analysis models delignated herein have been case studied in relation to Indian industries. However, the model or hierarchy of various risk dimensions, risk sources; and classification of health hazards can be applicable to appropriate industries all over the globe. Some alteration may incur depending on the geographic situation of coal mining industry in analyzing occupational health hazards and associated risks. The framework for analyzing risks and occupational health hazards based on fuzzy based decision making approach can be applied in industrial context of different countries. Apart from the case studies mentioned above, the work also proposes a risk based decision support framework for selection of safety measure system for underground coal mines. In this case, occupational risks and alternative safety measure systems have been identified through literature survey. This part is a purely a theoretical formulation followed by analysis of assumed data which has not been case studied in reality. The novelty of the proposed framework is to analyze various risk dimensions in software engineering projects, IT Outsourcing, construction projects; also occupational health hazards in underground coal mining industry in a fuzzy based decision making framework. Instead of exploring historical data, survey report of the company; an experienced decision making group has been appointed to provide subjective judgement in regards of likelihood of occurrence and impact of various risks; consequence of exposure, period of exposure, and probability of exposure of various health hazards. Subjective decision making data have been transformed into appropriate fuzzy number sets to quantify overall risks extent. Thus, the proposed framework provides a platform to quantify extent of risk in industrial context

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"

info:eu-repo/semantics/publishedVersio

Leveraging contextual-cognitive relationships into mobile commerce systems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyMobile smart devices are becoming increasingly important within the on-line purchasing cycle. Thus the requirement for mobile commerce systems to become truly context-aware remains paramount if they are to be effective within the varied situations that mobile users encounter. Where traditionally a recommender system will focus upon the user – item relationship, i.e. what to recommend, in this thesis it is proposed that due to the complexity of mobile user situational profiles the how and when must also be considered for recommendations to be effective. Though non-trivial, it should be, through the understanding of a user’s ability to complete certain cognitive processes, possible to determine the likelihood of engagement and therefore the success of the recommendation. This research undertakes an investigation into physical and modal contexts and presents findings as to their relationships with cognitive processes. Through the introduction of the novel concept, disruptive contexts, situational contexts, including noise, distractions and user activity, are identified as having significant effects upon the relationship between user affective state and cognitive capability. Experimental results demonstrate that by understanding specific cognitive capabilities, e.g. a user’s perception of advert content and user levels of purchase-decision involvement, a system can determine potential user engagement and therefore improve the effectiveness of recommender systems’ performance. A quantitative approach is followed with a reliance upon statistical measures to inform the development, and subsequent validation, of a contextual-cognitive model that was implemented as part of a context-aware system. The development of SiDISense (Situational Decision Involvement Sensing system) demonstrated, through the use of smart-phone sensors and machine learning, that is was viable to classify subjectively rated contexts to then infer levels of cognitive capability and therefore likelihood of positive user engagement. Through this success in furthering the understanding of contextual-cognitive relationships there are novel and significant advances that are now viable within the area of m-commerce

Three Essays in Financial Economics

This dissertation studies three topics in financial economics. In the first chapter, "ESG Investing in Emerging Markets: Betting on Firm Fundamentals or Riding Investor Preferences?", we examine the relation between firms' environmental, social, and governance (ESG) practices and the pricing of corporate bonds in emerging markets, which is an important yet understudied market for ESG-related issues. Firms with different ESG scores can have different costs of capital, either because ESG scores help forecast future cash flows -- the "fundamental" channel -- or because investors have non-pecuniary preferences for high-ESG-score assets -- the "preference" channel. We identify the existence of a preference channel with a natural experiment -- the historical opening of the Chinese onshore bond market -- that leads to an increase in the proportion of international investors, who are more ESG-conscious. Consistent with theory, we find that the bond yield of companies with high ESG scores decreases more than that of companies with low ESG scores. By focusing on firms that also have bonds traded in the offshore market, which, as opposed to the onshore market, does not experience any change in regulation, we can control for issuer-time fixed effects in a triple difference design, hence reducing considerably the influence of the fundamental channel. In the second chapter, "Watch what they do, not what they say: Estimating regulatory costs from revealed preferences", we show that distortion in the size distribution of banks around regulatory thresholds can be used to identify costs of bank regulation. We build a structural model in which banks can strategically bunch their assets below regulatory thresholds to avoid regulations. The resulting distortion in the size distribution of banks reveals the magnitude of regulatory costs. Using U.S. bank data, we estimate the regulatory costs imposed by the Dodd-Frank Act. Although the estimated regulatory costs are substantial, they are significantly lower than those in self-reported estimates by banks. In the third chapter, "Fuzzy Bunching", we introduce a new fuzzy bunching approach that is robust to noise. The existing bunching approach identifies the extent of bunching from a sharp spike in the probability density function. In many finance settings, however, the sharp spike could be diffused by data noise. The key idea behind our fuzzy bunching estimator is to identify bunching from the area of a bulge in the cumulative distribution function. The fuzzy bunching approach also avoids density estimation, which makes it easy to implement in sparse data. We provide the theoretical foundation of this approach and illustrate the advantages by using simulated and real data