An approximate reasoning based linguistic multi-criteria group decision-making method

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Team situation awareness measure using semantic utility functions for supporting dynamic decision-making

Team decision-making is a remarkable feature in a complex dynamic decision environment, which can be supported by team situation awareness. In this paper, a team situation awareness measure (TSAM) method using a semantic utility function is proposed. The semantic utility function is used to clarify the semantics of qualitative information expressed in linguistic terms. The individual and team situation awareness are treated as linguistic possibility distributions on the potential decisions in a dynamic decision environment. In the TSAM method, team situation awareness is generated through reasoning and aggregating individual situation awareness based on a multi-level hierarchy mental model of the team. Individual and team mental models are composed of key drivers and significant variables. An illustrative example in telecoms customer churn prediction is given to explain the effectiveness and the main steps of the TSAM method. © 2009 Springer-Verlag

Probability Transform Based on the Ordered Weighted Averaging and Entropy Difference

Dempster-Shafer evidence theory can handle imprecise and unknown information, which has attracted many people. In most cases, the mass function can be translated into the probability, which is useful to expand the applications of the D-S evidence theory. However, how to reasonably transfer the mass function to the probability distribution is still an open issue. Hence, the paper proposed a new probability transform method based on the ordered weighted averaging and entropy difference. The new method calculates weights by ordered weighted averaging, and adds entropy difference as one of the measurement indicators. Then achieved the transformation of the minimum entropy difference by adjusting the parameter r of the weight function. Finally, some numerical examples are given to prove that new method is more reasonable and effective

Managing Incomplete Preference Relations in Decision Making: A Review and Future Trends

In decision making, situations where all experts are able to efficiently express their preferences over all the available options are the exception rather than the rule. Indeed, the above scenario requires all experts to possess a precise or sufficient level of knowledge of the whole problem to tackle, including the ability to discriminate the degree up to which some options are better than others. These assumptions can be seen unrealistic in many decision making situations, especially those involving a large number of alternatives to choose from and/or conflicting and dynamic sources of information. Some methodologies widely adopted in these situations are to discard or to rate more negatively those experts that provide preferences with missing values. However, incomplete information is not equivalent to low quality information, and consequently these methodologies could lead to biased or even bad solutions since useful information might not being taken properly into account in the decision process. Therefore, alternative approaches to manage incomplete preference relations that estimates the missing information in decision making are desirable and possible. This paper presents and analyses methods and processes developed on this area towards the estimation of missing preferences in decision making, and highlights some areas for future research

Development of a fuzzy qualitative risk assessment model applied to construction industry

Dissertação para obtenção do Grau de Doutor em Engenharia IndustrialThe construction industry is plagued by occupational risky situations and poor working conditions. Risk Assessment for Occupational Safety (RAOS) is the first and key step to achieve adequate safety levels, particularly to support decision-making in safety programs. Most construction safety efforts are applied informally under the premise that simply allocating more resources to safety management will improve safety on site. Moreover, there are many traditional methods to address RAOS, but few have been adapted and validated for use in the construction industry, thus producing poor results. The contribution of this dissertation is a qualitative fuzzy RAOS model, tailored for the construction industry, named QRAM (Qualitative Risk Assessment Model). QRAM is based on four dimensions: Safety Climate Adequacy, (work accidents) Severity Factors, (work accidents) Possibility Factors and Safety Barriers Effectiveness. The risk assessment is based on real data collected by observation of reality, interviews with workers, foreman and engineers and consultation of site documents (working procedures, reports of work accident investigation, etc.), avoiding the use of data obtained by statistical tecnhiques. To rating each parameter it was defined qualitative evaluators - linguistic variables - which allow to perform a user-friendly knowledge elicitation. QRAM was, firstly evaluated by “peer” review, with 12 safety experts from Brazil (2), Bulgaria (1), Greece (3), Turkey (3) and Portugal (3), and then, evaluated by comparing QRAM with other RAOS tecnhiques and methods. The safety experts , concluded that: a) QRAM is a versatile tool for occupational safety risk assessment on construction sites; b) the specific checklists for knowledge elicitation are a good decision aid and, c) the use of linguistic variables is a better way to make the risk assessments process more objective and reliable.Fundação para a Ciência e Tecnologia - PhD Scholarship SFRH/BD/39610/200

Safety Culture Monitoring: A Management Approach for Assessing Nuclear Safety Culture Health Performance Utilizing Multiple-Criteria Decision Analysis

Nuclear power plants are among the most technologically complex of all energy facilities. This complexity reflects the precision needed in design, maintenance and operations to harness the energy of the atom safely, reliably and economically. Nuclear energy thus requires consistent, high levels of organizational performance by the highly skilled professionals who operate and maintain nuclear power plants (Nuclear Energy Institute [NEI], 2014, p. 1). A key element for achieving consistent, high levels of performance in a nuclear organization is its safety culture. Nuclear safety culture is for an organization what character and personality is for an individual: a feature that is made visible primarily through behaviors and espoused values. Nuclear safety culture is undergoing constant change. It represents the collective behaviors of the organization, which change as the organization and its members change and apply themselves to their daily activities. As problems arise, the organization learns from them. Successes and failures become ingrained in the organization’s nuclear safety culture and form the basis on which the organization conducts business. These behaviors are taught to new members of the organization as the correct way to perceive, think, act and feel (NEI, 2014, p. 1). Nuclear Safety Culture (NSC) is defined as the core values and behaviors resulting from a collective commitment by leaders and individuals to emphasize safety over competing goals to ensure protection of people and the environment (Institute of Nuclear Power Operations [INPO], 2012a, p. iv). Thus, nuclear safety culture depends on every employee, from the board of directors, to the control room operator, to the field technician in the switchyard, to the security officers and to contractors on site. That is, nuclear safety culture is affected by everything we say and everything we do. Nuclear safety is a collective responsibility meaning no one in the organization is exempt from the obligation to ensure nuclear safety first (NEI, 2014, p. 1). Furthermore, NSC is a leadership responsibility. Leaders reinforce safety culture at every opportunity so that the health of safety culture is not taken for granted. Leaders frequently measure the health of safety culture with a focus on trends rather than absolute values. Leaders communicate what constitutes a healthy safety culture and ensure everyone understands his or her role in its promotion. Leaders recognize that safety culture is not all or nothing but is, rather, constantly moving along a continuum. As a result, there is a comfort in discussing safety culture within the organization as well as with outside groups, such as regulatory agencies (INPO, 2012a). That is, NSC like everything else rises and falls based on leadership (Maxwell, 1998). In order to facilitate a healthy NSC, which is the sine qua non of safe nuclear plant operation, the leadership team needs to understand its present health in order to address NSC issues. It has been said “To manage risk, one has first to comprehend it” (Gheorghe, 2005, p. xvii). Equally true, in order to manage the nuclear safety culture of an organization we must first comprehend it. The goal of this research is to provide an ongoing holistic, objective, transparent and safety-focused process to identify early indications of potential problems linked to culture. The process uses a cross-section of available data (e.g., the corrective action program, performance trends, NRC inspections, industry evaluations, nuclear safety culture assessments, self-assessments, audits, operating experience, workforce issues and employee concerns program and other process inputs). These data are then analyzed utilizing Multiple-criteria Decision Analysis (MCDA) methodology that incorporates belief degrees of the management team leading to insights about its meaning which may lead directly to corrective actions

Fuzzy Sets, Fuzzy Logic and Their Applications

The present book contains 20 articles collected from amongst the 53 total submitted manuscripts for the Special Issue “Fuzzy Sets, Fuzzy Loigic and Their Applications” of the MDPI journal Mathematics. The articles, which appear in the book in the series in which they were accepted, published in Volumes 7 (2019) and 8 (2020) of the journal, cover a wide range of topics connected to the theory and applications of fuzzy systems and their extensions and generalizations. This range includes, among others, management of the uncertainty in a fuzzy environment; fuzzy assessment methods of human-machine performance; fuzzy graphs; fuzzy topological and convergence spaces; bipolar fuzzy relations; type-2 fuzzy; and intuitionistic, interval-valued, complex, picture, and Pythagorean fuzzy sets, soft sets and algebras, etc. The applications presented are oriented to finance, fuzzy analytic hierarchy, green supply chain industries, smart health practice, and hotel selection. This wide range of topics makes the book interesting for all those working in the wider area of Fuzzy sets and systems and of fuzzy logic and for those who have the proper mathematical background who wish to become familiar with recent advances in fuzzy mathematics, which has entered to almost all sectors of human life and activity

Advanced system engineering approaches to dynamic modelling of human factors and system safety in sociotechnical systems

Sociotechnical systems (STSs) indicate complex operational processes composed of interactive and dependent social elements, organizational and human activities. This research work seeks to fill some important knowledge gaps in system safety performance and human factors analysis using in STSs. First, an in-depth critical analysis is conducted to explore state-of-the-art findings, needs, gaps, key challenges, and research opportunities in human reliability and factors analysis (HR&FA). Accordingly, a risk model is developed to capture the dynamic nature of different systems failures and integrated them into system safety barriers under uncertainty as per Safety-I paradigm. This is followed by proposing a novel dynamic human-factor risk model tailored for assessing system safety in STSs based on Safety-II concepts. This work is extended to further explore system safety using Performance Shaping Factors (PSFs) by proposing a systematic approach to identify PSFs and quantify their importance level and influence on the performance of sociotechnical systems’ functions. Finally, a systematic review is conducted to provide a holistic profile of HR&FA in complex STSs with a deep focus on revealing the contribution of artificial intelligence and expert systems over HR&FA in complex systems. The findings reveal that proposed models can effectively address critical challenges associated with system safety and human factors quantification. It also trues about uncertainty characterization using the proposed models. Furthermore, the proposed advanced probabilistic model can better model evolving dependencies among system safety performance factors. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance. This research work would help better understand, analyze, and improve the system safety and human factors performance in complex sociotechnical systems

A novel engineering framework for risk assessment of Mobile Offshore Drilling Units

Natural oil and gas has become one of mankind’s most fundamental resources. Hence, the performance of mobile offshore drilling units (MODUs) under various conditions has received considerable attention. MODUs are designed, constructed, operated, and managed for harsh geographical environments, thus they are unavoidably exposed to a wide range of uncertain threats and hazards. Ensuring the operational safety of an MODU’s system is often a complex problem. The system faces hazards from many different sources which dynamically threaten its integrity and can cause catastrophic consequences at time of failure. The main purpose of this thesis is to propose a methodology to improve the current procedures used in the risk assessment of MODUs. The aim is to prevent a critical event from occurring during drilling rather than on measures that mitigate the consequences once the undesirable event has occurred. A conceptual framework has been developed in this thesis to identify a range of hazards associated with normal operational activities and rank them in order to reduce the risks of the MODU. The proposed methodology provides a rational and systematic approach to an MODU’s risk assessment; a comprehensive model is suggested to take into consideration different influences of each hazard group (HG) of an offshore system. The Fuzzy- analytic hierarchy process (AHP) is used to determine the weights of each HG. Fault tree analysis (FTA) is used to identify basic causes and their logical relationships leading to the undesired events and to calculate the probability of occurrence of each undesirable event in an MODU system. The BBN technique is used to express the causal relationships between variables in order to predict and update the occurrence probability of each undesirable event when any new evidence becomes available. Finally, an integrated Fuzzy multiple criteria decision making (MCDM) model based on the Fuzzy-AHP and a Fuzzy techniques for order preference by similarity to an ideal solution (TOPSIS) is developed to offer decision support that can help the Decision maker to set priorities for controlling the risk and improving the MODU’s safety level. All the developed models have been tested and demonstrated with case studies. An MODU’s drilling failure due to its operational scenario has been investigated and focus has been on the mud circulation system including the blowout preventer (BOP)

A contribution to the ranking and description of classifications

This thesis presents a novel and complete fuzzy multi-criteria decision making (MCDM) methodology. This methodology is specifically designed for selecting classifications in the framework of unsupervised learning systems. The main results obtained are twofold. On the one hand, the definition of fuzzy criteria to be used to assess the suitability of a set of given classifications and, on the other hand, the design and development of a natural language generation (NLG) system to qualitatively describe them. Unsupervised learning systems often produce a large number of possible classifications. In order to select the most suitable one, a set of criteria is usually defined and applied sequentially to assess and filter the obtained classifications. This is done, in general, by using a true-false decision in the application of each criterion. This approach could result in classifications being discarded and not taken into account when they marginally fail to meet one particular criterion even though they meet other criteria with a high score. An alternative solution to this sequential approach has been introduced in this thesis. It consists of evaluating the degree up to which each fuzzy criterion is met by each classification and, only after this, aggregating for each classification the individual assessments. This overall value reflects the degree up to which the set of criteria is globally satisfied by each classification. Five fuzzy criteria are defined and analysed to be used collectively to evaluate classifications. The corresponding single evaluations are then proposed to be aggregated into a collective one by means of an Ordered Weighted Averaging (OWA) operator guided by a fuzzy linguistic quantifier, which is used to implement the concept of fuzzy majority in the selection process. In addition, a NLG system to qualitatively describe the most important characteristics of the best classification is designed and developed in order to fully understand the chosen classification. Finally, this new methodology is applied to a real business problem in a marketing context. The main purpose of this application is to show how the proposed methodology can help marketing experts in the design of specific-oriented marketing strategies by means of an automatic and interpretable segmentation system