An inspection and replacement policy for a protection system is described by a mathematical model that incorporates multiple aspects of maintenance quality. A three-state component failure model is assumed, with a defective state preceding failure. The quality of maintenance intervention is modelled by supposing that inspections may misclassify defects (false positives and false negatives) and further that an inspection may induce a defect. The quality of replacement is modelled by supposing that a component arises from a heterogeneous population, composed of weak and strong items and with the mixing parameter determining quality. Isolation valves used in water distribution systems motivate the model development, and a case study is considered in this context. We evaluate the impact of these aspects of the quality of maintenance upon cost and production losses. Defect induction is found to be a key determinant of the cost-optimal policy. The proposed model allows us to verify conditions that justify investment in higher quality maintenance, and thus to provide guidance for prioritization of this investment.
This paper puts forward a decision model for allocation of intensive care unit (ICU) beds under scarce resources in healthcare systems during the COVID-19 pandemic. The model is built upon a portfolio selection approach under the concepts of the Utility Theory. A binary integer optimization model is developed in order to find the best allocation for ICU beds, considering candidate patients with suspected/confirmed COVID-19. Experts’ subjective knowledge and prior probabilities are considered to estimate the input data for the proposed model, considering the particular aspects of the decision problem. Since the chances of survival of patients in several scenarios may not be precisely defined due to the inherent subjectivity of such kinds of information, the proposed model works based on imprecise information provided by users. A Monte-Carlo simulation is performed to build a recommendation, and a robustness index is computed for each alternative according to its performance as evidenced by the results of the simulation.
In this paper, a utility-based multicriteria model is proposed to support the physicians to deal with an important medical decision—the screening decision problem—given the squeeze put on resources due to the COVID-19 pandemic. Since the COVID-19 emerged, the number of patients with an acute respiratory failure has increased in the health units. This chaotic situation has led to a deficiency in health resources. Thus, this study, using the concepts of the multiattribute utility theory (MAUT), puts forward a mathematical model to aid physicians in the screening decision problem. The model is used to generate which of the three alternatives is the best one for where patients with suspected COVID-19 should be treated, namely, an intensive care unit (ICU), a hospital ward, or at home in isolation. Also, a decision information system, called SIDTriagem, is constructed and illustrated to operate the mathematical model proposed.
The COVID-19 pandemic has brought health systems to the brink of collapse in several regions around the world, as the demand for health care has outstripped the capacity of their services, especially regarding intensive care. In this context, health system managers have faced a difficult question: who should be admitted to an intensive care unit (ICU), and who should not? This paper addresses this decision problem using Expected Utility Theory and Bayesian decision analysis. In order to estimate the chances of survival for patients, a structured protocol has been proposed conjointly with physicians, based on the Sequential Organ Failure Assessment (SOFA) score. A portfolio selection approach is proposed to support tackling the ICU allocation problem. A simulation study shows that the proposed approach is more advantageous than other approaches already presented in the literature, with respect to the number of lives saved. The patients’ probabilities of survival inside and outside the ICU are important parameters of the model. However, assessing such probabilities can be a difficult task for health professionals. In order to give due treatment to the imprecise information regarding these probabilities, a Monte Carlo simulation is used to estimate the probabilities of recommending a patient be admitted to the ICU is the most appropriate decision, given the conditions presented. The methodology was implemented in an Information and Decision System called SIDTriagem, which is available online for free. With regards to managerial implications, SIDTriagem has a great potential to help in the response to public health emergencies systems as it facilitates rational decision-making regarding allocating ICU beds when resources are scarce.
Understanding the factors that affect the quality of maintenance tasks is critical to ensure an effective maintenance program. In this paper, we present a comprehensive framework to evaluate de impact of human and environmental/managerial factors on inspection quality, specifically regarding the probability of defect induction by this kind of activity that usually has solely informative purposes. We discuss the influence of Disruptive External Events (DEEs) on the probability of human error. A DEE can disrupt appropriate conditions for performing inspection tasks, making maintenance staff more subject to errors that can result in defect induction. We present a delay-time based mathematical model for an inspection-maintenance policy applied to a non-repairable single-component technical system, incorporating to the model a probability of defect induction at inspections that may vary according to parameters related to DEEs and human reliability. We investigate how likely a defect induction can be depending on the capacity of the team to recover proper conditions to more reliable inspections. An important finding concerning the induction of defects calls attention to the need to invest in more appropriate conditions to carry out interventions on the system. The results obtained highlight the importance of a deeper investigation integrating technical aspects of the systems with relevant subjects from areas related to human behavior and social sciences.
This paper proposes a multicriteria model based on the delay time concept and the FITradeoff method to support the choice of a service provider who inspects isolation valves, thereby enabling maintenance actions that have a strategic focus to be established for natural gas industries. We consider the possibility of errors of judgment (false negatives), thus allowing an approximation of reality to be made. In addition, the FITradeoff method is used to enable a decision‐maker to aggregate his/her preferences by taking account of more than one criterion simultaneously, even though the criteria generally conflict with each other. Furthermore, we undertake a numerical application to assess the practical implications. The model proved to be effective at supporting decision‐makers to select companies to provide periodic inspection services, which are often carried out under contracts. We have also evaluated how maintenance decisions change due to different decision‐makers' standards so as to obtain more appropriate procedures for maintenance actions. This has an impact on reducing the occurrence of failures and, consequently, reducing risks to people who live near the gas pipelines.
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