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 reviews works that consider the mathematical modelling of mission-abort policies. In a mission-abort policy (MAP), a valuable, and perhaps vulnerable system performs a mission with two, sometimes conflicting objectives, mission success and system survival, and the purpose of modelling is to determine conditions under which a mission should be aborted. Such problems are important in defence, and emerging in transportation and health management. We classify models by: the nature of the mission and the system; the nature of the return or rescue; type of deterioration model; and the decision objectives. We show that the majority of works consider a model of a one system, one target mission in which the mission is aborted once the hazard of failure reaches a critical level and the operating environment is the same for the outbound and inbound parts of the mission. Typically, the hazard of failure depends on the number of shocks received so far. Our analysis indicates that there has been little modelling development for multiple systems that can multi-task and dependent systems with common-cause failures, for example. We find no evidence that MAPs are used in practice and no works reviewed develop software demonstrators. We think there is considerable scope for modelling applications in transportation (e.g. dynamic train re-scheduling, last-mile logistics) and medical treatments, and MAPs may be more general than the literature that we have reviewed suggests.
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