Search

The United Kingdom has one of the poorest lung cancer survival rates in Europe. In this study, to help design and evaluate a single lung cancer pathway (SCP) for Wales, existing diagnostic pathways and processes have been mapped and then modelled with a discrete event simulation. The validated models have been used to provide key performance indicators and to examine different diagnostic testing strategies. Under the current diagnostic pathways, the mean time to treatment was 72 days for surgery patients, 56 days for chemotherapy patients, and 61 days for radiotherapy patients. Our research demonstrated that by ensuring that the patient attends their first outpatient appointment within 7 days and streamlining the diagnostic tests would have the potential to remove approximately 11 days from the current lung cancer pathway resulting in a 21% increase in patients receiving treatment within the Welsh Government set target of 62 days. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that makes use of continuous-time Markov chains and regret functions to find an appropriate compromise in the context of multicriteria decision analysis (MCDA). This method was an innovation in the relationship between uncertainty and decision parameters, and it allows for a much more robust sensitivity analysis. The proposed approach avoids the drawbacks of arbitrary user-defined and method-specific parameters by defining transition rates that depend only upon the performances of the alternatives. This results in a flexible and easy-to-use tool that is completely transparent, reproducible, and easy to interpret. Furthermore, because it is based on Markov chains, the model allows for a seamless and innovative treatment of uncertainty. We apply the approach to an oil and gas decommissioning problem, which seeks a responsible manner in which to dismantle and deactivate production facilities. The experiments, which make use of published data on the decommissioning of the field of Brent, account for 12 criteria and illustrate the application of the proposed approach. [ABSTRACT FROM AUTHOR]

Reinfection and multiple viral strains are among the latest challenges in the current COVID-19 pandemic. In contrast, epidemic models often consider a single strain and perennial immunity. To bridge this gap, we present a new epidemic model that simultaneously considers multiple viral strains and reinfection due to waning immunity. The model is general, applies to any viral disease and includes an optimal control formulation to seek a trade-off between the societal and economic costs of mitigation. We validate the model, with and without mitigation, in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil. The model can derive optimal mitigation strategies for any number of viral strains, whilst also evaluating the effect of distinct mitigation costs on the infection levels. The results show that relaxations in the mitigation measures cause a rapid increase in the number of cases, and therefore demand more restrictive measures in the future. [ABSTRACT FROM AUTHOR]

This work proposes a novel framework for planning the capacity of diagnostic tests in cancer pathways that considers the aggregate demand of referrals from multiple cancer specialties (sites). The framework includes an analytic tool that recursively assesses the overall daily demand for each diagnostic test and considers general distributions for both the incoming cancer referrals and the number of required specific tests for any given patient. By disaggregating the problem with respect to each diagnostic test, we are able to model the system as a perishable inventory problem that can be solved by means of generalized G/D/C queuing models, where the capacity |$C$| is allowed to vary and can be seen as a random variable that is adjusted according to prescribed performance measures. The approach aims to provide public health and cancer services with recommendations to align capacity and demand for cancer diagnostic tests effectively and efficiently. Our case study illustrates the applicability of our methods on lung cancer referrals from UK's National Health Service. [ABSTRACT FROM AUTHOR]

Mining is an important economic activity and a highly complex industry. As such, it demands a complex supply chain to connect mines to clients, often involving railways, ports and long-distance maritime shipping. State-of-the-art optimization tools are an invaluable asset to help manage such a complex environment, which makes mining industry a very fertile ground for operational research applications. This paper aims to present a bibliographical review of published works involving operational research applications in the mining industry. We start by mapping applications within each isolated link of the chain. Then, we make inroads into the researches involving and connecting multiple links of the mining chain. Finally, we present summaries of our finding and pinpoint some directions for research opportunities in the mining industry. [ABSTRACT FROM AUTHOR]

The Brazilian oil and gas (O&G) sector has huge potential to grow in the next few decades. Changes made by the Petroleum Law established the Brazilian Bid Round system to offer exploratory areas and to grant exploration and production (E&P) licences, resulting in the expansion of investment in E&P. Due to Petrobras' monopoly in the E&P sector until 1998, only a small portion of Brazil's sedimentary basins (SBs) have been explored, leaving a large amount of exploratory data to be acquired in poorly explored areas, where little knowledge about hydrocarbon potential is available. This article proposes a model to analyse Brazilian O&G policy decisions, based on historical data and Markov Chains. The model aims to support E&P area offers, taking into account the consequences of the demand for E&P goods and services. We apply the proposed model to Brazilian Bid Round contracts, and derive the optimal interval between Bid Rounds for SBs. The results suggest that the upstream sector in Brazil is impaired by the absence of a regular schedule for Bid Rounds, and will be improved with more predictability in exploratory offers. [ABSTRACT FROM AUTHOR]

The United Kingdom has one of the poorest lung cancer survival rates in Europe. In this study, to help design and evaluate a single lung cancer pathway (SCP) for Wales, existing diagnostic pathways and processes have been mapped and then modelled with a discrete event simulation. The validated models have been used to provide key performance indicators and to examine different diagnostic testing strategies. Under the current diagnostic pathways, the mean time to treatment was 72 days for surgery patients, 56 days for chemotherapy patients, and 61 days for radiotherapy patients. Our research demonstrated that by ensuring that the patient attends their first outpatient appointment within 7 days and streamlining the diagnostic tests would have the potential to remove approximately 11 days from the current lung cancer pathway resulting in a 21% increase in patients receiving treatment within the Welsh Government set target of 62 days., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Background: UK's National Health Service (NHS) has one of the poorest lung cancer survival rates in Europe. To improve patient outcomes, a single cancer pathway was introduced in the NHS. In this study, a Discrete Event Simulation was developed to understand bottlenecks during lung cancer treatment., Methods: This study focused on the lung cancer diagnostic pathways at two Welsh hospitals. Discrete Event Simulation is a computer-based method that has been effectively used in demand and capacity planning. In this study, simulation models were developed for the current and proposed single cancer pathways. The validated models were used to provide Key Performance Indicators. Several "what-if" scenarios were considered for the current and proposed pathways., Results: Under the current diagnostic pathway, the mean time to treatment for a surgery patient was 68 days at the Royal Glamorgan Hospital and 79 days at Prince Charles Hospital. For chemotherapy patients, the mean time to treatment was 52 days at the Royal Glamorgan Hospital and 57 days at Prince Charles Hospital. For radiotherapy patients, the mean time to treatment was 44 days at Royal Glamorgan Hospital and 54 days at Prince Charles Hospital. Ensuring that the patient attends their first outpatient appointment within 7 days and streamlining the diagnostic tests would have the potential to remove approximately 20 days from the current lung cancer pathway resulting in a 20-25% increase of patients receiving treatment within 62 days. Ensuring that patients begin their treatment within 21 days of diagnosis sees almost all patients comply with the 62-day target., Conclusions: Discrete Event Simulation coupled with a detailed statistical analysis provides a useful decision support tool which can be used to examine the current and proposed lung cancer pathways in terms of time spent on the pathway., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tlcr-20-919). Tracey J. England, Paul R. Harper, Tom Crosby, Daniel Gartner, and Edilson F. Arruda report that they received grants from Cancer Research UK during the conduct of this study. The other authors have no conflicts of interest to declare., (2021 Translational Lung Cancer Research. All rights reserved.)

This work proposes a novel framework for planning the capacity of diagnostic tests in cancer pathways that considers the aggregate demand of referrals from multiple cancer specialties (sites). The framework includes an analytic tool that recursively assesses the overall daily demand for each diagnostic test and considers general distributions for both the incoming cancer referrals and the number of required specific tests for any given patient. By disaggregating the problem with respect to each diagnostic test, we are able to model the system as a perishable inventory problem that can be solved by means of generalized G/D/C queuing models, where the capacity [Formula: see text] is allowed to vary and can be seen as a random variable that is adjusted according to prescribed performance measures. The approach aims to provide public health and cancer services with recommendations to align capacity and demand for cancer diagnostic tests effectively and efficiently. Our case study illustrates the applicability of our methods on lung cancer referrals from UK's National Health Service., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.)