A bi-objective optimization model for technology selection and donor’s assignment in the blood supply chain

Los procesos de toma de decisiones suponen frecuentemente más de un objetivo. En el caso de la selección de tecnologías en procesos de captación de productos sanguíneos, están en conflicto los costos de recolección asociados a la tecnología utilizada y la cantidad de donantes requeridos para la satisfacción de la demanda. De igual forma, en la cadena de suministro de sangre  este tipo de decisiones se tornan más complejas cuando se consideran las características propias del sistema, como proporcionalidad de tipos de sangre y compatibilidad entre productos. Para dar solución a este problema se propone un modelo de programación lineal entera que contiene como objetivos la minimización del costo total y del número de donantes. Este modelo está sujeto a restricciones de capacidad, proporcionalidad de tipos de sangre y satisfacción de demanda entre otras. Para la solución del modelo se utilizó Open Solver 2.1 y para la generación de las soluciones eficientes que conforman el frente de Pareto se implementó en VBA el algoritmo épsilon restricciones aumentado.Decision-making processes often contain more than one objective. In technology selection in the blood collection processes, the cost related to the collection technology and the amount of donors required to meet the demand are in conflict. In the same way, in the blood supply chains decisions become more complex when features of the system such as blood type proportions and compatibilities are considered. In order to generate solutions to this problem, an Integer Linear Programming is proposed considering total cost minimisation and amount of donors required. This model also considers distinct constraints such as capacity, proportionality, and demand fulfilment among others. Open Solver 2.1 was used to solve this problem in combination with Visual Basic for Applications for generating the set of efficient solutions that make up the Pareto front through the augmented Epsilon constraint algorithm

The agency role of simulation models in model-building groups

Simulation models, in particular System Dynamics (SD) models, can be used in a group modelling setting to communicate, integrate, learn, collaborate, organize knowledge and derive new insights. Such models can play the roles of conceptual integrators, representations, learning or predictive tools. In this ethnographic study of two in-depth SD group modelling projects we discovered that SD models can be active agents in the group-model building process by initiating cognitive transition on participants’ (model and case based) modes of reasoning. We found that the cognitive transition was achieved through a series of surprises or shocks that refuted participants’ prior conceptions and forced them to switch between case-based and model-based reasoning during the model-building process. Based on these insights, we present a framework that describes how simulation models change the mode of reasoning in group modelling project and explains the model’s agency role. The study addresses the calls from earlier OR articles to contribute with more case studies using an ethnographic method looking into simulation artefact agency

Personalised Ambient Monitoring (PAM) for People with Bipolar Disorder

This paper presents the architecture and preliminary trial results of a monitoring system for patients with bipolar disorder containing environmental and wearable sensors

Facilitating the analysis of a UK national blood service supply chain using distributed simulation

In an attempt to investigate blood unit ordering policies, researchers have created a discrete-event model of the UK National Blood Service (NBS) supply chain in the Southampton area of the UK. The model has been created using Simul8, a commercial-off-the-shelf discrete-event simulation package (CSP). However, as more hospitals were added to the model, it was discovered that the length of time needed to perform a single simulation severely increased. It has been claimed that distributed simulation, a technique that uses the resources of many computers to execute a simulation model, can reduce simulation runtime. Further, an emerging standardized approach exists that supports distributed simulation with CSPs. These CSP Interoperability (CSPI) standards are compatible with the IEEE 1516 standard The High Level Architecture, the defacto interoperability standard for distributed simulation. To investigate if distributed simulation can reduce the execution time of NBS supply chain simulation, this paper presents experiences of creating a distributed version of the CSP Simul8 according to the CSPI/HLA standards. It shows that the distributed version of the simulation does indeed run faster when the model reaches a certain size. Further, we argue that understanding the relationship of model features is key to performance. This is illustrated by experimentation with two different protocols implementations (using Time Advance Request (TAR) and Next Event Request (NER)). Our contribution is therefore the demonstration that distributed simulation is a useful technique in the timely execution of supply chains of this type and that careful analysis of model features can further increase performance

Modelling and simulation techniques for supporting healthcare decision making : a selection framework

The development of this workbook has been led by a team of researchers from five UK universities with a grant from the UK Engineering and Physical Sciences Research Council (EPSRC). They are investigating the use of modelling and simulation in healthcare as part of the RIGHT (Research Into Global Healthcare Tools) project. The workbook was developed following an extensive review of literature on the application of modelling and simulation in healthcare and other safety- critical industries, supplemented by the team’s extensive expertise of modelling and simulation in healthcare. In order to produce this summary guide, thousands of articles were categorised according to the techniques used, when they were used, and with what resources.peer-reviewe

Introduction to the special issue : management science in the fight against Covid-19

At the time of writing of this Editorial in April 2021, Covid-19 continues to ravage our planet, with an official global death toll now exceeding three million, and a horrendous legacy of economic and human damage. The roll-out of vaccination has given hope that we will soon reach the end of this chapter of history. However, it will take years for the world to overcome this calamity and many individuals whose health or livelihoods have been destroyed will never fully recover. This failure of the world to effectively respond to the challenge of Covid-19 is all the more bitter because the outbreak of a novel pathogen was entirely predictable; the spread, preventable; and the suffering, avoidable. The experience of different countries around the world shows that the ability to plan, and to execute plans in a disciplined fashion, can make all the difference between relative security and catastrophe. The challenge for Management Scientists is to show that our discipline can have a role – a critical role – as a part of this planning. Epidemiological models of disease dynamics have been prominent through this crisis but do not fully capture the constraints in the health system and cannot directly support many of the management decisions which have to be made as part of the response. As Management Scientists, our perspective and our modelling tools have the potential to address those shortcomings; but if our profession cannot demonstrate our ability to add value, others will do so in our place

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication