ABSTRACT
Efficient, high-quality and competitive urban, suburban and long-distance rail passenger transport is very important and key public passenger transport system that needs to be constantly developed as a whole. However, the demand for public passenger transport has been significantly lower than the long-term trend so far since March 2020, mainly due to the pandemic caused by COVID-19. The situation improved slightly in the summer months of 2020 and 2021, but traffic flows are still not at the required level before the pandemic period. Therefore, it is now necessary to take several measures for the revitalization and conceptual systematic development of rail passenger transport as well. This is necessary, in particular, to re-attract passengers to public passenger transport and also to ensure better and more efficient transport services in the particular regions. This paper analyses the current status of the measures that have been introduced in rail passenger transport in the Slovak Republic during the individual waves of the COVID-19 pandemic. Subsequently there are proposed strategic solutions and long-term systematic measures that can be implemented in pandemic times in order to maintain safe and attractive rail passenger transport. © 2022 Kaunas University of Technology. All rights reserved.
ABSTRACT
The health care system is characterized by limited resources, including the physical facilities as well as skilled human resources. Due to the extensive fixed cost of medical facilities and the high specialization required by the medical staff, the problem of resource scarcity in a health care supply chain is much more acute than in other industries. In the pandemic of the Coronavirus, where medical services are the most important services in communities, and protective and preventive guidelines impose new restrictions on the system, the issue of resource allocation will be more complicated and significantly affect the efficiency of health care systems. In this paper, the problem of activating the operating rooms in hospitals, assigning active operating rooms to the COVID-19 and non-COVID-19 patients, assigning specialty teams to the operating rooms and assigning the elective and emergency patients to the specialty teams, and scheduling their operations is studied by considering the new constraints of protective and preventive guidelines of the Coronavirus. To address these issues, a mixed-integer mathematical programming model is proposed. Moreover, to consider the uncertainty in the surgery duration of elective and emergency patients, the stochastic robust optimization approach is utilized. The proposed model is applied for the planning of operating rooms in the cardiovascular department of a hospital in Iran, and the results highlight the role of proper management in supplying sufficient medical resources effectively to respond to patients and scheduled surgical team to overcome the pressure on hospital resources and medical staff results from pandemic conditions.
ABSTRACT
A real-life problem is the rostering of nurses at hospitals. It is a famous nondeterministic, polynomial time (NP) -hard combinatorial optimization problem. Handling the real-world nurse rostering problem (NRP) constraints in distributing workload equally between available nurses is still a difficult task to achieve. The international shortage of nurses, in addition to the spread of COVID-19, has made it more difficult to provide convenient rosters for nurses. Based on the literature, heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity, especially for large rosters. Heuristic-based algorithms in general have problems striking the balance between diversification and intensification. Therefore, this paper aims to introduce a novel metaheuristic hybridization that combines the enhanced harmony search algorithm (EHSA) with the simulated annealing (SA) algorithm called the annealing harmony search algorithm (AHSA). The AHSA is used to solve NRP from a Malaysian hospital. The AHSA performance is compared to the EHSA, climbing harmony search algorithm (CHSA), deluge harmony search algorithm (DHSA), and harmony annealing search algorithm (HAS). The results show that the AHSA performs better than the other compared algorithms for all the tested instances where the best ever results reported for the UKMMC dataset. © 2022 Tech Science Press. All rights reserved.
ABSTRACT
Educational timetabling is a fundamental problem impacting schools and universities’ effective operation in many aspects. Different priorities for constraints in different educational institutions result in the scarcity of universal approaches to the problems. Recently, COVID-19 crisis causes the transformation of traditional classroom teaching protocols, which challenge traditional educational timetabling. Especially for examination timetabling problems, as the major hard constraints change, such as unlimited room capacity, non-invigilator and diverse exam durations, the problem circumstance varies. Based on a scenario of a local university, this research proposes a conceptual model of the online examination timetabling problem and presents a conflict table for constraint handling. A modified Artificial Bee Colony algorithm is applied to the proposed model. The proposed approach is simulated with a real case containing 16,246 exam items covering 9,366 students and 209 courses. The experimental results indicate that the proposed approach can satisfy every hard constraint and minimise the soft constraint violation. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more balanced solutions for the online examination timetabling problems. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
ABSTRACT
In this paper, we consider the changes to mathematics learning support (MLS) at Maynooth University due to the COVID-19 pandemic, including the provision of novel online study groups aimed at increasing student engagement and interaction. We briefly outline the local, national and international impact of COVID-19 on MLS and then focus on the results of a student survey. Respondents who regularly used online MLS were broadly positive about their experiences. They cited, in particular, the influence of tutors and the scheduled study groups, which provided structure and motivation as well as the opportunity to work with others and ask questions in less intimidating small groups. However, some respondents highlighted factors that impacted negatively on their engagement. These included low attendance or interaction from peers, timetabling issues or busy schedules, lack of awareness of the details of the services and increased feelings of discomfort and anxiety in an online environment. We consider how this student feedback may influence our future online and in-person supports. © 2021 The Author(s) 2021. Published by Oxford University Press on behalf of The Institute of Mathematics and its Applications. All rights reserved
ABSTRACT
The COVID-19 pandemic has exposed a paradox in historical models of medical education: organizations responsible for applying consistent standards for progression have needed to adapt to training environments marked by inconsistency and change. Although some institutions have maintained their traditional requirements, others have accelerated their programs to rush nearly graduated trainees to the front lines. One interpretation of the unplanned shortening of the duration of training programs during a crisis is that standards have been lowered. But it is also possible that these trainees were examined according to the same standards as usual and were judged to have already met them. This paper discusses the impacts of the COVID-19 pandemic on the current workforce, provides an analysis of how competency-based medical education (CBME) in the context of the pandemic might have mitigated wide-scale disruption, and identifies structural barriers to achieving an ideal state. The paper further calls upon universities, health centres, governments, certifying bodies, regulatory authorities, and health care professionals to work collectively on a truly time-variable model of CBME. The pandemic has made clear that time variability in medical education already exists and should be adopted widely and formally. If our systems today had used a framework of outcome competencies, sequenced progression, tailored learning, focused instruction, and programmatic assessment, we may have been even more nimble in changing our systems to care for our patients with COVID-19.