A Mixed Integer Linear Programming for COVID-19 Related Medical Waste Reverse Logistics Network Design

Medical wastes bring probable hazards and public risks if not handled correctly, especially during the covid-19 pandemic. Waste management costs are rising due to continues devastation of covid-19 virus. Increased care and handling processes have been implemented to avoid further spreading the infection while ensuring the proper disposal. Given this, medical waste related to covid-19, including other wastes generated by medical facilities, requires delicate integration into the waste management process thru a reverse logistics network for cost-efficient collection, processing, and transport style. Using AMPL software as solving tool, this paper was designed to utilize a mixed-integer linear programming model of a reverse logistics network to improve medical waste management by identifying the total minimum cost of the waste disposal cycle from Waste generating facilities, treatment facilities, and to the disposal factory. © 2022 ACM.

An Integer Linear Programming for Hospital Bed Scheduling and Capacity Management

COVID-19 has struck the Philippines in December 2019 and has brought great panic to the country's healthcare system. In a short period of time, the number of infected increased exponentially. Hospitals are suddenly filled with patients infected by the virus to the extent that patients wait for hours to days to be admitted. Others die on the road even before finding hospitals that can accommodate them. The hospitals and the country's healthcare system must consider this increasing demand to serve patients fully. Patient planning is commonly used in other countries to maximize bed allocation. A recent study using Bernoulli Distributed Random Variable represents the binary integer program. The approach combines the queuing model and simulation to reduce the patient dismissal rate and increase hospital output. On the other hand, this paper deals with strategic hospital bed capacity optimization using linear integer programming by considering the diverse resources, such as doctors, nurses, beds, and hospital rooms. © 2022 ACM.

Learning from a distance during a pandemic outbreak: Factors affecting students’ acceptance of distance learning during school closures due to COVID-19

Distance learning has become the only solution for learning in the current Covid-19 pandemic outbreak. A more straightforward form of distance learning with the utilization of telepresence and cloud-based productivity tools was apparent in many institutions. The present study investigated this phenomenon and ask, “What factors affect students’ acceptance of distance learning during school closures due to COVID-19?”. An extended Unified Theory of Acceptance and Use of Technology was employed to answer the research question, with 156 students participating in the study. The result revealed that Effort Expectancy (EE) has the biggest effect on students’ acceptance of distance learning during school closures (β=0.372, p<0.001). Additionally, the extended variable of Socia l Presence (SP) was also showing great effects on students' acceptance (β=0.296, p<0.001). However, one of the UTAUT constructs, Facilitating Conditions, was found to have no effect on students' acceptance. Practical implications for schools and distance learning program managers were discussed to provide insight on improving a distance learning program. This study contributes to the body of knowledge on learning technologies as well as on how society, especially in the educational sector, should continue despite the current pandemic crisis. © Italian e-Learning Association.