ABSTRACT
The lockdowns implemented in most countries in response to the COVID-19 pandemic led to the need for improvisation in the delivery of higher education. These measures involved the widespread use of remote learning platforms coupled with reconfiguring content and pedagogy to suit an electronic environment. While many of these emergency innovations may be abandoned once the pandemic ends, some lessons can be drawn from the experience and adapted for use in the post-COVID-19 world. This work describes the delivery of the module entitled “Optimization in Chemical Engineering” to postgraduate (masters and doctorate) students at De La Salle University in the first half of 2021. We emphasized model building skills through a series of structured exercises using case studies from literature. Mathematical foundations and software proficiency were initially taught separately in a parallel track. The final term project and oral exam were designed to combine the skills learned and to further emphasize the role of practical engineering interpretation of model outputs. In addition, students were required to work in pairs to address both mental health and invigilation issues. We also discuss the prospects for the use of this approach in face-to-face and hybrid delivery for both postgraduate curriculum and continuing education for professional engineers. Copyright © 2022, AIDIC Servizi S.r.l.
ABSTRACT
The COVID-19 pandemic posed a huge impact to the global economy and on the environment. Recent developments in battling the pandemic include the availability of vaccines for public use. The distribution of vaccines requires systematic planning as supplies to a given region can be limited. Process integration techniques such as mathematical programming can be applied to develop solutions for systematic planning of COVID-19 vaccine distribution. To enable the use of mathematical programs for policymakers, a graphical user interface is needed. In this study, a web-based application (app) for allocating vaccines is presented, utilizing non-linear programs to optimally distribute vaccines to different population groups. Two models are incorporated in the app: (1) a model that minimizes the number of vaccines required to slow down the spread of the disease, i.e., reduce the reproductive number to less than one and (2) a model that determines the optimal allocation at the minimum reproductive number. The optimization models for the app were written in Python using the Pyomo package while the graphical user interface is written in JavaScript with the use of the React library. The optimization models are accessed through an API served using a Python web server utilizing the web framework Flask. A case study is used to illustrate how the models work in the web application. © 2021, AIDIC Servizi S.r.l.
ABSTRACT
The global scientific community has been successful in their efforts to develop, test, and commercialize vaccines for COVID-19. However, the limited supply of these vaccines remains to be a widespread problem as different nations have started their respective vaccine rollouts. Policymakers continue to deal with the difficult task of determining how to allocate them. This research work will present how the use of mathematical models can provide valuable decision support under such conditions. Both a linear programming model and a nonlinear programming model have been developed to determine the optimal allocation of COVID-19 vaccines that minimize fatalities and COVID-19 transmission, respectively. These scenarios have to be dealt with when not enough vaccines are available, and the pandemic is still in progress. The model is capable of handling large scale allocation problems such as those intended for the general population of a country. It could also be scaled down for organizations such as private companies or universities. The model also considers multiple vaccines with different levels of efficacy. The distribution of vaccines reduces transmission and relative infectiousness of individuals across different age groups. A hypothetical case study is solved to illustrate the computational capability of the models. The results indicate that priority should be given to the elderly when fatalities are minimized. In contrast, the younger population should then be prioritized when the objective shifts to suppressing contagion. © 2021, AIDIC Servizi S.r.l.
ABSTRACT
Abstract: The global scientific community has intensified efforts to develop, test, and commercialize pharmaceutical products to deal with the COVID-19 pandemic. Trials for both antivirals and vaccines are in progress; candidates include existing repurposed drugs that were originally developed for other ailments. Once these are shown to be effective, their production will need to be ramped up rapidly to keep pace with the growing demand as the pandemic progresses. It is highly likely that the drugs will be in short supply in the interim, which leaves policymakers and medical personnel with the difficult task of determining how to allocate them. Under such conditions, mathematical models can provide valuable decision support. In particular, useful models can be derived from process integration techniques that deal with tight resource constraints. In this paper, a linear programming model is developed to determine the optimal allocation of COVID-19 drugs that minimizes patient fatalities, taking into account additional hospital capacity constraints. Two hypothetical case studies are solved to illustrate the computational capability of the model, which can generate an allocation plan with outcomes that are superior to simple ad hoc allocation.