ABSTRACT
This paper investigates a new multi-objective order assignment and scheduling problem for personal protective equipment (PPE) production and distribution during the outbreak of epidemics like COVID-19. The objective is to simultaneously minimize the total cost and maximize the PPE supply timeliness. For the problem, we first develop a bi-objective mixed-integer linear program (MILP). Then an epsilon-constraint combined with logic-based Benders decomposition method is proposed based on some explored properties. We then extend the proposed model to handle dynamics and randomness. In particular, we design a predictive reactive rescheduling approach to address random order arrivals and manufacturer disruptions. Computational experiments on a real case from China and 100 randomly generated instances are conducted. Results show that the proposed algorithm significantly outperforms an adapted epsilon-constraint method combined with the proposed MILP and the widely used non-dominated sorting genetic algorithm II (NSGA-II) in obtaining high-quality Pareto solutions. Note to Practitioners-The unprecedented outbreak of COVID-19 and its rapid spread caught numerous national and local governments unprepared. Healthcare systems faced a vital scarcity of PPEs. The urgency of producing and delivering PPEs increases as the number of infected cases rapidly increases. A key challenge in response to the epidemic is effectively and efficiently matching the demands and needs. Performing practical and efficient order assignment and scheduling for PPE production during the COVID-19 outbreak is critical to curbing the COVID-19 pandemic. This work first proposes a bi-objective mixed-integer linear program for optimal order assignment and scheduling for PPE production. The aim is to achieve an economical and timely PPE production and supply. A novel method that combines the epsilon-constraint framework and the logic-based Benders decomposition is proposed to yield high-quality Pareto solutions for practical-sized problems. Computational results indicate that the proposed approaches are practical and feasible, which can help decision-makers to perform acceptable order assignment and scheduling decisions.
ABSTRACT
The COVID-19 pandemic has caused serious damage to the health, life and, economic stability of human beings all over the world. In order to combat this disease, researchers from all over the world, including computer scientists, are beginning to engage in cross-regional cooperation to conduct research on SARS-CoV-2. One of the latest reports pointed out that the sequence deletion of the specific region of the SARS-CoV-2 genomic is related to its viral infectivity. In addition, the sequence deletion of this specific region is also found in Hepatitis B Virus (HBV), and Hepatocellular carcinoma (HCC). Through next-generation sequencing (NGS) technology, the sequence data of biological genomes can be quickly obtained, but the number of short reads generated by NGS is often as high as one million big data. It is a challenge to detect the information necessary to provide the exact sequence deletion breakpoint from these NGS data, especially in the sequence data of highly variable viral genomes. In our previous research, we proposed VirDelect, a bioinformatics tool that can detect exact breakpoints in Viral NGS data. In this paper, a new method, One-base Alignment Plus (OAP), is proposed to enhance further the core VirDelect algorithm, in order to improve the sequence deletion detection correctness. We use the simulated data of SARS-CoV-2 and HBV with different deletion lengths and the real data of HBV to conduct experiments and evaluate the correctness. The experimental results showed that VirDelect+OAP was able to find deletions that VirDelect could not find in the simulation data, and in the real data, the correctness of VirDelect+OPA was raised effectively. © 2020 IEEE.