Revisiting Meta-Heuristic Optimization Method in solving Stochastic System Design Problem

Optimization is an important issue in the real world, and most problems can be transformed into optimization problems. However, such stochastic optimization problems are always accompanied by uncertainty, especially in the industries of innovative technologies (i.e., wearable devices and sensors on healthcare), integrated supply chain, and sustainable operations management. Due to the outbreak of COVID-19 pandemics last year, it has become quite difficult for industries to quickly obtain their supplies and optimize their operations. Therefore, a Particle Swarm Optimization Retrospective Approximation (PSORA) algorithm is proposed to solve and validate the problem using a unimodal example and sensitivity analysis. PSORA uses the framework of Retrospective approximation (RA) to iteratively solve a sequence of sample path approximation problems with increasing sample sizes;each sample path problem is solved by the improved PSO algorithm. When the sample size approaches infinite, the improved PSO algorithm solves the sample path problem to approximately identify the real objective function. Our simulation results show that PSORA is robust, and converges quickly. The result of the developed optimal model can provide marginal insights to decision-makers in problem-solving. © 2021 IEEE.

PrivyTRAC – Privacy and security preserving contact tracing system

Smartphone location-based methods have been proposed and implemented as an effective alternative to traditional labor intensive contact tracing methods. However, there are serious privacy and security concerns that may impede wide-spread adoption in many societies. Furthermore, these methods rely solely on proximity to patients, based on Bluetooth or GPS signal, ignoring lingering effects of virus, including COVID-19, present in the environment. This results in inaccurate risk assessment and incomplete contact tracing. A new system concept called PrivyTRAC preserves user privacy, increases security and improves accuracy of smartphone contact tracing. PrivyTRAC enhances users’ and patients’ privacy by letting users conduct self-evaluation based on the risk maps download to their smartphones. No user information is transmitted to external locations or devices, and no personally identifiable patient information is embedded in the risk maps as they are processed anonymized and aggregated locations of confirmed patients. The risk maps consider both spatial proximity and temporal effects to improve the accuracy of the infection risk estimation. Experiments conducted in the paper illustrate improvement of PrivyTRAC over proximity-based methods in terms of true and false positives. An approach to further improve infection risk estimation by incorporating both positive and negative local test results from contacts of confirmed cases is also described. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.