Optimizing resource allocation with a hybrid algorithm: Enhancing time series mobility tasks efficiency

Resource allocation is extremely important, especially when dealing with national security and threats. During the COVID-19 pandemic, it was proven that testing stations helped reduce the number of confirmed cases. Due to the mobile and rapid assembly nature of testing stations, this study considered distance and the number of confirmed cases, developing the City Parallel Time Circular Shift (CPTCS) algorithm to address time series considerations. The research results compared the original Traveling Salesman Problem (TSP), Whale Optimization Algorithm for the Traveling Salesman Problem (WOA-TSP), and Simulated Annealing for the Traveling Salesman Problem (SA-TSP) with four different datasets: Taipei, Taoyuan, Taichung, and Kaohsiung cities. In the dataset for Taoyuan City, the proposed CPTCS algorithm reduced the overall total tour length by 22.27 %, 14.62 %, and 17.15 % compared to the other three methods, and decreased the number of moves by 96.4 %, 94.7 %, and 93.39 %. Finally, by comparing different cities, it was found that the weights for distance and the number of confirmed cases should be equal. This finding provides significant reference value for long-term series of mobile tasks in the face of national security and threats.