In this paper, the shortest path problem is considered with emphasis on reducing the COVID‐19 contamination risk. Generally, the shortest path problem focuses on planning the minimal path considering the path distance criterion, and here the aim is to generate the shortest safe path with obstacles free collision and virus infection saving. For that, the novel Dhouib‐Matrix Shortest Path Problem (DM‐SPP) method is enhanced to reduce the probability of catching COVID‐19 by keeping people away from crowded and risked space. DM‐SPP is enriched with a grid map, namely, the risk pandemic grid map, gathering the human flow density of each area in order to mark the risk epidemic zone. To prove the performance of DM‐SPP to optimize the trajectory in COVID‐19 virus infection, two case sites are used (a campus case study represented as 40 × 40 grid map and an experimental platform of 50 × 50 grid map). The solutions generated by DM‐SPP are graphically represented using the Python programing language, and its results are compared to the results of recently developed metaheuristics in the literature, namely, the classical ant colony optimization metaheuristic, the improved ant colony optimization metaheuristic, the classical A ∗ algorithm, and the improved A ∗ algorithm.
Dhouib et al. (Thu,) studied this question.
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