Designing a Sustainable Model for Providing Health Services Based on the Internet of Things and Meta-Heuristic Algorithms

Abstract

In this article, a health service delivery model based on the Internet of Things (IoT) under uncertainty is presented. The considered model includes a set of patients, doctors, vehicles, and services that should be provided in the shortest time and cost. The most important decisions of the network include the allocation of specialist doctors to patients, the routing of vehicles, and doctors to provide health services. The dataset of the problem has been provided to the hospital and centers using IoT tools and an integration framework has been designed for this problem. The results of solving the numerical examples show that to reduce the service delivery time and the distance traveled by vehicles, the design costs of the model should be increased. Also, the increase in the rate of uncertainty during service delivery leads to an increase in total costs in the health system. In this article, Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-objective imperialist Competitive algorithm (MOICA) were proposed to solve the model, and the results showed that the proposed methods are more efficient than the exact methods. These algorithms have achieved close to optimal results in the shortest possible time. Also, the calculation results in large numerical examples show the high efficiency of the MOICA.