Synergizing AI and Multiprocessor Systems for Task Scheduling in Industry 4.0

Task Scheduling is the establishment of specific guidelines for the consecutive execution of activities. Within the domain of parallel computing, the process of scheduling becomes complex, requiring the use of several processors and the allocation of resources to each individual processor. The goal is to effectively distribute particular tasks across processors and computers to accomplish predetermined objectives. It is essential to focus on work scheduling and resource management, as these are the main problems in numerical comparison. This study employs a metaheuristic hybrid strategy to address the inherent challenges of task scheduling. The primary objective is to enhance the efficiency of resource allocation and work scheduling, which is achieved by utilizing a Hybrid Genetic Algorithm (HGA). The results obtained from implementing the HGA algorithm on 40 operational tasks demonstrate its higher performance in comparison to other algorithms. The approach is very effective in tackling task scheduling problems in multiprocessor computing systems. This approach is notable for its faster convergence time and more efficient utilization of resources compared to other algorithms. The HGA technique is a reliable solution for improving work scheduling and resource management in parallel computing environments.