The integration of shared renewable resources considering setup times for the parallel machine scheduling problem

With the advent of Industry 5.0, the rise of advanced technologies, and the fast deployment of human–machine collaborative systems, there is a need for novel approaches to optimizing resources and increasing overall production efficiency. In this regard, we investigate a parallel machine scheduling problem (PMS) where various renewable resources switch among unrelated parallel machines during the operational plan. Each machine can be equipped with a specified number of resources, one at a time, on its left and right sides. We propose a mathematical model for the renewable-resource-constrained parallel machine scheduling problem that minimizes total setup times. We further incorporate realistic features such as non-simultaneous start times of machines, machine eligibility, and due date constraints for jobs. We propose a hybrid meta-heuristic algorithm to solve large instances by employing a novel constructive heuristic and the simulated annealing algorithm. Several instances are tested to validate the solution approach and underline its efficiency for large-sized ones. Through the case of a wiring harness manufacturer, we provide managerial insights on the benefit of either increasing the number of sharing renewable resources or increasing the number of machines in PMS problems, which can reduce total setup times by up to 19%.