Description:
Open shop scheduling problems (OSSP) are highly significant in engineering and industry, involving critical scheduling challenges. The job type determines the duration required for material transfer between machines and remains unaffected by distance, eliminating any waiting time for transfers. This study also investigates machine-to-machine transfer durations, influenced by job types and independent of distance. The approach considers a transportation system allowing an unrestricted quantity of transportation machines, ensuring no delay is required for open shop scheduling during transfers. These specific scheduling problems are described as no-wait open shop scheduling problems (NWOSP). A mathematical model called mixed integer nonlinear programming models (MINLP) is utilized, employing the GAMS BONMIN solver for small and medium-scale problems to address smaller-scale instances of these problems. Three metaheuristic algorithms of Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Ant Lion Optimizer (ALO) are compared to resolve large-scale problems by considering a sorting function assisted by MATLAB to change the continuous metaheuristics environment to a permutation scheduling problem considering 250 iterations. Since metaheuristics are a theoretically powerful optimizer tool designed to expedite the resolution of intricate single-objective nonlinear problems and locate the most advantageous solution, reference point programming with Euclidean distances (RPPED) is applied as the powerful tool for expediting the nonlinearity conditions and converting the bi-objective mixed integer linear programming (BOMILP) to suggested single objective MINLP.