Concurrent scheduling of jobs and AGVs in a flexible job shop system: a parallel hybrid PSO-GA meta-heuristic

This research proposes a novel mixed integer linear programming (MILP) model along with a Parallel Hybrid PSO-GA Algorithm (PPSOGA) to address the simultaneous scheduling of jobs and Automated Guided Vehicles (AGVs) in a flexible job shop system. Wherein, finite multiple AGVs, alternative process routes, and job re-entry are considered. To the best of our knowledge, no study in the literature has highlighted the efficacy of parallel computing in the simultaneous scheduling of jobs and transporters in a flexible job shop system which remarkably reduces run-time. For this purpose, the suggested meta-heuristic is designed to be compatible with parallel computing and is compared against a number of well-known meta-heuristics (i.e., Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization) on a set of 40 benchmark instances generated using a combination of different distributions (i.e., uniform, exponential, and normal distributions). Employing two Tukey tests, the run-time means and the objective value means of all the suggested meta-heuristics are examined and compared against one another, the results of which emphasizes the superiority of the PPSOGA over all the other solution approaches in terms of the objective function’s value and run-time. Finally, it is discovered that even the sequential mode of the PPSOGA (i.e., the PSOGA) produces better objective values compared to other meta-heuristics.