Process sequencing for a pick-and-place robot in a real-life flexible robotic cell

Abstract

Robots are used in manufacturing cells for wide purposes including pick and place of the items from a location to a destination. As far as the authors' knowledge in this context, the scheduling problem of a real-life flexible robotic cell (FRC) with intermediate buffers is missing in the literature. Therefore, in this study, the process-sequencing problem of a real-life FRC is considered, aiming to minimize the cyclic operation time of the cell. The problem is mathematically modeled and solved for a real case. Since computation times for solving the problems rise exponentially with increasing the number of machines in the FRC, a genetic, a simulated annealing, and a hybrid genetic algorithms are proposed to solve the large-sized problems. The objective function value of a given solution in metaheuristic algorithms is computed by solving a linear programming model. After tuning the parameters of the proposed algorithms, several numerical instances are solved, and the performance of these algorithms are evaluated and compared. The results show that the performance of the hybrid genetic algorithm was significantly better than both genetic and simulated annealing algorithms.