Bacterial foraging optimization algorithm in robotic cells with sequence-dependent setup times

Abstract In this paper, we propose an improved discrete bacterial foraging algorithm to determine the optimal sequence of parts and robot moves in order to minimize the cycle time for the 2-machine robotic cell scheduling problem with sequence-dependent setup times. We present a method to convert the solutions from continuous to discrete form. In addition, two neighborhood search techniques are employed to updating the positions of each bacterium during chemotaxis and elimination–dispersal operations in order to accelerate the search procedure and to improve the solution. Moreover, a multi-objective optimization algorithm based on NSGA-II combined with the response surface methodology and the desirability technique is applied to tune the parameters as well as to enhance the convergence speed of the proposed algorithm. Finally, a design of experiment based on central composite design is used to determine the optimal settings of the operating parameters of the proposed algorithm. The results of the computational experimentation with a large number of randomly generated test problems demonstrate that the proposed method is relatively more effective and efficient than the state-of-the-art algorithms in minimizing the cycle time in the robotic cell scheduling.