Clonal selection algorithms for task scheduling in a flexible manufacturing cell with supervisory control

A new approach for the problem of optimal task scheduling in a manufacturing cell is proposed in this work, as a combination of a clonal algorithm with the supervisory control of discrete-event dynamical systems. Two methodologies are proposed. In the first one, the clonal selection algorithm (CSA) performs the search for the optimal solution, using randomized searches over permutations of sequences of operations. The supervisory control has the role of encoding all the problem constraints, allowing for the search to be conducted on the feasible solution set only. The second methodology is similar, but the CSA uses a local search 2-opt to improve the best individual of each generation. The preliminary results show that both methodologies can obtain significant gains in the total plant operation time in relation to the greedy control policy employed on an example system considered here. A better performance of the CSA + 2-opt methodology can also be observed, when compared with the Clonal Selection Algorithm alone. The proposed methodology provides robustness and flexibility to the solutions - these features are not usually present in most optimization-based solutions for those problems.