Real-time routing in flexible flow shops: a self-adaptive swarm-based control model

This paper presents a self-adaptive, swarm-based control model for real-time part routing in a flexible flow-shop environment. The proposed control model is a multi-agent system that exhibits adaptive behaviour, which has been inspired from the natural system of the wasp colony. The production problem, which has been previously studied in the literature by several researchers, involves assigning trucks to paint booths in real-time in a flexible flow-shop environment with the objective of throughput maximization and minimization of number of paint flushes accrued by the production system, assuming no a priori knowledge of the colour sequence or colour distribution of trucks is available. The proposed control model is benchmarked with the results of previous studies reported in the literature in solving the same production problem. The proposed control model uses self-adapting threshold parameters to facilitate the production flow in real time. A simulation-based software environment is designed and developed to investigate its performance. The simulation results show that the proposed control model is more robust to environmental changes, and it outperforms the previously reported studies on the basis of throughput, number of setups, and cycle time performance measures.