Multi-objective chaotic evolutionary-based cell configuration and load balancing for reconfigurable production lines

Abstract

With the continuous advancement of intelligent manufacturing, the reconfigurable manufacturing system (RMS) has become an important development direction for modern manufacturing industry by virtue of its high degree of flexibility and reconfigurable characteristics. As a concrete realization form of RMS, reconfigurable automated production line (RAPL) provides an effective technical path to cope with diversified and individualized market demands. In this study, a multi-constraint mathematical model is constructed around the cell configuration and balance optimization problem in RAPL, taking into account the different production line organization methods and cell service modes. Multi-objectives are established involving the minimization of the cycle time, the smoothing index among the manufacturing cells, and the total number of machines of the RAPL. Recognizing the collaborative interaction between mobile robots and machines, a specific theoretical cycle time derivation method is proposed for this RAPL system, and a general-purpose simulation model is designed to support the evaluation and optimization of multiple configuration schemes, thereby verifying the accuracy of the derivation model (with an error of only 1.5 %). To overcome the inefficiency and trial-and-error nature of manual methods, a multi-objective chaotic evolutionary algorithm (MOCEO) is developed. MOCEO demonstrates superior performance and stability, achieving high-quality solutions in a single run and outperforming classical algorithms such as NSGA-II and SPEA2 in hypervolume (HV), distance (GD) and other metrics. The proposed approach provides reliable decision-making support, enabling efficient and effective configuration and balancing of RAPL systems.