A multi-objective particle swarm optimisation for integrated configuration design and scheduling in reconfigurable manufacturing system

To provide accurate capacity and functionality needed for each demand period (DP), a reconfigurable manufacturing system (RMS) is able to change its configuration with time. For the RMS with multi-part flow line configuration that concurrently produces multiple parts within the same family, the cost and delivery time are dependent on its configuration and relating scheduling for any DP. So far, the study on solution method for the integrated optimisation problem of configuration design and scheduling for RMS is scarce. To efficiently find solutions with tradeoffs between total cost and tardiness, a multi-objective particle swarm optimisation (MoPSO) based on crowding distance and external Pareto solution archive is presented to solve practical-sized problems. The devised encoding and decoding methods along with the particle updating mechanism of MoPSO ensure any particle a feasible solution. The comparison between MoPSO and epsilon-constraint method versus small-sized cases illustrates the effectiveness of MoPSO. The comparative results between MoPSO and nondominated sorting genetic algorithm II (NSGA-II) against eight problems show that the MoPSO outperforms the NSGA-II in both solution quality and computation efficiency for the integrated optimisation problem.

Automated summary

The study focuses on the integrated optimization problem of configuration design and scheduling for a reconfigurable manufacturing system (RMS), and proposes a multi-objective particle swarm optimization (MoPSO) method. Comparative results show that MoPSO outperforms epsilon-constraint method and nondominated sorting genetic algorithm II (NSGA-II) in both solution quality and computation efficiency.