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

Abstract

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 ε-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.

Keywords:

• Reconfigurable manufacturing system
• configuration design
• scheduling
• mixed integer linear programming
• multi-objective particle swarm optimisation

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was funded by the National Natural Science Foundation of China [grant numbers 51575108, 51875101 and 7181110].