A model for generating optimal process plans in RMS

Abstract

A new model for optimizing the manufacturing cost of process plans for reconfigurable manufacturing systems (RMS) using genetic algorithms is proposed. The model represents machines' configurations in the process plan representation string. It considers all process planning parameters simultaneously while simplifying the problem formulation and reducing the computational complexity by choosing the following parameters: machine assignment, machine configuration, operation sequencing, operation cluster sequencing and assigning the tools and tool approach directions (TAD) to the operations. A new approach is proposed, which guarantees that operations with specific constraints are clustered together. A string of continuous variables to represent a process plan is introduced. A new method, which insures that any randomly generated chromosome will result in a feasible process plan, has been developed. The problem formulation is presented and illustrated with two examples and the results are presented and analysed. The results showed that process planning for RMS would cost less, depending on the different cost indices. The presented optimal process planning method can also be used in aiding the part/machine assignment activities and in the initial design stage of reconfigurable manufacturing systems (RMS).

Keywords:

• Process planning
• Optimization
• Genetic algorithms
• Constraint satisfaction
• Reconfigurable manufacturing systems

Acknowledgements

The support for this research received from the Canada Research Chairs (CRC) program and the Natural Sciences and Engineering Research Council (NSERC) of Canada is acknowledged.