Mathematical Modeling for Reconfigurable Process Planning

The paradigm shift in manufacturing systems and their increased flexibility, and changeability require corresponding responsiveness in support functions to achieve cost-effective adaptability. Reconfigurable Process Planning (RPP) is an important enabler of changeability for evolving products and systems. Mathematical programming and formulation is presented, for the first time, to reconfigure process plans to account for changes in parts' features beyond the scope of the original product family. Reconfiguration of precedence graphs to optimize the scope and cost of process plans reconfiguration is achieved by inserting/removing features iteratively using a novel 0-1 integer programming model. The proposed RPP mathematical scheme scales better with problem size compared with classical process planning models. The formulation of the mathematical model at each iterative step of reconfiguration has been automated. A process plan reconfiguration index (RI) that captures the extent of changes in the plan and their implications has been introduced. A prismatic benchmark and an industrial case study are used for illustration and verification. The computational behavior and advantages of the proposed model are discussed, analyzed and compared with classical models.