Sequential process planning: A hybrid optimal macro-level approach

Increased global competition and unpredictable market changes are challenges facing manufacturing enterprises. Changes of part design and engineering specifications trigger frequent and costly changes in process plans, setups, and machinery. The paradigm shift in manufacturing systems and their increased changeability also require corresponding responsiveness in support functions; process planning is a key logical enabler that should be further developed to cope with changes encountered at the system level and to support new manufacturing paradigms and continuously evolving products. Retrieval-based planning, predicated on rigid predefined boundaries of part families, does not satisfactorily support this changeable manufacturing environment. On the other hand, pure generative planning is not yet a reality. Therefore, a sequential hybrid approach at the macro level is proposed where, initially, the part family’s master plan is retrieved, followed by application of modeling tools and solution algorithms to arrive at the plans of the new parts, whose features could exceed its respective original family boundaries. Two distinct generative methods, namely reconfigurable process planning and process replanning, are presented and compared. A genuine reconfiguration of process plans to optimize the scope, extent, and cost of reconfiguration is achieved using a 0–1 integer programming model. Also, because the problem is combinatorial in nature, a random-based evolutionary simulated annealing algorithm has been tailored for replanning. The developed methods are, conceptually and computationally, analyzed and validated using an industrial case study.