A Hybrid Genetic Algorithm with Variable Neighborhood Search for Dynamic IPPS

Abstract

Integrated Process Planning and Scheduling (IPPS), which is a hot research topic, has provided a blueprint for efficient manufacturing processes. However, in real production, the machining environment changes dynamically because of external and internal fluctuations. These disturbances, which include machine breakdowns, rush order arrivals, and so on, may render the optimal process plan and schedule less efficient or even infeasible. The Dynamic IPPS (DIPPS) which can better model the practical manufacturing environment is rarely researched because of its complexity. In this chapter, a new dynamic IPPS model is formulated, the combination of a Hybrid Algorithm (HA) and rolling window technology is applied to solve the dynamic IPPS problem, and two kinds of disturbances are considered, which are the machine breakdown and new job arrival. A hybrid Genetic Algorithm with a Variable Neighborhood Search (GAVNS) is developed for the dynamic IPPS problem because of its good search performance. Three experiments which are adapted from some famous benchmark problems have been conducted to verify the performance of the proposed algorithm, and the computational results are compared with those of Improved Genetic Algorithm (IGA). The results show that the proposed method has achieved significant improvement in solving the DIPPS.