A hybrid genetic algorithm with variable neighborhood search for dynamic integrated process planning and scheduling. (December 2016)

• Record Type: Journal Article
• Authors: Xia, Hao; Li, Xinyu; Gao, Liang
• Is Part Of: Computers & industrial engineering. Volume 102(2016)
• Page Start: 99
• Page End: 112
• Publication Date: 2016-12
• Subjects: Dynamic IPPS -- Hybrid algorithm -- GAVNS -- Rolling window technology; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.10.015
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A new dynamic IPPS model is formulated is this paper. The rolling scheduling strategy is used for dynamic IPPS problem. A hybrid GAVNS is developed for dynamic IPPS. Two efficient neighborhoods are applied for local search. Abstract: Integrated process planning and scheduling (IPPS) which is a hot research topic has provided a blueprint of efficient manufacturing process, but 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, will make the optimal process plan and schedule may become less efficient or even infeasible. The dynamic IPPS (DIPPS) can better model the practical manufacturing environment but is rarely researched because of its complexity. In this paper, a new dynamic IPPS model is formulated, the combination of 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 variable neighborhood search (GAVNS) is developed for the dynamic IPPS problem because of its good searching performance. Three experiments which are adopted from some famous benchmark problems have been conducted to verify the performance of the proposed algorithm, and the computational results are compared with the results of improved genetic algorithm (IGA). The results show that the proposed method has achieved significant improvement for solving the DIPPS.