Multi-fidelity digital twin based optimization for aircraft overhaul shop scheduling

Aircraft overhaul is of paramount importance for ensuring safety and reliability of aircraft throughout their entire lifecycle. The considerable number of overhaul tasks and prevalence of manual operations contribute to the elevated complexity and stochasticity of the aircraft overhaul shop scheduling problem (AOSSP), which is seldom considered in current researches. Digital twin (DT) has been proved to be an effective technical measure to simulate, evaluate, and predict the entire lifecycle of its physical entity in the fields of aerospace, automotive, infrastructure, etc. In most situations, the advantages of DT rely on the exact high-fidelity modeling of the physical systems to describe their features, behaviors, rules, etc. However, to optimize a large scale system as the aircraft overhaul shop, the high-fidelity digital twin model will be extremely computationally expensive. To this end, this paper proposes a multi-fidelity digital twin based optimization (MFDTBO) framework to solve AOSSP, which exploits the advantage of digital twin with acceptable level of computation cost. Firstly, the AOSSP is formulated mathematically after analyzing the aircraft overhaul process. Then the framework of MFDTBO is proposed, which embeds an improved hybrid genetic TABU search algorithm into multi-fidelity optimization with ordinal transformation and optimal sampling (MO²TOS). The AOSSP is solved in four stages, i.e. task assignment optimization with low fidelity digital twin, AOSSP optimization with low fidelity digital twin, AOSSP optimization with high fidelity digital twin, ultra-high fidelity digital twin simulation. The effectiveness of the proposed MFDTBO is verified and compared in different scales of test instances with different computation cost. A case study of applying the MFDTBO in aircraft overhaul digital twin system is provided to demonstrate the feasibility.