## 1 Introduction
Compared with the traditional optimization algorithm, the intelligent algorithm is widely used to solve the combinational optimization problem, such as IPPS, job-shop scheduling, disassembly scheduling, remanufacturing system scheduling and other problems. [PERSON] [19] and others studied the distributed integrated process planning problem, used triangular fuzzy numbers to express the machining time of machine tools and the transportation time of parts, proposed a new three-layer encoding method, which improved the genetic algorithm by improving crossover and mutation strategies, and added alternative machine tools and order exchange strategies to enhance the local search ability of the algorithm. [PERSON] [20] and others studied the job-shop scheduling problem with dual resource constraints, established a model with the goal of minimizing the completion time, combined a knowledge-guided search with a small-based search, and proposed a new encoding scheme, a knowledge-guided Drosophila optimization algorithm, to solve it. [PERSON] et al. [21] addressed an energy-efficient scheduling problem of flexible job-shops with complex processes, and they proposed a novel improved-crossover artificial bee colony algorithm. [PERSON] et al. [22] firstly performed an improved genetic algorithm to solve the energy consumption scheduling problem of a remanufacturing system with disassembly, reprocessing and reassembly, and then they provided a new idea for the integrated optimization of a remanufacturing system. [PERSON] et al. [23] presented an improved whale optimization algorithm to solve discrete problems, and the quality and efficiency of the algorithm were verified by experiments. [PERSON] et al. [24] proposed a hybrid optimization algorithm which is a modified discrete gravitational search algorithm to deal with the problem of the emergency scheduling of priority-based rescue vehicles for extinguishing forest fires. [PERSON] et al. [25] addressed a multi-objective invasive weed optimization algorithm proposed to solve the scheduling problem for a remanufacturing system with parallel disassembly workstations. [PERSON] et al. [26] performed a novel multi-objective bi-population differential artificial bee colony algorithm for the energy-efficient scheduling problem with the multi-resource constraints of a multi-variety and small-batch dynamic flexible job-shop. [PERSON] et al. [27] proposed a fuzzy disassembly scheduling method based on the fruit fly optimization algorithm, which can effectively solve the disassembly scheduling problem in uncertain environments. [PERSON] et al. [28] presented an improved artificial bee colony algorithm for disassembly scheduling and verified its feasibility and effectiveness. [PERSON] et al. [29] performed a hybrid genetic algorithm based on the variable neighborhood search solution method to solve a remanufacturing system's scheduling problem. [PERSON] et al. [30] addressed a fruit fly optimization algorithm to optimize disassembly line balancing. [PERSON] et al. [31], based on the established stochastic disassembly network graph, combined different disassembly decision-making criteria, and typical stochastic models for disassembly time analysis were developed. [PERSON] et al. [32] proposed the novel non-dominated sorting genetic algorithm II (NSGA-II) based on adaptive crossover probability and multi-crossover operators to solve the multi-objective optimization model of the laser remanufacturing process. [PERSON] et al. [33] performed a novel hybrid multi-criteria decision-making technique called grey fuzzy TOPSIS. In summary, the IPPS problem has caused many concerns in the theoretical research, model construction and solution approaches. However, the existing methods are difficult to solve the problem of process route optimization and resource matching of various parts in the production process of aerospace complex components with multiple varieties and models. Additionally, the following problems still need to be solved.