###### Abstract  
The production of multiple types of satellites based on a common manufacturing platform represents a permutation flowshop scheduling problem (PFSP) with complex constraints. This is a highly complex scheduling problem, yet there is still a gap between theoretical research and practical application, particularly in the satellite industry. Therefore, we propose a more practical method that integrates discrete-event simulation modelling and an improved NEH algorithm to solve a more realistic PFSP. The discrete-event simulation-based method includes the following three main components: a flexible PFSP simulation modelling approach, an improved NEH algorithm, and an interaction mechanism between the simulation model and the optimisation algorithm. The proposed method allows automatic and flexible simulation modelling according to the characteristics of the actual satellite manufacturing workshop, which determines the practical nature of the approach proposed in this paper and then achieves excellent scheduling results based on the special interaction mechanism. The computational results demonstrate that this is a 9.18% improvement over the initial NEH algorithm and a 1.40% improvement over the best current improved NEH algorithm. discrete-event simulation; simulation modelling; satellite batch production; flowshop scheduling; NEH algorithm; Plant Simulation +
Footnote †: journal: Medical Imaging and Applications  
applications. It has been proven that some variants of PFSP are NP-hard problems [9]. Therefore, PFSPs have a wide range of engineering application backgrounds and high theoretical research value. With the depth of research and the need to solve practical problems, more variants of PFSP are being studied, for example, multi-objective PFSPs [10], no-idle and no-wait PFSPs [11], PFSPs with sequence-dependent setup time [12], PFSPs with limited buffers [13], and variations and combinations of these problems [14; 15]. Most studies are based on a mathematical approach, like some new algorithm or new search method [16; 17]. However, due to the complexity and randomness of the actual environment, it is difficult to accurately establish a mathematical model for the objective functions. With the advent of small-batch and customised production modes, the mathematical approach of PFSPs will make it increasingly difficult to meet future research needs. According to the published PFSP literature, a very important phenomenon is that theoretical research on PFSPs has rarely been applied, which indicates that there is still a considerable gap between theoretical research and practical application in the PFSP field. The first reason for this phenomenon is that it is difficult to describe a complex real system with mathematical models accurately. The second reason may be that most researchers have only focussed on one particular problem in the PFSP at a time. At present, a more pragmatic method for solving a realistic PFSP is still lacking. With the development of computers, discrete-event simulation technology has become an important decision-making tool for solving various discrete-event problems [18; 19]. In addition, the authenticity of discrete-event simulation is more prominent compared to mathematical methods. For example, assessing the maturity level of Industry 4.0 [20], determining the logistics scheduling strategies [21], determining the production layout [22], allocating the resources in production logistics systems [23], evaluating the eco-efficiency of railway systems [24], identifying and alleviating bottlenecks [25], rationalising the allocation of production resources in conjunction with deep reinforcement learning [26], evaluating fossil fuel boiler electrification for decarbonisation [27], and conducting supply chain management research [28]. PFSPs are also a type of discrete-event problem, and there is a relatively small amount of research that combines discrete-event simulation with scheduling [29].