# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 1 Introduction  
In the shipbuilding industry, an essential part of scientific management is lead time, which is necessary for shipyards to arrange production plans, particularly in production organization and progress of production control [1, 2]. Additionally, lead time is closely related to the production efficiency of frontline manufacturing workers. The rationality of its arrangement directly affects workers' production enthusiasm, thereby affecting product quality and labor productivity [3]. For example, if the evaluation of lead time is insufficient and management has not prepared the appropriate plan for construction peaks, the construction cycle will be prolonged. Here, to avoid affecting the construction cycle, workers must operate overtime for long periods, resulting in a decline in construction quality. Conversely, if the evaluation of lead time is overestimated, it results in excess construction capacity problems and human resource waste [4]. Consequently, lead time should be arranged reasonably, which means that the planned lead time must be as close as possible to the actual lead time in the shipyards' production planning stage. However, because the shipbuilding industry is a labor-intensive industry, this has resulted in a significant difference between planned and actual lead times (Figure 1). Therefore, to rationalize the lead time arrangement, lead-time prediction becomes particularly critical [2, 4, 5]. For many years, managers frequently used the experience evaluation method to specify lead time [6]. However, this method is time-consuming and inefficient. Thus, production planning and scheduling (PPS) cannot be properly organized, making shipyard management ineffective [7]. Lead time is affected by various factors and restricted byvarious conditions [8]. Several researchers have studied the lead time of production, and some results have been achieved [7; 9; 10]. In recent research, machine learning (ML) has been widely applied in the prediction of production lead time to understand the complex relationship between lead time and its affecting factors. [PERSON] et al. [9] proposed using ML algorithms to predict the lead time of jobs in the manufacturing flow-shop environment; the results indicated that ML algorithms can sufficiently understand the non-linear relationship, and they obtained good prediction accuracy from ML models. [PERSON] et al. [7] analyzed the key features of lead time, provided importance scores, and developed ML models to predict lead time in the semiconductor manufacturing industry. In particular, [PERSON] et al. [10] attempted to improve production management capabilities by analyzing the lead time based on spool fabrication and painting datasets. They applied ML algorithms and compared the performance of each. In ML, the SVM algorithm, proposed by [PERSON] [11] in 1995, is widely used in the prediction field. Because it is based on statistical learning theory and the principle of structural risk minimization, an SVM can theoretically converge on the global optimal solution of a problem. Moreover, it exhibits unique advantages in solving small samples and nonlinear problems. It has strong generalization ability and has become a popular research topic in the field of industrial forecasting. [PERSON] et al. [12] applied an SVM model to predict time series. They demonstrated that the SVM model performs well in time-series forecasting. [PERSON] et al. [13] proposed using an SVM model to forecast the short-term load of an electric power system. They demonstrated that the forecast performance of the SVM model was better than that of a back-propagation neural network (BPNN). [PERSON] et al. [14] used an SVM model to predict copper prices. The results indicated that the SVM model can predict copper-price volatilities near reality. However, the disadvantage of an SVM is that it is too sensitive to parameters, and an efficient SVM model can be built only after its parameters are carefully selected [15].