## 1 Introduction  
At present, the textile industry is one of the pillar industries of China's national economy, but it is also a labor-intensive industry, facing the problems of low productivity and high human resource costs [1]. In the whole knitting industry, the processes of cutting, dropping and transporting fabric are still mainly performed manually. Manual production not only has a high labor cost but is also not conducive to intelligent and large-scale production [2]. With the application and development of intelligent scheduling theory and robotics, intelligent manufacturing has become the main direction of the current strategic transformation of the textile industry [3, 4], while the impact of the pandemic and the development of the Internet have also greatly promoted the transformation of intelligent production in the textile industry [5]. Automated production in the knitting workshop is achieved by assigning production orders to the circular knitting machines through the workshop management system. After the orders are completed, robots, Automated Guided Vehicles(AGVs) and other equipment are dispatched to complete the processes of cutting, dropping and transporting fabrics to achieve continuous automatic production in the knitting workshop. There are multiple parallel machines in the production and processing equipment, and the processes of cutting, falling and transporting fabrics are completed by the corresponding processing and production equipment assigned by the workshop management system according to the process flow. Therefore, the production scheduling problem in the knitting shop is a typical hybrid shop scheduling problem (HFSP), which is a generalization of the traditional flow shop scheduling problem (FSP), characterized by the existence of multiple parallel machines that can process at least one process at the same time, and it has been proven to be an NP-hard problem [6]. In order to improve the automatic production efficiency of the knitting workshop and reduce production costs, the scheduling of equipment in the workshop is optimized. Therefore, a more efficient solution to HFSP is proposed, which has practical reference value for promoting the intelligent development of the whole process of knitting production. Precise optimization algorithms, such as dynamic programming and integer linear programming, are usually not applicable to large-scale shop scheduling problems. A common approach to solve flow shop scheduling problems is to use swarm intelligence algorithms, and some of the most commonly used algorithms include the genetic algorithm (GA), particle swarm optimization (PSO) algorithm, artificial fish school (AFS) algorithm, gray wolf optimization (GWO) algorithm [6; 7], etc. Among these algorithms, GA has been successfully applied to many combinatorial optimization problems, such as machine scheduling problems and workshop scheduling problems [8]. However, GA needs to be improved to enhance the algorithm performance for problems with a higher complexity. Moreover, improved genetic algorithms based on local search algorithms are one of the most common forms of improvement [9]. In this paper, the knitting workshop scheduling problem is considered as a combinatorial optimization problem, which focuses on the optimal arrangement of production schedules to meet the requirements of specific objectives through the rational allocation of parallel equipment and the cooperation between equipment. For this reason, this paper constructs a knitting workshop production scheduling model and proposes an IGA based on tabu search, following the arrangement coding of normal shop scheduling in decoding according to various rules to obtain a feasible solution to the problem in order to reduce the production time and production cost by scheduling. The sections of this paper are organized as follows: Section 2 describes the work related to the hybrid flow shop. Section 3 describes the mathematical model of production scheduling in the knitting shop. Section 4 proposes an IGA based on tabu search.