## 1 Introduction
[7] proposed a multi-agent approach to address the FJSP with setup and transportation times, aiming to minimize makespan. [PERSON] et al. [8] established a mixed-integer linear programming model, aiming at minimizing makespan and total energy consumption for the FJSP with setup and transportation time. Considering the transportation and sequence-based setup time constraints, [PERSON] et al. [9] established an integer programming model that optimized both energy consumption and makespan. However, in the process of research, the above literature only considers the situation in which the workpiece is single and different, and rarely considers the production demand for different parts in batches. For the scheduling of small-batch workpieces based on MTO requirements, there will be continuous processing of the same type of workpieces on the same machine, which will eliminate the interference of machine adjustment factors (tools and fixtures replacement, workpiece positioning, tool alignment measurement, program adjustment, etc.). In addition, some enterprises use the actual production of the work center as a unit to arrange production; at this time, the workpieces in the same multi-equipment work center can be transferred, ignoring the handling time. For example, [PERSON] et al. [10] set up a dual-center flexible job shop-scheduling model with one machine in the first center and k parallel machines in the second center to solve the dual-center flexible job shop scheduling problem with the goal of minimizing the maximum completion time. In FJSP solving, an efficient algorithm is the key to solving the problem model effectively. In recent years, many methods have been applied to solving the FJSP. For example, some studies have proposed precise algorithms, including mathematical programming and mixed-integer target programming, such as that by [PERSON] et al. [11]. However, with the increase in the number of batch workpieces and the number of machines, the mathematical model has become extremely complex, the problem-solving difficulty has increased, and the traditional accurate algorithm has been unable to meet the solving needs. Therefore, many scholars have begun to study intelligent optimization algorithms to obtain near-optimal solutions. In the field of evolutionary algorithms (EAs), a large number of researchers have made many improvements to the evolutionary algorithm to improve the effectiveness of the FJSP solution. [PERSON] et al. (2012) established a multi-objective optimization model, aiming at minimizing energy consumption and completion time for flexible job shop scheduling problems with transportation constraints, and proposed an improved genetic algorithm to solve this problem. [PERSON] and [PERSON] (2013) consider the energy-efficient flexible job shop scheduling problem with transportation and sequence-dependent setup times, and an imperialist competitive algorithm with feedback (FICA) is developed to minimize makespan, total tardiness, and total energy consumption simultaneously. On the basis of considering factors such as adjustment time and handling time, [PERSON] and [PERSON] (2014) proposed an NSGA-II-V algorithm to solve the multi-equipment work center scheduling problem with the optimization objective of minimizing the maximum completion time and handling times. In addition, in the swarm intelligence (SI) field, the results of algorithm research and improvement are more outstanding. In solving the FJSP, combining two or more SI algorithms or criteria leads to better results than those obtained when using a single algorithm or criterion alone (2015). [PERSON] and [PERSON] (2016) combined variable neighborhood search (VNS) and the ant algorithm (AA), and proposed a new general meta-heuristic algorithm, namely learning variable neighborhood search (LVNS), to solve job shop scheduling problems. [PERSON] et al.