I. Introduction
Flexible job shop scheduling problem (FJSP) is one of the intractable combinatorial optimization problems which widely exists in actual production scenarios [1]. In the FJSP system, each job requires several operations which need to be transferred to different machines for processing [2]. In order to complete transportation tasks efficiently, many enterprises select transportation resources, such as trucks, conveyors, cranes, and automated guided vehicles (AGVs) to automatically transport jobs [3]. In recent years, many researches have been carried out considering different constraints, such as setup time [4], [5], and preventive maintenance [6], to match the requirements in different scenarios. However, most researchers ignore the transportation time, or just include it in the processing time. This simplification of the problem makes it less realistic and practical. Actually, there exists a strong coupling relationship between processing tasks and transportation tasks [7]. On the one hand, the previous processing task decides the starting position and starting time of the transportation task. On the other hand, the transportation tasks affect the starting time of the next processing task [8]. Thus, considering the scheduling of transportation resources and production resources simultaneously can make the solutions more practical and effective, which is worthy of study.