## 1 Introduction
### Literature Review  
It can be found from recent studies that the intelligent optimization algorithm is the most common method to solve a FJSP that adopts an AGV for material transportation, and the near-optimal or optimal solution of the scheduling problem can be obtained through continuous update and iteration [14]. [PERSON] et al. [15] developed a mixed integer linear programming model with the goal of minimizing the manufacturing period and proposed a heuristic algorithm solution model based on tabu search. [PERSON] et al. [16] took the minimization of delay cost and the minimization of makespan as objective functions and solved the model with genetic algorithm to solve the integrated scheduling problem of processing equipment and an AGV in FMS. [PERSON] et al. [17] discussed the path planning problem of an AGV in a flexible job shop. Taking the makespan as the optimization objective, they proposed a two-stage ant colony algorithm to first solve the allocation and path selection problems of an AGV. [PERSON] et al. [18] constructed a planning model with the goal of shortening the manufacturing cycle to the maximum extent under the minimum running time and adopted an adaptive genetic algorithm to solve the optimal scheduling scheme. [PERSON] [19] proposed a meta-heuristic gravity search algorithm to solve the scheduling problems of processing equipment and AGVs in a job shop. The model constructed aimed at minimizing the makespan, completing assigned tasks faster, and solving system resources. [PERSON] [20] designed a network optimization algorithm to solve the problems of AGV scheduling and path planning as well as the scheduling problems of production equipment. Taking the minimum waiting time as the delay penalty objective letter, he established a mathematical model suitable for the control of the minimum cost according to the delay cost. [PERSON] et al. [21] constructed a flexible shop scheduling model aimed at minimizing the AGV buffer waiting time and total driving distance and proposed an improved harmony search algorithm to solve the mathematical model. [PERSON] et al. [22] considered the optimal number of AGVs, the shortest transportation time, and path planning, and proposed an improved genetic algorithm to solve the integrated scheduling problem of AGVS and processing equipment in a flexible manufacturing system with the makespan as the optimization objective. [PERSON] et al. [23] established a flexible job-shop scheduling model aiming at minimizing the makespan, studied flexible job-shop scheduling with AGVs, and proposed an improved genetic algorithm solution model. [PERSON] et al. [24] proposed a meme algorithm combining waiting time to solve FJSP to minimize the makespan. As can be seen from the above FJSP regarding the use of an AGV for material transportation, the scheduling objective of most workshops has only been the completion time measurement, without considering the factors related to energy saving and emissions reduction. In recent years, environmental problems have become increasingly prominent, reducing carbon emissions and building a resource-saving and environmentally friendly society has become the consensus of all countries worldwide. As an energy-intensive field, manufacturing consumes nearly one third of the world's energy [25; 26]. The study of energy saving shop scheduling is of great significance to the sustainable development of the industry [27; 28]. Therefore, the questions of how to reduce the energy consumption of the workshop as much as possible and realize reasonable scheduling of processing equipment and AGVs under the condition of ensuring normal operation of the workshop and minimizing production time have become research hotspots. [PERSON] et al. [29] studied a green multi-objective logistics system problem where the objective was to simultaneously minimize the makespan and energy consumption, and they proposed a new multi-population co-evolutionary algorithm to solve it. [PERSON] et al.