## 1 Introduction
Once one factory is established, it will run continuously for 365 days a year and 24 hours per day. In addition, the strict control of environmental parameters in the wafer production process makes it necessary for manufacturing factories and processing equipment to meet their production continuity requirements with uninterrupted high energy consumption. For example, precise temperature control of the etching process--the temperature fluctuation of 0.1 \({}^{\circ}\)C up and down will cause deviation of the wafers' excellent rate, which results in wafer scrapping. Therefore, for semiconductor wafer manufacturing, we should not only care about the process, but also put energy saving and environmental protection on the agenda. In this paper, carbon emissions from semiconductor wafer fabrication are taken as an energy-saving indicator for research. Regarding the study of carbon emissions indicator, [PERSON] et al. [21] created a flexible job shop low-carbon scheduling model that considers make span, machine load, and carbon emissions indicators, and proposed a hybrid non-dominated sorting genetic algorithm II to solve the model. In order to help enterprises to quantify the carbon footprint indicator, [PERSON] et al. [22] proposed a method for calculating the carbon footprint of workshop products and an improved fruit fly optimization algorithm to minimize the carbon footprint and production cycle of all products. [PERSON] et al. [23] developed a study on the robot assembly line system, using a multi-objective co-evolution algorithm to solve the carbon footprint minimization problem. [PERSON] et al. [24] proposed an improved multi-objective genetic algorithm to solve the multi-objective flexible job shop scheduling problem while minimizing the total carbon footprint and total number of delayed jobs. So far, there is no literature on the research of carbon emissions indicator in the semiconductor wafer manufacturing. Grey Wolf Optimizer (GWO) is a new swarm intelligence optimization algorithm designed in 2014 by Professor [PERSON] and his team. The algorithm is inspired by the strict social hierarchy and predatory behavior of the grey wolf population in nature [25]. It is used to solve single-objective optimization problems. In 2016, [PERSON] et al. proposed a Multi-Objective Grey Wolf Optimizer (MOGWO) [26] based on the GWO algorithm to solve multi-objective optimization problems. Due to its simple algorithm structure, parameter setting, and better optimization effect, the Grey Wolf algorithm has been widely used in the research of scheduling, image processing, medical treatment, and designing photonic crystal waveguides [27; 28; 29; 30; 31]. However, there is no relevant literature about its application in green collaborative scheduling of the semiconductor wafer distributed heterogeneous factory. In summary, this paper constructs a green manufacturing collaborative optimal scheduling model for a semiconductor wafer distributed heterogeneous factory for the first time, and designs an improved multi-objective Grey Wolf Optimizer (IMOGWO) to solve this problem. The remainder of this paper is organized as follows. The green manufacturing collaborative optimal scheduling model of the semiconductor wafer distributed heterogeneous factory is formulated in Section 2. The green collaborative scheduling problem of the semiconductor wafer distributed heterogeneous factory is described in detail in Section 3. Section 4 presents experimental results and analysis of the results. Section 5 provides the conclusions and future work. Green Manufacturing Shop Scheduling Problem for Semiconductor Wafer Distributed Heterogeneous Factory