## 4 Method Design
### Proposed Analytical Procedures  
The analytical procedures of the proposed method design are summarised in this subsection. 4.4.1. Collecting Parameters and Data of the Mathematical Model  
The collection of parameters and data includes the following four parts: Vehicle information: the number, carrying capacity, loading and unloading time, velocity, utilizing cost, TC of electric and fuel vehicles; carbon emissions of ton-km transportation of fuel vehicle; endurance mileage and charging time of the electric vehicle. Applier data: distance between each applier storage and factory storage; material inventory of each applier. Factory parameters: time windows; carbon quota; minimum transportation quantity; the best rest time, the shortest rest time and the longest rest time of electric vehicle drivers are required between continuous working. 4.4.2. Obtaining Scheduling Scenarios under Different Vehicle Combination Conditions  
First, setting parameters of a minimal number combination of electric and fuel vehicles to meet the demand of time windows and inputting data into the GA algorithm program designed for the mathematical model under the constraint of Equations (7)-(15), and output scheduling scenario of the objective combination of Equations (1)-(6). Second, gradually increase the number of electric and fuel vehicles and repeat the step first. After that, screen out the scheduling scenarios that meet the constraint of Equations (15) and (16). 4.4.3. Calculating Index Weights  
Firstly, construct the index system of benefits evaluation of production scheduling and the corresponding judge matrix. Secondly, calculate the weight value of each index according to Equations (17) and (18) and establish the weight vector Equation (19). Thirdly, conduct consistency checks according to Equations (21) and (22). 4.4.4. Evaluating Scheduling Scenarios  
First, according to Equations (23) to (24), the indicators of scheduling scenarios are dimensionless to generate the result of Equation (25). Second, calculate the weighted normalised matrix \(V\) according to Equation (26). Third, identify positive and negative ideal solutions according to Equations (27)-(30). Fourth, calculate the distances from each scheduling scenario to the positive and negative ideal solutions according to Equations (31) and (32) and the closeness degree from each scheduling scenario to the ideal solution according to Equation (33). Finally, output optimal scenarios according to the closeness degree of each scheduling scenario. 5. Analytical Results  
The detailed information and analytical results of a real-world case study are introduced in this section. 5.1. Case Information  
The presented ISS-AFLCSS was applied to the case for production scheduling operation in Chengsteel Company from Hebei Province, China. With the proposed 2030 Agenda for Sustainable Development, sustainable development is regarded as the primary objective of the production and program of the Chengsteel Company. Chengsteel's focus is gradually shifting from the core of economic benefit in the past to the comprehensive consideration of the economy, the environment and society today. As the birthplace of comprehensive development and utilisation technology of vanadium-titanium magnetite in China, Chengsteel is located in a mountainous area and vehicles have become the main means of transportation for local raw materials. Recently, the company introduced electric freight vehicles as a comprehensive benefit of consideration in production scheduling. From an economic point of view, electric vehicles will lead to some economic losses compared to fuel vehicles. However, from the perspective of sustainable development of the company, it is necessary to make a scientific and objective evaluation of the introduction of electric vehicles. Therefore, based on the project, this study discusses the economic, environmental and social comprehensive benefits of vehicles from the macro perspective of SS.