# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 1 Introduction
Currently, the research on customer priority has been extensively applied in various domains, including vehicle scheduling, multi-automated guided vehicle systems, cold chain distribution routes, and vehicle dispatch decision-making processes. Such studies indicate the customer priorities has the potential to significantly enhance customer satisfaction and exert a beneficial influence on decision-making processes of scheduling [14]. However, the application of customer priority research remains relatively underexplored within the context of vehicle scheduling in coal loading. Vehicle scheduling is a complex optimization conundrum that frequently necessitates algorithmic solutions. Presently, vehicle scheduling dilemmas are commonly tackled through diverse methodologies, including genetic algorithms [15; 16], ant colony algorithms [17; 18], particle swarm algorithms [19; 20], and simulated annealing algorithms [21; 22]. [PERSON] et al. [23] employed an optimized genetic algorithm to address baggage vehicle scheduling issue at airports. [PERSON] et al. [24] presented an innovative and enhanced ant colony optimization algorithm for managing the production allocation scheduling issue, encompassing non-identical batch machines and multiple vehicles. [PERSON] et al. [25] utilized a hybrid particle swarm algorithm to address a hazardous materials vehicle scheduling model involving multiple distribution centers. [PERSON] et al. [26] proposed an adaptive neighborhood simulated annealing algorithm for tackling the heterogeneous fleet vehicle path problem with multiple cross-docking facilities. [PERSON] et al. [27] introduced a quantum adaptive sparrow search algorithm to tackle this large-scale intricate scheduling problem, considering factors like renewable energy and loads. [PERSON] et al. [28] developed an improved Sparrow Search Algorithm by integrating a coded transformation technique for operation sequencing and chaotic mapping. Vehicle scheduling problems can be addressed using a range of algorithms. The MCVS problem addressed in this study is characterized by its large scale and complexity. In comparison to the four aforementioned algorithms, the Sparrow search algorithm is noted for its simplicity and ease of implementation, making it particularly well-suited for addressing large-scale and complex scheduling problems [29]. However, the traditional Sparrow search algorithm exhibits limitations, including lower efficiency and a propensity to converge to local optima [30]. Enhancements to the Sparrow search algorithm with other optimization techniques could potentially enhance its performance and search efficacy. This study introduces customer priority into vehicle scheduling and applies an algorithm to address this issue, aiming to improve overall scheduling efficiency and customer satisfaction within intelligent coal loading systems, thereby addressing a gap in existing research on vehicle scheduling before coal loading. This approach is expected to provide a robust theoretical and practical foundation for advancing intelligence and efficiency within the coal industry. This paper mainly studies the MCVS of coal mine industries, which is divided into five chapters, and its specific structure is as follows: The Section 1 analyzes the coal loading operation flow; Section 2 explains the expression and symbol of the problem; the proposed algorithm is described in Section 3. Section 4 mainly analyzes the MCVS in detail with a case study, and the fifth part summarizes this paper.