面向大规模复杂任务的多约束着色旅行商问题建模和智能优化算法研究

随着人工智能和物联网等技术的发展，现代物流产业规模化、自动化和智能化水平不断提高。如何对智慧物流行业大规模复杂调度问题进行精确建模、分析和求解且同时满足人们多样化、个性化的实际需求成为目前优化调度领域亟待解决的难题。着色旅行商问题（CTSP）通过引入颜色来描述旅行商对城市的差异性访问，扩展了传统的旅行商问题（TSP）和多旅行商问题。为了描述更加复杂的优化调度问题，本项目将容量、优先级和时间窗等多约束引入CTSP模型，围绕面向大规模复杂任务的多约束CTSP建模和智能优化算法设计问题展开，研究内容涉及基于超图和整数规划的多约束CTSP建模与理论分析、大规模多约束CTSP启发式智能优化算法设计以及在智慧物流领域中的应用。项目有望构建更加符合实际工程需求的CTSP模型，给出一套通用的高效智能求解算法，进一步推动TSP或CTSP研究发展，为解决大规模复杂调度优化问题提供模型、理论和方法支撑。

With the development of technologies such as artificial intelligence and the Internet of Things, the scale, automation and intelligence level of the modern logistics industry has been continuously improved. How to accurately model, analyze and solve large-scale and complex scheduling problems in the smart logistics industry and at the same time meet people's diverse and personalized actual demand has become an urgent problem to be solved in the field of optimal scheduling. The Colored Traveling Salesman Problem (CTSP) extends the traditional Traveling Salesman Problem (TSP) and Multiple Travelling Salesman Problem by introducing colors to describe the differential city visits among multiple salesmen. In order to describe more complex optimization scheduling problems, this project introduces multiple constraints such as capacity, priority and time window into the CTSP model, and focuses on the modeling and intelligent optimization algorithm design problems for CTSP with multiple constraints and large-scale complex tasks, which includes modeling and theoretical analysis of CTSP with multiple constraints based on hypergraphs and integer programming, heuristic intelligent optimization algorithm design for large-scale CTSP with multiple constraints and their applications in the field of smart logistics. The project is expected to build a more general CTSP model that is in line with actual engineering requirements, provide a set of general efficient and intelligent solving algorithms, which can further promote the research and development of TSP or CTSP, and provide model, theoretical and method support for solving large-scale complex scheduling optimization problems.