本文基于整车物流智能调度决策支持系统的研发、实施与运维的成功应用，论述运筹学在智能化上的应用路径以及实践驱动的学术路径。该系统是国内较早在汽车物流企业实现落地的智能化调度系统，其所形成的思想理论与方法技术揭示了运筹学在智能化应用上的核心价值，以及实践驱动的学术价值，对解决“卡脖子”难题提供示范性思路。本文提出运筹学在智能化研发上“三环七步”的整体研发框架。首先，分析智能化需求的运筹学特征，详细介绍汽车整车物流的发展趋势、瓶颈及智能调度需求；其次，论述运筹学系统模型的作用与建模方法，分析汽车整车物流系统模型的决策要素、目标及约束，提出汽车整车物流智能调度的运筹学应用问题。然后，提出“模式装箱”的新装箱理论问题，明确问题的计算难解性、可解性及核心科学特征。进一步，建立汽车整车物流调度应用问题与科学问题的混合整数线性规划模型；提出求解汽车整车物流调度问题的分支定界算法，以及大规模问题求解的时空分解及滚动求解方法与技术；提出面向运筹应用的生产测试及压力测试方法，给出汽车整车物流调度的测试分析的流程与结果。此外，提出深度集成整车运输管理系统与仓库管理系统、优化算法引擎驱动的分布式、多视图、多系统融合的智能调度决策支持系统。最后，论述该系统在实施过程中的推广使用和运维情况，并对运筹学应用及实践驱动的科学研究进行总结与展望。
This paper discusses both the applied path of operations research on intelligence and the practice driven academic path, based on the development, implementation and maintenance of a referred decision support system that has been successfully deployed to automobile outbound logistics. The system is so far a pioneering intelligent dispatching system for automobile logistics company in China, and the corresponding thoughts, theories, methodologies and technologies demonstrate the key value of the discipline of operations research in the promotion of intelligent applications and the significance of practice in stimulating academic development, and so forth provide referred systematic approach for tackling bottleneck problems. This paper proposes a "Three Stages and Seven Steps" framework for the application of operations research on intelligent research and development. Under the framework, the paper firstly addresses the characteristics of intelligent application related to operational research, and particularly addresses intelligent scheduling decision requirements of automotive logistics and its developing trends and bottlenecks. Secondly, the paper discusses the roles of systematic model and related model building methods, and further identifying the scientific problems occurring in automotive outbound logistics by analyzing its decisional factors, objectives and constraints. Moreover, the new scientific problems so called "pattern constrained bin-packing" are proposed with computational intractability, solvability and key scientific properties. Furthermore, the paper establishes mixed integer linear programming models for practical and theoretical problems, respectively, and develops branching and bound algorithm. In addition, the paper also addresses the technologies and methodologies for time-space decomposition and rolling solutions of large-scale problems, and further proposes the production testing based on real data and stress testing method for the applications of operations research, and shows the results and testing analysis for outbound automobile logistics scheduling. In addition, this paper proposes a distributed, multi-view, multi-system integration intelligent scheduling decision support system, which is deeply integrated with automobile transportation management system and warehouse management system, driven by optimization algorithm engine. Finally, we introduce detail system implementations with deployment, promotions and maintenance, and briefly address related practice-driven scientific research outputs and future directions.
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