Real-Time Vehicle Routing and Scheduling Problems

实时车辆路径和调度问题

• 批准号: 9713682
• 负责人: Patrick Jaillet
• 金额: $ 17.53万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9713682&HistoricalAwards=false
• 关键词: Real; Time; Vehicle; Routing; Scheduling

9713682 Jaillet This research investigates key aspects of real-time dynamic problems in logistics and transportation. The goals of the research are to (1) systematically analyze and integrate the most promising paradigms/solution strategies for dealing with real-time vehicle routing and scheduling problems, (2) formulate adequate corresponding generic models, and (3) develop, analyze, and implement algorithms for solving them. The focus of the research is on the rigorous mathematical analysis of canonical models which would capture the essence of this new class of problems. A main theme of the methodology will be on the integration of a priori optimization, on-line strategy, distributed local heuristics, randomization, and Markov decision process. Interest in the development of real-time and dynamic models of fleet operations and of fleet management systems which are responsive to change in demand, traffic network and other conditions is emerging in many industries and for a wide variety of applications. Managing and making use of the vast quantities of real-time information made available by navigation technologies, satellite positioning systems, automatic vehicle identification systems and spatial GIS databases requires the development of new models and algorithms. This research holds the potential for significant advances in both the theory and applications of such models and algorithms.

9713682 Jaillet 这项研究调查了物流和运输中实时动态问题的关键方面。 研究的目标是（1）系统地分析和集成处理实时车辆路径和调度问题的最有前途的范式/解决策略，（2）制定适当的相应通用模型，以及（3）开发、分析和实现解决这些问题的算法。 研究的重点是对规范模型进行严格的数学分析，以捕捉此类新问题的本质。 该方法的一个主题是先验优化、在线策略、分布式局部启发式、随机化和马尔可夫决策过程的集成。 在许多行业和各种应用中，人们对开发实时和动态的车队运营模型和车队管理系统产生了兴趣，这些模型可以响应需求、交通网络和其他条件的变化。 管理和利用导航技术、卫星定位系统、自动车辆识别系统和空间地理信息系统数据库提供的大量实时信息需要开发新的模型和算法。 这项研究有望在此类模型和算法的理论和应用方面取得重大进展。