報告題目：Generalized Riskiness Index in Vehicle Routing under Uncertain Travel Times: Formulations, Properties, and Exact Solution Framework
時間：2024年11月25日上午9:00-10:00
地點：中關村校區主樓216
報告人：張真真
報告人簡介：
張真真，同濟大學經濟與偉德國際1946bv官網副教授、博士生導師。入選上海市高層次人才計劃。長期致力于大規模整數規劃和不確定優化的理論研究與算法設計，及在物流與運輸規劃、智能制造等方面的應用。目前已發表高質量論文30余篇，包括Operations Research、INFORMS Journal on Computing、Transportation Science、Transportation Research Part B、NeurIPS等，主持國家自然科學基金青年項目及優秀青年項目、上海市人才項目和華為、中遠海運科研課題各1項，創新研究群體項目“綜合運輸系統運營管理”骨干成員?，F任管理科學與工程學會交通運輸分會執行秘書長、世界交通大會貨運與物流系統優化技術委員會委員、運籌學會隨機服務與運作管理分會理事，并長期擔任Operations Research，Transportation Science等30多個國際知名期刊的審稿人。
報告內容簡介：
We consider a vehicle routing problem with time windows under uncertain travel times where the goal is to determine routes for a fleet of homogeneous vehicles to arrive at the locations of customers within their stipulated time windows to the maximum extent while ensuring that the total travel cost does not exceed a prescribed budget. Specifically, a novel performance measure that accounts for the riskiness associated with late arrivals at the customers, called the generalized riskiness index (GRI), is optimized. The GRI covers several existing riskiness indices as special cases and generates new ones. We demonstrate its salient managerial and computational properties to motivate it better. We propose alternative set partitioning-based models of the problem. To obtain the optimal solution, we develop an exact solution framework combining route enumeration and branch-price-and-cut algorithms, in which the GRI is dealt with in route enumeration and column generation subproblems. We mainly reduce the solution space by exploiting the GRI and budget constraints’ properties without losing optimality. The proposed method is tested on a collection of instances derived from the literature. The results show that a new instance of the GRI outperforms several existing riskiness indices in mitigating lateness. The exact method can solve instances with up to 100 nodes to optimality. It can consistently solve instances involving up to 50 nodes, outperforming state-of-the-art methods by more than doubling the manageable instance size.
（承辦：管理工程系、科研與學術交流中心、中國運籌學會數據科學與運籌智能分會（籌））
