讲座题目：Effective Exact Solution Framework for Routing Optimization with Time Windows and Travel Time Uncertainty

主讲人：章　宇　教授

主持人：殷允强　教授

时　 间：2023年10月26日（星期四）  上午10:00

地　 点：电子科技大学清水河校区，经管楼C103

主讲人简介：

章宇，西南财经大学工商管理学院教授、博导，国家级青年人才。东北大学本科、直博，新加坡国立大学联培博士、博后、访问学者。主要从事物流、供应链、交通、医疗服务运营管理的鲁棒优化与决策研究。主持和参与国家自然科学基金项目4项。在Operations Research (UTD24)、Mathematical Programming、Production and Operations Management (UTD24)、INFORMS Journal on Computing (UTD24)、Transportation Science等权威期刊发表学术论文20余篇。获中国管理科学与工程学会优秀博士学位论文奖、Omega期刊最佳论文奖，单篇论文入选ESI高被引论文。受邀担任OR、POM、JOC等学术期刊审稿人。任中国管理现代化研究会青年工作委员会秘书长、中国系统工程学会物流系统工程分会委员、中国运筹学会决策科学分会理事。为中远、中烟、重庆电力等做项目咨询。

讲座简介：

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 better motivate it. 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. By exploiting the properties of both the GRI and budget constraint, we mainly reduce the solution space without loss of 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, and can consistently solve instances involving up to 50 nodes, outperforming state-of-the-art methods by more than doubling the manageable instance size.

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