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运作优化与智能决策系列前沿讲座第 5期

作者:运作优化与智能决策系研究所  来源:管科系   /  更新日期: 2019-07-02  点击量:509

讲座题目A Benders Decomposition Approach for the Multi-Vehicle Production Routing Problem

 主讲人 罗志兴 副教授

主持人 殷允强 教授

时间2019712日(星期五)上午10: 30

地点:电子科技大学清水河校区,经管楼C301


讲座简介 

The Production Routing Problem (PRP) arises in the applications of integrated supply chain which jointly optimizes the production, inventory, distribution, and routing decisions. The literature on this problem is quite rare due to its complexity. In this talk, we consider the multi-vehicle PRP (MVPRP) with Order-Up to level inventory replenishment policy where, every time a customer is visited, the quantity delivered is such that the maximum inventory level is reached. We propose an exact Benders’ decomposition approach to solve the MVPRP, which decomposes the problem as a master problem and a slave problem. The master problem decides whether to produce the product, the quantity to be produced, and the customers to be replenished for every period of the planning horizon. The resulting slave problem decomposes into a Capacitated Vehicle Routing Problem for each period of the planning horizon where each problem is solved using an exact algorithm based on the set partitioning model, and the identified feasibility and optimality cuts are added to the master problem to guide the solution process. Valid inequalities and initial optimality cuts are used to strengthen the LP-relaxation of the master formulation. The exact method is tested on MVPRP instances and on instances of the multi-vehicle Vendor-Managed Inventory Routing Problem, a special case of the MVPRP, and the good performance of the proposed approach is demonstrated.

 

主讲人简介

罗志兴博士于2010年在华南理工大学计算机科学与工程学院获得学士学位(本科三年毕业),于2014年在香港城市大学商学院获得博士学位(博士四年毕业),同年加入南京大学工程管理学院,现任副教授,主要研究领域包括运筹优化算法设计、智慧物流、智能制造等。主持国家自然科学基金项目1项,发表SCI/SSCI国际著名期刊论文15篇,其中5篇以第一或通信作者身份发表在运筹优化和交通运输领域顶级期刊INFORMS Journal on Computing、Transportation Science以及Transportation Research Part-B: Methodological。2018年参加京东举办的“全球运筹优化挑战赛”,获得城市物流运输车辆智能调度优化全球总冠军。



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经济与管理学院

运作优化与智能决策研究所

 2019年7月2日