We consider a 2-vehicle routing problem (2VRP) which can be viewed as a building block for the variety of vehicle routing problems (VRP). As a simplified version of the 2VRP, we consider a 2-period balanced travelling salesman problem (2TSP) and describe a polynomially solvable case of this NP-hard problem. For the 2VRP with general settings, we suggest a framework based on the Held and Karp dynamic programming algorithm. Our algorithms based on this framework show an exceptionally good performance on the published test data. Our approach can be easily extended to a variety of constraints/attributes in the VRP, hence the wording "small but rich" in the title of our paper. We also introduce a new methodological approach: we use easy solvable special cases for generating test instances and then use these instances in computational experiments.
Spatial-temporal-demand clustering for solving large-scale vehicle routing problems with time windows
Logistics and Supply Chain Management, School of Management, Technical University of Munich（物流与供应链管理系，管理学院，慕尼黑技术大学）；Munich Data Science Institute (MDSI), Technical University of Munich（慕尼黑数据科学研究所（MDSI），慕尼黑技术大学）
Contextual Stochastic Vehicle Routing with Time Windows
School of Management, Technical University of Munich（慕尼黑工业大学管理学院）；Amazon.com, Inc.（亚马逊公司）；Munich Data Science Institute, Technical University of Munich（慕尼黑工业大学慕尼黑数据科学研究所）；Polytechnique Montréal（蒙特利尔理工学院）
Learning to Insert for Constructive Neural Vehicle Routing Solver
School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China（自动化与智能制造学院，南方科技大学，深圳，中国）；Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, Southern University of Science and Technology, Shenzhen, China（广东省全驱动系统控制理论与技术重点实验室，南方科技大学，深圳，中国）；Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China（计算机科学系，香港城市大学，香港特别行政区，中国）；School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China（数学与统计学学院，西安交通大学，西安，中国）
