Order picking is the single most cost-intensive activity in picker-to-parts warehouses, and as such has garnered large interest from the scientific community which led to multiple problem formulations and a plethora of algorithms published. Unfortunately, most of them are not applicable at the scale of really large warehouses like those operated by Zalando, a leading European online fashion retailer.
Based on our experience in operating Zalando's batching system, we propose a novel batching problem formulation for mixed-shelves, large scale warehouses with zoning. It brings the selection of orders to be batched into the scope of the problem, making it more realistic while at the same time increasing the optimization potential.
We present two baseline algorithms and compare them on a set of generated instances. Our results show that first, even a basic greedy algorithm requires significant runtime to solve real-world instances and second, including order selection in the studied problem shows large potential for improved solution quality.
Modified Dynamic Programming Algorithms for Order Picking in Single-Block and Two-Block Rectangular Warehouses
University of Newcastle（纽卡斯尔大学）；University of South Florida（南佛罗里达大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；International Computer Science Institute, University of California at Berkeley（加州大学伯克利分校国际计算机科学研究所）
Improved formulations of the joint order batching and picker routing problem
订单拣选是从仓库的存储位置检索订购产品的过程。在拣货员到零件的订单拣货系统中，两个或多个客户订单可以被分组并分配给单个拣货员。然后，必须做出关于拣货行程期间物品的访问顺序的路由决策。（J.Won和S.Olafsson 2005）发现，与单独和顺序解决这两个问题相比，解决配送和路由的集成问题使仓库管理员能够更有效地组织订单拣选操作。因此，我们调查的数学规划制定这一综合问题。我们根据Valle，Beasley和da Cunha 2017的发现提出了几种改进的公式，可以显着改善计算结果。更具体地说，我们重建的连接约束，并产生新的切割平面在我们的分支和切割框架。我们还讨论了一些问题的性质，通过研究的图形表示的结构，我们提出了两种类型的附加约束。我们还考虑了这个问题的无逆转的情况。我们提出了有效的配方，通过建立不同的辅助图。最后，我们提出了计算结果公开可用的测试问题，单块和多块仓库配置
Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations
University of South Florida（南佛罗里达大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Carey Business School（凯瑞商学院）；University of Newcastle（纽卡斯尔大学）；School of Information and Physical Sciences（信息与物理科学学院）
A bin packing formulation for the joint batching, routing and sequencing problem
CNRS, Grenoble INP, G-SCOP, University of Grenoble Alpes（法国国家科学研究中心，格勒诺布尔理工学院，G-SCOP，格勒诺布尔阿尔卑斯大学）；Univ. Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRIStAL（里尔大学，法国国家科学研究中心，法国国家信息与自动化研究所，里尔中央理工学院，CRIStAL联合研究实验室）；Department of Mathematics, Computer Science and Physics, University of Udine（乌迪内大学数学、计算机与物理系）；INSA Lyon, Université Lumière Lyon 2, Université Claude Bernard Lyon 1, Université Jean Monnet Saint-Etienne, DISP UR4570（里昂国立应用科学学院，里昂第二大学，里昂第一大学，圣艾蒂安让·莫奈大学，DISP联合研究实验室）
Multi-Robot Routing with Time Windows: A Column Generation Approach
University of Maryland（马里兰大学）；University of Southern California（南加州大学）；Verisk（Verisk公司）；ESG UQAM（魁北克大学蒙特利尔分校 ESG 学院）；GERAD（GERAD研究院）；University of California, Irvine（加州大学欧文分校）；Laminaar（Laminaar公司）
