A Distance-Reduction Trajectory Tracking Control Algorithm for a Rear-Steered AGV

This paper presents a Lyapunov-based switched trajectory tracking control design for a rear-steered automated guided AGV (AGV). Given a moving reference whose position and orientation have to be tracked by the AGV, the main objective of the controller is to reduce AGV’s distance from the reference while adjusting its orientation. The distance reduction issue is important, especially in huge warehouses operating a group of AGVs, since the rate of AGV-to-reference distance reduction contributes to the possibility of AGV-to-AGV collision. A set of control algorithms is proposed to handle large AGV’s orientation. Simulations that show the performance of the proposed method is presented

Increasing the Revenue of Self-Storage Warehouses by Optimizing Order Scheduling

We consider a self-storage warehouse, facing storage orders for homogeneous or heterogeneous storage units over a certain time horizon. The warehouse operations manager needs to decide which storage orders to accept and schedule them across different storage units to maximize revenue. We model warehouse operations as scheduling n independent multiprocessor tasks with given start and end times, with an objective to maximize revenue. With operational constraints like the maximal upscaling level, precedence order constraints, and maximal idle time, the established mixed-integer program cannot be efficiently solved by commercial softwares. We therefore propose a column generation approach and a branch-and-price method to find an optimal schedule. Computational experiments show that, compared with current methods in self-storage warehouses, our method can significantly increase the revenue