To walk or not to walk? Designing intelligent order picking warehouses with collaborative robots

Order picking is a physically demanding, time-consuming, and costly process in most warehouses. To make the process more efficient, recently, ride-on autonomous robotic order pick trucks (collaborative robots or cobots) have been introduced, that assist the order picker. The order picker can ride on the cobot to travel large distances, but when picking, the cobot behaves as a robot and moves autonomously to the next stop location. The question is where the order picker should get on the cobot (step-on location) to ride further. Using traditional low-level order pick trucks, the order picker rides to every stop location and steps on the truck every time immediately after depositing the picked item on the pick pallet or roll cage. Although riding the truck may be faster than walking, stepping off/on the truck is time-consuming and also demanding for order pickers as it puts much pressure on the knee joints. The cobots allow reducing both travel time (compared to walking only) and knee flexion (compared to riding only). We determine the benefits of choosing optimal step-on locations by formulating an optimization model to minimize total time, including a penalty on the number of knee flexes of the order picker. Since the problem is computationally intractable for large-sized problems, we propose a dynamic programming approach which finds the shortest path of subproblems in each aisle. We find that the optimal collaboration strategy will decrease total travel time, as well as knee flexion of the order picker. Based on Monte Carlo simulation, our results indicate time savings up to 27.9% for one-block and 26.5% for two-block warehouses compared to a heuristic from practice. Based on the data and working practice we obtained from a retail warehouse, the optimal collaboration strategy can improve current practice between 14.5% and 24.1%.