Integrated Batching, Pick Sequencing and Robot Assignment for Robotic Warehouse Automation

Future warehouses will see robots autonomously picking up all the items ordered by the user and delivering them for packaging, sorting and delivery. This requires warehouses to schedule the robots to autonomously cater to all customer orders. In this paper, we solve the problem of batching orders, where several user orders are clubbed into one to be collectively picked by one robot, scheduling the visit of the robot to pick up the desired items and assigning one robot to carry the operation. Each robot is assumed to have a small payload capacity that restricts the number of orders that can be batched. Each item is assumed to be found at several locations, and the robot can select anyone of the locations. The proposed solution uses heuristics to create initial batches that are further optimised by using Genetic Algorithm. Genetic Algorithm also jointly optimises the batching and item pick sequencing. The selection of one out of several sites is done by using a Dynamic Programming approach. The solution is post processed by using a Dynamic Programming solution for optimally scheduling the robot visits within every batch. The experimental results show that the proposed solution scales to a large number of orders because of the use of heuristics, while the results are better than the use of greedy heuristic approaches.