Research on a Dynamic Task Update Assignment Strategy Based on a “Parts to Picker” Picking System

Abstract

Order picking is a crucial operation in the storage industry, with a significant impact on storage efficiency and cost. Responding quickly to customer demands and shortening picking time is crucial given the random nature of order arrival times and quantities. This paper presents a study on the order-picking process in a distribution center, employing a “parts-to-picker” system, based on dynamic order batching and task optimization. Firstly, dynamic arriving orders with uncertain information are transformed into static picking orders with known information. A new method of the hybrid time window is proposed by combining fixed and variable time windows, and an order consolidation batch strategy is established with the aim of minimizing the number of target shelves for picking. A heuristic algorithm is designed to select a shelf selection model, taking into account the constraint condition that the goods on the shelf can meet the demand of the selection list. Subsequently, task division of multi-AGV is carried out on the shelf to be picked, and the matching between the target shelf and the AGVs, as well as the order of the AGVs to complete the task of picking, is determined. A scheduling strategy model is constructed to consider the task completion time as the incorporation of moving time, queuing time, and picking time, with the shortest task completion time as the objective function and AGV task selection as the decision variable. The improved ant colony algorithm is employed to solve the problem. The average response time of the order batching algorithm based on a hybrid time window is 4.87 s, showing an improvement of 22.20% and 40.2% compared to fixed and variable time windows, respectively. The convergence efficiency of the improved ant colony algorithm in AGV task allocation is improved four-fold, with a better convergence effect. By pre-selecting the nearest picking station for the AGVs, the multi-AGV picking system can increase the queuing time. Therefore, optimizing the static picking station selection and dynamically selecting the picking station queue based on the queuing situation are proposed. The Flexsim simulation results show that the queue-waiting and picking completion times are reduced to 34% of the original, thus improving the flexibility of the queuing process and enhancing picking efficiency.

Optimization of AGV Sorting Systems in Pharmaceutical Distribution: A Two-Stage Package Assignment and Simulation Approach

“…This model proved effective with Solomon's VRPTW benchmarks. Similarly, Liang et al [17] tackled the "parts to picker" warehouse picking system. Proposing a dynamic task update assignment strategy, their approach combined a hybrid time window batching method and shelf selection model to optimize dynamic orders.…”

Hybrid Genetic Algorithms for Order Assignment and Batching in Picking System: A Systematic Literature Review

“…Liang, KB et al [37] detailed a study focused on improving the order picking process within a distribution center through the implementation of a "parts-to-picker" system. The primary goal was to efficiently respond to customer demands while minimizing the picking time, considering the uncertainty in the arrival times and quantities of orders.…”

Optimizing Rack Locations in the Mobile-Rack Picking System: A Method of Integrating Rack Heat and Relevance

“…Meanwhile, the pickers stand still at the picking station, retrieving the SKUs from the racks to fulfill the orders' picking. After picking is completed, the robot needs to transport the racks from the picking station back to the empty location for storage [21]. Therefore, the storage assignment problem of MRPS can be divided into SKUs storage racks optimization and rack location optimization.…”