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

Kaibo Liang, Li Zhou, Jianglong Yang, Huwei Liu (), Yakun Li, Fengmei Jing, Man Shan and Jin Yang
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Kaibo Liang: School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China
Li Zhou: School of Information, Beijing Wuzi University, Beijing 101149, China
Jianglong Yang: School of Information, Beijing Wuzi University, Beijing 101149, China
Huwei Liu: School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China
Yakun Li: School of Information, Beijing Wuzi University, Beijing 101149, China
Fengmei Jing: School of Information, Beijing Wuzi University, Beijing 101149, China
Man Shan: School of Information, Beijing Wuzi University, Beijing 101149, China
Jin Yang: School of Information, Beijing Wuzi University, Beijing 101149, China

Mathematics, 2023, vol. 11, issue 7, 1-29

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.

Keywords: multi-AGV dynamic order batch; task assignment; picking table; ant colony optimization; flexsim simulation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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