An order-picking operations system for managing the batching activities in a warehouse

Abstract

Nowadays, customer orders with high product variety in small quantities are often received and requested for timely delivery. However, the order-picking process is a labour-intensive and costly activity to handle those small orders separately. In such cases, small orders are often grouped into batches so that two or more orders can be served at once to increase the picking efficiency and thus reduce the travel distance. In this paper, an order-picking operations system (OPOS) is proposed to assist the formulation of an order-picking plan and batch-handling sequence. The study integrates a mathematical model and fuzzy logic technique to divide the receiving orders into batches and prioritise the batch-handling sequence for picking, respectively. Through the proposed system, the order-picking process can be managed as batches with common picking locations to minimise the travel distance, and the batch-picking sequence can be determined as well. To demonstrate the use of the system, a case study in a third-party logistics warehouse is presented, and the result shows that both the order-picking activity and labour utilisation can be better organised.

Keywords:

• order picking
• order batching
• distribution logistics
• fuzzy logic

Acknowledgements

The authors would like to thank the Research Office of the Hong Kong Polytechnic University for supporting this project (project code: RPGM).

Additional information

Notes on contributors

Cathy H.Y. Lam

Cathy H.Y. Lam is currently a Research Student in the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University. She received her Bachelor's degree from the Hong Kong Polytechnic University in 2008. Her current research areas cover supply chain management, decision support system and artificial intelligence applications.

K.L. Choy

K.L. Choy is an Associate Professor in the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University. He gained his MSc degrees in Manufacturing Systems Engineering and in Management Science and his MPhil in Engineering at the University of Warwick, UK in the 1990s and a Doctorate degree at the Hong Kong Polytechnic University in 2003. He has published more than 80 international journal papers in the areas of logistics information, data systems, supply chain management and technology management, as well as applying expert systems to industry.

G.T.S. Ho

G.T.S. Ho is currently a Lecturer in the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University. He received his BEng (Hons) in Manufacturing Engineering and PhD in Industrial and Systems Engineering from the Hong Kong Polytechnic University. His research interests range between data mining, artificial intelligent systems, logistics workflow optimization, logistics and supply chain management. During the years of research, Dr. Ho has published more than 50 papers in both international journals and conference publications.

C.K.M. Lee

C.K.M. Lee is currently an Assistant Professor in the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong. She obtained her PhD and B. Eng degree from The Hong Kong Polytechnic University. Dr Lee has authored or co-authored more than 100 journal and conferences papers. Her current research areas include logistics information management, manufacturing information systems, product development and data mining techniques.