TY - JOUR
T1 - A genetic-algorithm based method for storage location assignments in mobile rack warehouses
AU - Zhang, Dongwen
AU - Si, Yaqi
AU - Tian, Zhihong
AU - Yin, Lihua
AU - Qiu, Jing
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019
Y1 - 2019
N2 - In recent years, mobile racks or auto robots have been widely used in e-commerce warehouses where storage location assignment is a fundamental problem in the order picking process. The present storage location assignment strategies mainly allocate stocks into various racks according to a specific objective function or the relationships between stocks. These strategies include the random storage assignment strategy (RAS) and the good- clustering storage location assignment strategy (GCAS). In this paper, we first analyze the key factors that affect the efficiency of the order picking system.The results show that the rack- moved-number (RMN) is a significant factor in the order picking process. Then, we propose a genetic- algorithm (GA) based method for the storage location assignment problem which adopts RMN as its fitness function. To find a better solution, we take the natural deduplicated stock sequence of history orders (NDSSHO) as a seed to initialize the population of chromosomes. We also define a specific cross mutation strategy to avoid checking the validity of chromosomes by exchanging selected genes and adjusting new generated chromosomes. At last, we compare the RMN of our proposed method with RAS and GCAS. The experimental results show that the RMN of our proposed method is about 50% less than RAS and GCAS.
AB - In recent years, mobile racks or auto robots have been widely used in e-commerce warehouses where storage location assignment is a fundamental problem in the order picking process. The present storage location assignment strategies mainly allocate stocks into various racks according to a specific objective function or the relationships between stocks. These strategies include the random storage assignment strategy (RAS) and the good- clustering storage location assignment strategy (GCAS). In this paper, we first analyze the key factors that affect the efficiency of the order picking system.The results show that the rack- moved-number (RMN) is a significant factor in the order picking process. Then, we propose a genetic- algorithm (GA) based method for the storage location assignment problem which adopts RMN as its fitness function. To find a better solution, we take the natural deduplicated stock sequence of history orders (NDSSHO) as a seed to initialize the population of chromosomes. We also define a specific cross mutation strategy to avoid checking the validity of chromosomes by exchanging selected genes and adjusting new generated chromosomes. At last, we compare the RMN of our proposed method with RAS and GCAS. The experimental results show that the RMN of our proposed method is about 50% less than RAS and GCAS.
KW - Genetic algorithm
KW - Mobile rack warehouse
KW - Order picking system
KW - Rack-moved-number
UR - http://www.scopus.com/inward/record.url?scp=85081949683&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081949683&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM38437.2019.9013447
DO - 10.1109/GLOBECOM38437.2019.9013447
M3 - Conference article
AN - SCOPUS:85081949683
SN - 2334-0983
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
M1 - 9013447
T2 - 2019 IEEE Global Communications Conference, GLOBECOM 2019
Y2 - 9 December 2019 through 13 December 2019
ER -