A genetic-algorithm based method for storage location assignments in mobile rack warehouses

Dongwen Zhang, Yaqi Si, Zhihong Tian, Lihua Yin, Jing Qiu, Xiaojiang Du

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Article number9013447
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
StatePublished - 2019
Event2019 IEEE Global Communications Conference, GLOBECOM 2019 - Waikoloa, United States
Duration: 9 Dec 201913 Dec 2019

Keywords

  • Genetic algorithm
  • Mobile rack warehouse
  • Order picking system
  • Rack-moved-number

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