| Peer-Reviewed

Order Batching Optimization in Dual Zone Type Warehouse Based on Genetic Algorithms

Received: 13 April 2015     Accepted: 24 April 2015     Published: 2 June 2015
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Abstract

With the rapid development of e-commerce and the global economy, order picking mode of multiple batches and small quantities becoming more and more, which makes artificial picking system occupy a larger proportion in a variety of ways. The optimization study of the artificial person picking system has a crucial role to enhance the efficiency of batch picking, then increasing customer satisfaction. In this paper, according to the characteristics of the problem, Genetic algorithm was selected as the model's algorithm. The detailed design of coding, crossover method, genetic algorithm in solving process variation method, is used to realize the optimization of the model.

Published in Science Journal of Business and Management (Volume 3, Issue 3)
DOI 10.11648/j.sjbm.20150303.13
Page(s) 77-81
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Manual Order Picking System, Order Batching, Genetic Algorithm

References
[1] Sebastian Henn,Algorithms for on-line order batching in an order picking warehouse [J]. Computer & Operations Research, 2012, 2549-2563.
[2] Sebastian Henn, Soren Koch, Karl F. Doerner, ect. Metaheuristics for the Order Batching Problem in the Manual Order Picking System[J]. BuR-Business Research, 2010, Vol.3 (1), pp.82-105
[3] Seval Ene,Nursel Ozturk, Storage location assignment and order picking optimization in the automotive industry[J]. Int J Adv Manuf Technol, 2011, pp.787-797
[4] Osman Kulak,Yusuf Sahin, Mustafa Egemen Taner.Joint order batching and picker routing in single and multiple-cross-aisle warehouse using cluster-based tabu search algorithms[J].Flex Serv Manuf J,2012,(24):52-80
[5] Amir Hossein Azadnia, Shahrooz Taheri, Pezhman Ghadimi, ect. Order Batching in Warehouse by Minimizing Total Tardiness: A Hybrid Approach of Weighted Association Rule Mining and Genetic Algorithms [J]. The Scientific World Journal, 2013, 1-13
[6] Gibson D R,Sharp G.P. Orderbatching Proeedures [J]. EuroPean Journal of Operational Researeh, 2005, 58(l),57-67.
[7] Le-Due, De Koste. Travel distance estimation and storage zone optimization in a 2-bloek class-based storage strategy warehouse [J]. Intemational Joumal of Production Researeh, 2004, 43(17), 3561-3581.
[8] Tho Le-Duc. Design and Control of Efficient Order Picking Processes [M]. Rotterdam: Erasmus University Rotterdam, 2005.
[9] Roodbergen , K.J. and DeKoster,R Routing order Pickers in awarehouse with a middle Aisle[J]. EuroPean Journal of Operational Researeh. 2001, 133, 32-43.
[10] Pratik J Parikh, Russell D Meller. Selecting between batch and zone order picking trategies in a distribution center [J]. Transportation Research Part E, 2008, 44: 696-719.
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  • APA Style

    Jie Zhu, Hong Zhang, Li Zhou, Jian Guo. (2015). Order Batching Optimization in Dual Zone Type Warehouse Based on Genetic Algorithms. Science Journal of Business and Management, 3(3), 77-81. https://doi.org/10.11648/j.sjbm.20150303.13

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    ACS Style

    Jie Zhu; Hong Zhang; Li Zhou; Jian Guo. Order Batching Optimization in Dual Zone Type Warehouse Based on Genetic Algorithms. Sci. J. Bus. Manag. 2015, 3(3), 77-81. doi: 10.11648/j.sjbm.20150303.13

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    AMA Style

    Jie Zhu, Hong Zhang, Li Zhou, Jian Guo. Order Batching Optimization in Dual Zone Type Warehouse Based on Genetic Algorithms. Sci J Bus Manag. 2015;3(3):77-81. doi: 10.11648/j.sjbm.20150303.13

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  • @article{10.11648/j.sjbm.20150303.13,
      author = {Jie Zhu and Hong Zhang and Li Zhou and Jian Guo},
      title = {Order Batching Optimization in Dual Zone Type Warehouse Based on Genetic Algorithms},
      journal = {Science Journal of Business and Management},
      volume = {3},
      number = {3},
      pages = {77-81},
      doi = {10.11648/j.sjbm.20150303.13},
      url = {https://doi.org/10.11648/j.sjbm.20150303.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjbm.20150303.13},
      abstract = {With the rapid development of e-commerce and the global economy, order picking mode of multiple batches and small quantities becoming more and more, which makes artificial picking system occupy a larger proportion in a variety of ways. The optimization study of the artificial person picking system has a crucial role to enhance the efficiency of batch picking, then increasing customer satisfaction. In this paper, according to the characteristics of the problem, Genetic algorithm was selected as the model's algorithm. The detailed design of coding, crossover method, genetic algorithm in solving process variation method, is used to realize the optimization of the model.},
     year = {2015}
    }
    

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    T1  - Order Batching Optimization in Dual Zone Type Warehouse Based on Genetic Algorithms
    AU  - Jie Zhu
    AU  - Hong Zhang
    AU  - Li Zhou
    AU  - Jian Guo
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    DO  - 10.11648/j.sjbm.20150303.13
    T2  - Science Journal of Business and Management
    JF  - Science Journal of Business and Management
    JO  - Science Journal of Business and Management
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    EP  - 81
    PB  - Science Publishing Group
    SN  - 2331-0634
    UR  - https://doi.org/10.11648/j.sjbm.20150303.13
    AB  - With the rapid development of e-commerce and the global economy, order picking mode of multiple batches and small quantities becoming more and more, which makes artificial picking system occupy a larger proportion in a variety of ways. The optimization study of the artificial person picking system has a crucial role to enhance the efficiency of batch picking, then increasing customer satisfaction. In this paper, according to the characteristics of the problem, Genetic algorithm was selected as the model's algorithm. The detailed design of coding, crossover method, genetic algorithm in solving process variation method, is used to realize the optimization of the model.
    VL  - 3
    IS  - 3
    ER  - 

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Author Information
  • School of Information, Beijing Wuzi University, Beijing, China

  • School of Information, Beijing Wuzi University, Beijing, China

  • School of Information, Beijing Wuzi University, Beijing, China

  • School of Information, Beijing Wuzi University, Beijing, China

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