Study on Order Batching Model Design Based on Hopfield Neural Network

Hong Zhang; Jie Zhu; Li Zhou

doi:doi:10.11648/j.sjbm.20150302.12

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Study on Order Batching Model Design Based on Hopfield Neural Network

Hong Zhang, Jie Zhu, Li Zhou

Published in Science Journal of Business and Management (Volume 3, Issue 2)

Received: 11 April 2015 Accepted: 18 April 2015 Published: 4 May 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. For order batching problem, according to scholars in the study of this problem, including taking the picking equipment capacity and load restrictions into account rarely. In the paper, Hopfield Neural Network algorithm for sorting equipment were chosen to establish a capacity constraint order batching model which taking shortest path of all orders as the objective function and maximum equipment utilization order batching model.

Published in	Science Journal of Business and Management (Volume 3, Issue 2)
DOI	10.11648/j.sjbm.20150302.12
Page(s)	60-64
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, Stochastic Service System, Hopfield Neural Network

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

Hong Zhang, Jie Zhu, Li Zhou. (2015). Study on Order Batching Model Design Based on Hopfield Neural Network. Science Journal of Business and Management, 3(2), 60-64. https://doi.org/10.11648/j.sjbm.20150302.12

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ACS Style

Hong Zhang; Jie Zhu; Li Zhou. Study on Order Batching Model Design Based on Hopfield Neural Network. Sci. J. Bus. Manag. 2015, 3(2), 60-64. doi: 10.11648/j.sjbm.20150302.12

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AMA Style

Hong Zhang, Jie Zhu, Li Zhou. Study on Order Batching Model Design Based on Hopfield Neural Network. Sci J Bus Manag. 2015;3(2):60-64. doi: 10.11648/j.sjbm.20150302.12

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@article{10.11648/j.sjbm.20150302.12,
  author = {Hong Zhang and Jie Zhu and Li Zhou},
  title = {Study on Order Batching Model Design Based on Hopfield Neural Network},
  journal = {Science Journal of Business and Management},
  volume = {3},
  number = {2},
  pages = {60-64},
  doi = {10.11648/j.sjbm.20150302.12},
  url = {https://doi.org/10.11648/j.sjbm.20150302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjbm.20150302.12},
  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. For order batching problem, according to scholars in the study of this problem, including taking the picking equipment capacity and load restrictions into account rarely. In the paper, Hopfield Neural Network algorithm for sorting equipment were chosen to establish a capacity constraint order batching model which taking shortest path of all orders as the objective function and maximum equipment utilization order batching model.},
 year = {2015}
}

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TY  - JOUR
T1  - Study on Order Batching Model Design Based on Hopfield Neural Network
AU  - Hong Zhang
AU  - Jie Zhu
AU  - Li Zhou
Y1  - 2015/05/04
PY  - 2015
N1  - https://doi.org/10.11648/j.sjbm.20150302.12
DO  - 10.11648/j.sjbm.20150302.12
T2  - Science Journal of Business and Management
JF  - Science Journal of Business and Management
JO  - Science Journal of Business and Management
SP  - 60
EP  - 64
PB  - Science Publishing Group
SN  - 2331-0634
UR  - https://doi.org/10.11648/j.sjbm.20150302.12
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. For order batching problem, according to scholars in the study of this problem, including taking the picking equipment capacity and load restrictions into account rarely. In the paper, Hopfield Neural Network algorithm for sorting equipment were chosen to establish a capacity constraint order batching model which taking shortest path of all orders as the objective function and maximum equipment utilization order batching model.
VL  - 3
IS  - 2
ER  -

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Author Information

Hong Zhang

School of Information, Beijing Wuzi University, Beijing, China
Jie Zhu

School of Information, Beijing Wuzi University, Beijing, China
Li Zhou

School of Information, Beijing Wuzi University, Beijing, China

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APA Style

Hong Zhang, Jie Zhu, Li Zhou. (2015). Study on Order Batching Model Design Based on Hopfield Neural Network. Science Journal of Business and Management, 3(2), 60-64. https://doi.org/10.11648/j.sjbm.20150302.12

Copy | Download

ACS Style

Hong Zhang; Jie Zhu; Li Zhou. Study on Order Batching Model Design Based on Hopfield Neural Network. Sci. J. Bus. Manag. 2015, 3(2), 60-64. doi: 10.11648/j.sjbm.20150302.12

Copy | Download

AMA Style

Hong Zhang, Jie Zhu, Li Zhou. Study on Order Batching Model Design Based on Hopfield Neural Network. Sci J Bus Manag. 2015;3(2):60-64. doi: 10.11648/j.sjbm.20150302.12

Copy | Download

@article{10.11648/j.sjbm.20150302.12,
  author = {Hong Zhang and Jie Zhu and Li Zhou},
  title = {Study on Order Batching Model Design Based on Hopfield Neural Network},
  journal = {Science Journal of Business and Management},
  volume = {3},
  number = {2},
  pages = {60-64},
  doi = {10.11648/j.sjbm.20150302.12},
  url = {https://doi.org/10.11648/j.sjbm.20150302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjbm.20150302.12},
  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. For order batching problem, according to scholars in the study of this problem, including taking the picking equipment capacity and load restrictions into account rarely. In the paper, Hopfield Neural Network algorithm for sorting equipment were chosen to establish a capacity constraint order batching model which taking shortest path of all orders as the objective function and maximum equipment utilization order batching model.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Study on Order Batching Model Design Based on Hopfield Neural Network
AU  - Hong Zhang
AU  - Jie Zhu
AU  - Li Zhou
Y1  - 2015/05/04
PY  - 2015
N1  - https://doi.org/10.11648/j.sjbm.20150302.12
DO  - 10.11648/j.sjbm.20150302.12
T2  - Science Journal of Business and Management
JF  - Science Journal of Business and Management
JO  - Science Journal of Business and Management
SP  - 60
EP  - 64
PB  - Science Publishing Group
SN  - 2331-0634
UR  - https://doi.org/10.11648/j.sjbm.20150302.12
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. For order batching problem, according to scholars in the study of this problem, including taking the picking equipment capacity and load restrictions into account rarely. In the paper, Hopfield Neural Network algorithm for sorting equipment were chosen to establish a capacity constraint order batching model which taking shortest path of all orders as the objective function and maximum equipment utilization order batching model.
VL  - 3
IS  - 2
ER  -

Copy | Download