Abstract
For massive order allocation problem of the third party logistics (TPL) in ecommerce, this paper proposes a general order allocation model based on cloud architecture and hybrid genetic algorithm (GA), implementing cloud deployable MapReduce (MR) code to parallelize allocation process, using heuristic rule to fix illegal chromosome during encoding process and adopting mixed integer programming (MIP) as fitness function to guarantee rationality of chromosome fitness. The simulation experiment shows that in mass processing of orders, the model performance in a multi-server cluster environment is remarkable superior to that in stand-alone environment. This model can be directly applied to cloud based logistics information platform (LIP) in near future, implementing fast auto-allocation for massive concurrent orders, with great application value.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Shen Xiao-ping, Lu Shao-ping, Nie Wei. Introduction to logistics [M]. Wuhan: Huazhong University of Science and Technology Press, 2010: 56–78 (in Chinese).
Wang Li-hong. Literature review on the assessment of logistic customer service effect from the perspective of the supply chain [J]. Journal of Hubei University (Philosophy and Social Science), 2012, 39(1): 94–98 (in Chinese).
Shi Wei. Study on TPL order processing system [J]. Journal of Shandong Institute of Business and Technology, 2009, 23(1): 52–54 (in Chinese).
Pallis G. Cloud computing: The new frontier of internet computing [J]. IEEE Internet Computing, 2010, 14(5): 70–73.
Feng Wei-dong, Chen Jian, Zhao Chun-jun. Partners’ selection process and optimization model for virtual corporations based on genetic algorithms [J]. Journal of Tsinghua University (Science and Technology), 2000, 40(10): 120–124 (in Chinese).
Cheng Fang-qi, Wang Hong-fei, Ye Fei-fan. Research on order allocation model for horizontal virtual enterprise [J]. Mechanical & Electrical Engineering Magazine, 2009, 26(4): 50–52. (in Chinese)
Dai Zhi-guo, Peng Wei-ping, Wu Wu-tao. Study of logistics management system for order manufacture enterprise [J]. Logistics Sci-Tech, 2008, 31(7): 11–13 (in Chinese).
Tian Yong-qing, Yang Bin, Li Zhi, et al. An algorithm of mining association rules based on cloud model in relational databases [J]. Journal of Shanghai Jiaotong University, 2003, 37(4): 512–515 (in Chinese).
Li Jian-feng, Peng Jian. Task scheduling algorithm based on improved genetic algorithm in cloud computing environment [J]. Journal of Computer Applications, 2011, 31(1): 184–186 (in Chinese).
Foster I, Zhao Y, Raicu I, et al. Cloud computing and grid computing 360-degree compared [C]//Proceedings of the 2008 Grid Computing Environments Workshop. Washington, DC: IEEE Computer Society, 2008: 1–10.
Randles M, Lamb D, Taleb-Bendiab A. A comparative study into distributed load balancing algorithms for cloud computing [C]//2010 IEEE 24th International Conference on Advanced Information Netwoking and Applications Workshops. Perth, Australia: IEEE, 2010: 551–556.
Ji Xiao-li. Order allocation model in supply chain and hybrid genetic algorithm [J]. Journal of Southwest Jiaotong University, 2005, 40(6): 811–815 (in Chinese).
Cusumano M. Cloud computing and SaaS as new computing platforms [J]. Communications of the ACM, 2010, 53(4): 27–29.
Liu J J, So S C K, Choy K L, et al. Performance improvement of third-party logistics providers — An integrated approach with a logistics information system [J]. International Journal of Technology Management, 2008, 42(3): 226–249.
Author information
Authors and Affiliations
Additional information
Foundation item: the National Science & Technology Pillar Program (Nos. 2011BAH21B02 and 2011BAH21B03) and the Chengdu Major Scientific and Technological Achievements (No. 11zHzD038)
Rights and permissions
About this article
Cite this article
Huang, Q., Lou, Xy., Wang, W. et al. Research of order allocation model based on cloud and hybrid genetic algorithm under ecommerce environment. J. Shanghai Jiaotong Univ. (Sci.) 18, 334–342 (2013). https://doi.org/10.1007/s12204-013-1403-4
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12204-013-1403-4