Research of order allocation model based on cloud and hybrid genetic algorithm under ecommerce environment |
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Authors: | Qiang Huang Xin-yuan Lou Wei Wang Shao-quan Ni |
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Institution: | 1. School of Traffic and Transportation, Southwest Jiaotong University, Chengdu 610031, China;School of Information and Engineering, Sichuan Agricultural University, Ya'an 625014, Sichuan, China 2. School of Information and Engineering, Southwest Jiaotong University, Chengdu 610031, China 3. Department of Electronic Commerce, Sichuan Finance and Economic Vocational College, Chengdu 610101, China 4. School of Traffic and Transportation, Southwest Jiaotong University, Chengdu 610031, China |
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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. |
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Keywords: | order allocation cloud architecture hybrid genetic algorithm (GA) MapReduce (MR) |
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