TY - GEN
T1 - A supplier selection and order allocation method for online to offline (O2O)e-commerce markets
AU - Hu, Yaoguang
AU - Gu, Qiusheng
AU - Wen, Jingqian
AU - Tang, Yue
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - The rapid development of information technology enables an increasing number of consumers to search and book products/services online first and then to consume them in brick-and-mortar stores. This new ecommerce model is called online to offline (O2O) e-commerce and has received significant managerial and academic attention. Many industries are building up O2O vertically oriented e-commerce platform, and its order allocation model takes the suppliers' income into account. The optimization goal is to minimize the manufacturing cost, transportation cost, delay in product delivery quantity, and maximize the suppliers credit evaluation. Considering the complexity of the model, I designed a genetic algorithm, and combining with heuristic rules to avoid a large amount of illegal neighboring points and initial solutions. Experimental examples show that we can get the balance between the suppliers' income and the cost of the entire supply chain. The algorithm can get stable within permitted operation time satisfaction solution, which is similar with the results of the LINGO optimization result, but the operation time is greatly decreased.
AB - The rapid development of information technology enables an increasing number of consumers to search and book products/services online first and then to consume them in brick-and-mortar stores. This new ecommerce model is called online to offline (O2O) e-commerce and has received significant managerial and academic attention. Many industries are building up O2O vertically oriented e-commerce platform, and its order allocation model takes the suppliers' income into account. The optimization goal is to minimize the manufacturing cost, transportation cost, delay in product delivery quantity, and maximize the suppliers credit evaluation. Considering the complexity of the model, I designed a genetic algorithm, and combining with heuristic rules to avoid a large amount of illegal neighboring points and initial solutions. Experimental examples show that we can get the balance between the suppliers' income and the cost of the entire supply chain. The algorithm can get stable within permitted operation time satisfaction solution, which is similar with the results of the LINGO optimization result, but the operation time is greatly decreased.
KW - O2O
KW - genetic algorithm
KW - order allocation
UR - http://www.scopus.com/inward/record.url?scp=85047469858&partnerID=8YFLogxK
U2 - 10.1109/ICIEA.2017.8283050
DO - 10.1109/ICIEA.2017.8283050
M3 - Conference contribution
AN - SCOPUS:85047469858
T3 - Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
SP - 1359
EP - 1364
BT - Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
Y2 - 18 June 2017 through 20 June 2017
ER -