Optimal decision of multiobjective and multiperiod anticipatory shipping under uncertain demand: A data-driven framework
Anticipatory shipping helps to reduce the waiting time of online customers to receive their products. Present studies on anticipatory shipping mainly consider a single period and ignore the waiting time saved for customers. This paper develops a data-driven framework to investigate the multiperiod a...
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| Vydané v: | Computers & industrial engineering Ročník 159; s. 107445 |
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| Jazyk: | English |
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Elsevier Ltd
01.09.2021
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| ISSN: | 0360-8352, 1879-0550 |
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| Abstract | Anticipatory shipping helps to reduce the waiting time of online customers to receive their products. Present studies on anticipatory shipping mainly consider a single period and ignore the waiting time saved for customers. This paper develops a data-driven framework to investigate the multiperiod anticipatory shipping problem with the objectives of minimizing the cost of online retailers and maximizing the saved waiting time of customers. First, we propose a dual-process sales forecasting framework that employs five machine learning algorithms to forecast online retailers’ daily sales using clickstream data and historical sales data. Then, we build a multiperiod and multiobjective integer programming model based on the forecasting sales and forecasting errors to explore the optimal quantity and time of products of anticipatory shipping. Finally, using TOPSIS, Shannon entropy, and LINMAP decision-making methods, the final optimal solution is selected from the Pareto set obtained by the NSGA-II algorithm. The case study results show that anticipatory shipping saves 5.96% cost for the online retailer and 1.69 days of waiting time on average for the customer compared with non-anticipatory shipping. Moreover, performing anticipatory shipping is especially beneficial in the case of high inventory holding cost, large distribution center capacity, and long transportation duration. |
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| AbstractList | Anticipatory shipping helps to reduce the waiting time of online customers to receive their products. Present studies on anticipatory shipping mainly consider a single period and ignore the waiting time saved for customers. This paper develops a data-driven framework to investigate the multiperiod anticipatory shipping problem with the objectives of minimizing the cost of online retailers and maximizing the saved waiting time of customers. First, we propose a dual-process sales forecasting framework that employs five machine learning algorithms to forecast online retailers’ daily sales using clickstream data and historical sales data. Then, we build a multiperiod and multiobjective integer programming model based on the forecasting sales and forecasting errors to explore the optimal quantity and time of products of anticipatory shipping. Finally, using TOPSIS, Shannon entropy, and LINMAP decision-making methods, the final optimal solution is selected from the Pareto set obtained by the NSGA-II algorithm. The case study results show that anticipatory shipping saves 5.96% cost for the online retailer and 1.69 days of waiting time on average for the customer compared with non-anticipatory shipping. Moreover, performing anticipatory shipping is especially beneficial in the case of high inventory holding cost, large distribution center capacity, and long transportation duration. |
| ArticleNumber | 107445 |
| Author | chen, Cheng Zou, Bipan Xu, Xianhao Peng, Hongxia Li, Zhiwen |
| Author_xml | – sequence: 1 givenname: Cheng surname: chen fullname: chen, Cheng organization: School of Management, Huazhong University of Science and Technology, Wuhan 430074, China – sequence: 2 givenname: Xianhao surname: Xu fullname: Xu, Xianhao email: xxhao@hust.edu.cn organization: School of Management, Huazhong University of Science and Technology, Wuhan 430074, China – sequence: 3 givenname: Bipan surname: Zou fullname: Zou, Bipan organization: School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430074, China – sequence: 4 givenname: Hongxia surname: Peng fullname: Peng, Hongxia organization: School of Business, Hubei University, Wuhan 430062, China – sequence: 5 givenname: Zhiwen surname: Li fullname: Li, Zhiwen organization: School of Management, Huazhong University of Science and Technology, Wuhan 430074, China |
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| Keywords | Anticipatory shipping Multiobjective Online retail Data-driven Integer programming model |
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