The online integrated order picking and delivery considering Pickers’ learning effects for an O2O community supermarket

The online integrated order picking and delivery considering Pickers’ learning effects for an O2O community supermarket
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DOI:
10.17632/dx7sds7ms8.3
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发表时间:
2019-02
期刊:
Transportation Research Part E: Logistics and Transportation Review
影响因子:
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通讯作者:
Jun Zhang;Feng Liu;Jiafu Tang;Yanhui Li
Jun Zhang;Feng Liu;Jiafu Tang;Yanhui Li
中科院分区:
其他
文献类型:
--
作者:
Jun Zhang;Feng Liu;Jiafu Tang;Yanhui Li

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线上到线下(O2O)社区超市是中国目前流行的O2O商业模式。由于在线客户订单的小批量、高频率、时敏性和动态到货,许多O2O社区超市面临着如何以最小的工期和配送成本挑选动态到货订单并交付给客户的挑战。为了获得全局最优的订单履行性能,研究了O2O社区超市的在线拣货和配送集成问题,并考虑了拣货者的学习效应以更好地规划集成问题。为了得到一个可行的、高效的调度方案,建立了在线算法A,并从理论上证明了竞争比为2。为了进一步验证算法A在实践中的有效性和效率,我们总结了实际的订单履行规则(命名为A1),并进行了数值实验,将算法A与A1进行了比较。此外,为了评估订单履行过程中的学习效果,拣货员的员工特征也有所不同。结果表明,在不同的情况下,算法A的性能均优于算法A1,考虑拣货者的学习效应对订单履行过程的准确性和可预测性具有重要意义。
The online-to-offline (O2O) community supermarket is currently a popular O2O business model in China. Owing to the small lot-size, high frequency, time-sensitive, and dynamic arrival of online customer orders, many O2O community supermarkets face challenges in how to pick up the dynamic arrival orders and deliver them to customers with minimum makespan and delivery cost. To achieve the global optimal order fulfillment performance, we study the online integrated order picking and delivery problem for an O2O community supermarket, and order pickers’ learning effects are considered to better plan the integrated problem. To propose a feasible and efficient schedule, the online algorithm A is established, and the competitive ratio is proved to be 2 theoretically. To further verify the effectiveness and efficiency of algorithm A in practice, we summarize the actual order fulfillment rules (named A1), and conduct numerical experiments to compare algorithm A with A1. Moreover order pickers’ workforce characteristics are varied to evaluate the learning effects on the order fulfillment process. The results show that Algorithm A performs better than A1 in different situations, and considering pickers’ learning effects is significant for the accuracy and predictability of order fulfillment process.