Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
E-Commerce Order Fulfillment Problem with Limited Time Window. |
| Συγγραφείς: |
Zhou, Quan1 (AUTHOR) quan.zhou3@mail.mcgill.ca, Gümüş, Mehmet1 (AUTHOR) mehmet.gumus@mcgill.ca, Miao, Sentao2 (AUTHOR) sentao.miao@colorado.edu |
| Πηγή: |
Operations Research. Nov/Dec2025, Vol. 73 Issue 6, p2914-2932. 19p. |
| Θεματικοί όροι: |
*Electronic commerce, *Heuristic, *Warehouses, *Cost control, *Order picking systems, Relaxation methods (Mathematics) |
| Περίληψη: |
In "E-Commerce Order Fulfillment Problem with Limited Time Window," Zhou, Gümüş, and Miao investigate how online retailers can improve order fulfillment by utilizing the limited fulfillment window in middle-mile logistics. The authors first characterize an optimal policy and then, introduce two simple heuristic approaches for fulfillment decisions based on Lagrangian relaxation. They show that the proposed algorithms are asymptotically optimal when the number of demand locations increases. They also discover that integrating the information about remaining fulfillment windows into the decision-making process can yield additional benefits. Their findings suggest that online retailers can gain a competitive advantage by offering customers options, such as a "two-day fulfillment" service instead of a "same-day fulfillment" service, particularly when faced with limited logistical capacities. We explore the optimization of the middle-mile fulfillment process in the context of e-commerce. In collaboration with a prominent e-commerce retailer in North America specializing in electronics and computer products, we develop a stochastic optimization problem to demonstrate how an efficient middle mile can alleviate strain on the critical last mile, leading to cost reduction and improved performance. First, we prove that the optimal policy is of a state-dependent threshold type. However, computing such thresholds is notoriously difficult because of the curse of dimensionality. We then introduce a Lagrangian relaxation-based policy (referred to as threshold Lagrangian relaxation (tLR)) as a heuristic approach for fulfillment decisions and prove its performance guarantee. To validate our findings, we utilize both synthetically generated data sets and data provided by our partner e-commerce retailer, and we conduct two numerical studies comparing the tLR policy with benchmark policies. These studies validate our findings on the performance guarantees of the proposed heuristic approach, highlighting the benefits of multiperiod fulfillment windows and the cost-reducing capabilities of the tLR policy. We conclude by emphasizing the importance of dynamic fulfillment strategies and the considerations that e-commerce companies should take into account when selecting their fulfillment policies. [ABSTRACT FROM AUTHOR] |
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