Predicting human discretion to adjust algorithmic prescription: a large-scale field experiment in warehouse operations
File(s)Sun_et_al_2022_Predicting_Human_Discretion.pdf (1.12 MB)
Accepted version
Author(s)
Sun, J
Zhang, DJ
Hu, H
Van Mieghem, JA
Type
Journal Article
Abstract
Abstract. Conventional optimization algorithms that prescribe order packing instructions (which items to pack in which sequence in which box) focus on box volume utilization yet tend to overlook human behavioral deviations. We observe that packing workers at the warehouses of the Alibaba Group deviate from algorithmic prescriptions for 5.8% of packages, and these deviations increase packing time and reduce operational efficiency. We posit two mechanisms and demonstrate that they result in two types of deviations: (1) information deviations stem from workers having more information and in turn better solutions than the algorithm; and (2) complexity deviations result from workers’ aversion, inability, or discretion to precisely implement algorithmic prescriptions. We propose a new “human-centric bin packing algorithm” that anticipates and incorporates human deviations to reduce deviations and improve performance. It predicts when workers are more likely to switch to larger boxes using machine learning techniques and then proactively adjusts the algorithmic prescriptions of those “targeted packages.” We conducted a large-scale randomized field experiment with the Alibaba Group. Orders were randomly assigned to either the new algorithm (treatment group) or Alibaba’s original algorithm (control group). Our field experiment results show that our new algorithm lowers the rate of switching to larger boxes from 29.5% to 23.8% for targeted packages and reduces the average packing time of targeted packages by 4:5%. This idea of incorporating human deviations to improve optimization algorithms could also be generalized to other processes in logistics and operations.
Date Issued
2022-02
Online Publication Date
2024-09-30T13:35:33Z
Date Acceptance
2020-07-24
ISSN
0025-1909
Publisher
INFORMS
Start Page
846
End Page
865
Journal / Book Title
Management Science
Volume
68
Issue
2
Copyright Statement
Copyright © 2021, INFORMS
Identifier
https://pubsonline.informs.org/doi/10.1287/mnsc.2021.3990
Publication Status
Published
Date Publish Online
2021-09-10