Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations
42 Pages Posted: 12 Apr 2019 Last revised: 27 Apr 2020
Date Written: April 26, 2020
Conventional optimization algorithms that prescribe order packing instructions (which items to pack in which sequence in which box or bin) focus on bin volume utilization yet tend to overlook human behavioral deviations. We observe that packing workers at Alibaba 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: (i) information deviations stem from workers having superior information relative to the algorithm while (ii) complexity deviations result from workers failing to understand, trust, or execute the algorithmic prescriptions due to their complexity.
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 pro-actively 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.
Keywords: Warehouse Operations, Behavioral Operations, Field Experiment, Retailing
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