We describe theoretical and data-driven work in collaboration with a large online retailer. In a warehouse, multi-item orders take up capacity on a wall until all items are picked. To avoid exceeding capacity, the retailer must balance picking efficiency with completing orders quickly. We build a data-driven simulation to model the dynamics of this warehouse picking problem. To provide a theoretical framework, we propose a batch scheduling model that captures the core aspects of the problem. We seek to minimise average order completion times, where the processing time of each batch is given by a submodular function, generalising previous problems in the literature. This turns out to be NP-hard to solve, but we develop approximation algorithms. Computational experiments suggest that the approximation algorithms perform much better than the guarantees suggest. Based on this model, we propose a policy that we test on our simulation. Preliminary results suggest that the policy may reduce the time orders stay at the wall, and reduce average wall utilisation.
Daniel is a fifth-year PhD student at the MIT Operations Research Center, advised by Professor Retsef Levi and Professor Georgia Perakis. He is currently working on a batch scheduling problem as applied to a warehouse picking problem. This is in collaboration with a large online retailer. More broadly, Daniel is interested in formulating and solving mathematical optimisation models to aid decision making for real world applications, particularly in the area of operations management. He previously obtained a bachelor degree in Mathematics at the University of Cambridge, and has also completed research internships at Amazon and the Institute for Infocomm Research.
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