173. Depalletization planning in a wholesale warehouse with uncertain retrieval sequence
Invited abstract in session TA-10: Fulfillment Operations I, stream Supply Chain Management and Production.
Thursday, 8:45-10:15Room: H16
Authors (first author is the speaker)
| 1. | Benjamin Riedel
|
| Lehrstuhl für Wirtschaftsinformatik, insbes. Business Intelligence, Friedrich Schiller Universität Jena | |
| 2. | Simon Emde
|
| Friedrich-Schiller-Universität Jena |
Abstract
Depalletization planning is a critical challenge in wholesale warehouses, where customer
orders are fulfilled from a forward picking area and replenished from a reserve storage
area. The uncertainty of retrieval sequences, limited storage capacity, and heterogene-
ity of items can lead to inefficient depalletization strategies, resulting in significant costs
and delays. To address this challenge, we propose a multiple-scenario approach (MSA)
that generates a set of realizations of future requests and solves a deterministic problem
for each scenario. We show that even solving one deterministic scenario to optimal-
ity is NP-hard, and therefore introduce heuristics to solve the scenarios efficiently. Our
results demonstrate that even a modified MSA approach that doesn’t require solving a
computationally expensive mixed-integer program (MIP) significantly outperforms the
decision rules used in practice, reducing the number of depalletizations needed by 70%.
Furthermore, our modified MSA approach outperforms an optimized version of the pol-
icy function used in practice by 11%. Reducing the number of depalletizations leads to
shorter delays in the picking area, resulting in significant cost savings, better resource
utilization, and improved customer satisfaction.
Keywords
- Stochastic Models
- Warehouse Design, Planning, and Control
- Production and Inventory Systems
Status: accepted
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