Developing a decision support tool for the operation of parallel AS/RS during partial downtime

Developing a decision support tool for the operation of parallel AS/RS during partial downtime: A case study at Jumbo Supermarkets

van den Brink, Luc (TU Delft Mechanical Engineering)

Schulte, F. (mentor)
Duinkerken, M.B. (graduation committee)
Jansen, D. (graduation committee)

Delft University of Technology

Mechanical Engineering | Multi-Machine Engineering

2024-05-31

This paper investigates the optimisation of Automated Storage and Retrieval Systems (AS/RS) in warehousing by minimising performance losses during partial downtime. Given the increasing automation in logistics, AS/RS systems play a pivotal role, yet the operation of those systems during partial downtime remains a topic ignored in literature. This research fills this gap by exploring the effects of partial downtime in AS/RS through a reusable Discrete Event Simulation model which was developed in Python. This model incorporates the influence of both upstream and downstream systems, a characteristic notably absent from the limited number of publicly-available AS/RS models. Collaborating with Jumbo Supermarkets, the study utilises their highly automated distribution centre with an Order Consolidation Buffer housing 4 dual-crane AS/RS units as a case study. The study identifies operational policies to mitigate partial downtime effects, developed for scenarios with one or both cranes down within an AS/RS. Results suggest strategic workload distribution adjustments among AS/RS can significantly reduce performance degradation, particularly during high workload periods. After comparing both scenarios, it was concluded that for most scenarios, it is beneficial to keep operating the remaining crane when a crane breaks down, even though this slows down repairs. Overall, this research offers insights into parallel AS/RS dynamics under partial downtime and provides practical guidelines for effective operations.

Parallel AS/RS
Discrete Event Simulation
Downtime
Modelling framework
Upstream and downstream influence
Order Consolidation Buffer
Workload distribution

http://resolver.tudelft.nl/uuid:ea064b46-cd87-42fc-8beb-7adbdd7c3d20

Student theses

master thesis

© 2024 Luc van den Brink