Development of a Decision Support Tool for the Storage Policy of a Robotic Mobile Fulfilment System

A CEVA Logistics Den Haag Case Study

Master thesis (2023)

Authors

M.K. Baran Mechanical Engineering

Contributors

F. Schulte Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 1)
A. Napoleone Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 2)
P.A. Wenzel Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 2)
X. Tang Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 2)
S. Vogelaar CEVA Logistics (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2023 Mitchel Baran
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Published Date

04-09-2023

Language

English

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Faculty

Mechanical Engineering

Abstract

This paper focuses on the effect of different storage policies on the performance of RoboticMobile Fulfilment Systems (RMFSs). The research is conducted under the instructions of the Technical University of Delft and CEVA Logistics the Hague. The aim of the research is to develop a decision support tool that can aid companies in the choice of the storage policy to use for their RMFS. RMFSs have multiple different levels of decision problems that need to be solved. The performance of a RMFS highly depends on the algorithms that are applied to solve these decision problems. This research focuses only on the storage policies of a RMFS. In this case storage policy refers to the decision in which pod items should be stored and where on the storage area the pod should be positioned. In order to gain a good understanding of the effect of such a policy on the overall performance of a RMFS, experiments should be performed. Physical experiments are however very hard and costly to perform. This research therefore makes use of a simulation study to test different storage policies in different scenarios. The simulation model used is an adaptation on an agent-based semi-open queuing network framework model by Merschformann et al. (2018a). In the experiments four different storage policies are investigated under three different storage layouts. The results of the simulations are analysed via the throughput, the pile-on and the distance travelled during the simulation. After this a score is given to
both the picking as well as the replenishment side of the system. It is important to investigate both sides of the system since the flaws on one side can negatively affect the other side. The scores of the replenishment and picking processes are then averaged to gain a final score which indicates the overall performance of the
policies. Finally a fifth policy has been developed where each pod can contain multiple different sized compartments. Unfortunately testing and verification of this policy was not possible in the given time frame. For this reason it has been left out of the experiments.