Data-Driven Transportation Planning for a Large Manufacturing Plant

Analysis and development of a planning support tool for the internal transportation at Tata Steel IJmuiden

Master thesis (2020)

Authors

P.J.A. Joon Civil Engineering & Geosciences

Contributors

R.R. Negenborn Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 1)
J Vleugel Transport and Planning - CEG (supervisor 2)
F. Schulte Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 2)
Faculty
Civil Engineering & Geosciences
Copyright: © 2020 Peter Joon
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Published Date

15-12-2020

Language

English

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Faculty

Civil Engineering & Geosciences

Abstract

Planning the on-site transportation of large manufacturing sites is a complex process. Currently these plans are made based on the on-site logistics constraints rather than on KPIs. Research has been focused on either analysis of system parameters or generally on KPIs, but lacks the combination of both on real-life cases. This gap is closed by taking both system analysis and KPI development into account to develop a working planning tool that can assist planners in a real-life situation. The goal of this study is to gain insights in the on-site transportation planning of large manufacturing plants and their performance measurement. This study answers the question: How can the on-site transportation planning at a large manufacturing plant be improved, by 1.) adding company KPIs and 2.) data-driven decision support based on the parameters of the locality and its constraints? Through the application of the DMADE methodology, this research question is answered. The SCOR performance measurement framework is used to determine the KPIs of the on-site transportation plans. A planning model, classified as a Resource-Constrained Multi-Project Scheduling problem, is formulated as a Mixed-Integer Linear Program. This planning model optimizes the on-site transportation plans for the KPIs, proves the correctness of the KPIs and shows the potential performance increase of on-site transportation plans if constructed by the planning model.