Print Email Facebook Twitter Port Call Efficiency Optimization, Using Data Analysis, Process Mining and Discrete Event Simulation Title Port Call Efficiency Optimization, Using Data Analysis, Process Mining and Discrete Event Simulation Author Mašović, Matti (TU Delft Technology, Policy and Management) Contributor Verbraeck, A. (graduation committee) Lefter, I. (mentor) De Leege, Arjen (graduation committee) Degree granting institution Delft University of Technology Programme Engineering and Policy Analysis Date 2019-08-27 Abstract Port call efficiency (PCE) is an important factor in the port choice of shipping lines. This research strives to contribute to the current work on PCE optimization. This is done by applying relative new techniques on port call related data. A combination of process mining (PM) and discrete event simulation (DEVS) is explored, with the Port of Rotterdam as a case study, to determine how they can contribute to identifying and assessing policies that improve port call efficiency. It is concluded that PM can be used as a tool for monitoring a port’s behaviour and spotting bottlenecks, from which port call efficiency policies are derived. Furthermore, it proved to be a useful method for conformance checking the event engine. In order to assess the identified policies, a discrete event simulation model is created, using a model structure, identified through PM. A policy is tested, where all the tugboats in the port work together as one fleet, instead of multiple fleets. From this, it is concluded that this method is successful in assessing scenarios, whose results can be translated back to real-live decision making. Subject Process Miningdiscrete event simulationAISPort of RotterdamLog Dataport call efficiencyoptimization To reference this document use: http://resolver.tudelft.nl/uuid:235714fc-d2b1-4feb-bbf2-b00c8d01e743 Part of collection Student theses Document type master thesis Rights © 2019 Matti Mašović Files PDF PUBLIC_VERSION_Master_The ... asovic.pdf 2.8 MB Close viewer /islandora/object/uuid:235714fc-d2b1-4feb-bbf2-b00c8d01e743/datastream/OBJ/view