Container terminal optimization: Pareto front optimization for maximum port area utilization

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dc.contributor.authorHauptmann, Johannes
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mechanics and Maritime Sciencesen
dc.contributor.examinerForsberg, Peter
dc.contributor.supervisorForsberg, Peter
dc.date.accessioned2024-06-24T12:00:54Z
dc.date.available2024-06-24T12:00:54Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractLarge quantities of containers are moved in ports around the world every day, motivating a focus on transport efficiency to optimize energy consumption, environmental impact, the cost of moving goods, utilization of scarce areas, lead times, and customer satisfaction. The significant investment in container terminals (CTs) and their equipment necessitates a high equipment occupancy rate as well. From a sustainability perspective, port operation is not only about energy consumption but also the equipment occupancy parameter, since the amount of equipment needed is proportional to the material used to build it. Recently built or upgraded CTs are automated, advanced container terminals (ACTs). This work develops a model of a basic ACT, and expand it into a complex layout that is fast enough to compute yet realistic. The goal was to perform multi-objective optimization on mooring time, equipment occupancy, and port land utilization based on such a model. In a typical ACT process, quay cranes lift containers from ships at berth and load automated guided vehicles (AGVs), which transport the containers to next to automated rail-mounted gantry cranes (ARMGs) at the yard, where they lift the containers and stack them in areas for further distribution outside the container terminal. This research leverages discrete event simulation (DES) and multi-objective optimization using genetic algorithms (GAs). This approach enables the evaluation of various alternatives and represents the stochastic behavior at the operational level. A simulation model has been developed using the Salabim package in Python as a base to evaluate the performance and optimize the operation of the ACT from quay to container stack. The basic model can be simulated on a regular computer, including the optimization, which is based on the Distributed Evolutionary Algorithms in Python (DEAP) library and its Non-dominated Sorting Genetic Algorithm II (NSGA-II) implementation. Based on the initially basic model, the scope gradually expand to cover multiple piers and a hinterland central hub, optimizing further aspects such as addressing the scarce land challenge in the pier areas. It is recommended that container logistics stakeholders partner up and plan for optimization and enhancement of the complete container management ecosystem. This work lowers the threshold to expand the scope and guides in what direction to improve. The model is built to be scalable, and more local conditions can be added. With additions to the model, such as bi-directional container movement, it should be useful to implement for real cases as a base for decision-making at new development or enhancements of container ports.
dc.identifier.coursecodeMMSX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308008
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectMulti-objective Container Terminal Optimization
dc.subjectPort Discrete Simulation
dc.subjectGenetic Algorithm Utilization
dc.titleContainer terminal optimization: Pareto front optimization for maximum port area utilization
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeÖvrigt, MSc
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