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Megaships and port operations: an optimization-based future scenarios analysis

Mario La Rocca

Megaships and port operations: an optimization-based future scenarios analysis.

Rel. Fabio Guido Mario Salassa. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale, 2020

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Abstract:

Berth Allocation Problem (BAP) is a typical optimization problem in the context of port operations. The objective is to minimize the difference between the arrival time and departure time of each ship, to make ports gain competitiveness. A variant of this problem considers constraints related to water depth and tides effects. This variant is more and more actual, because shipping companies are constantly increasing ship size and capacity in order to obtain better economies of scales, causing a lot of problems to port authorities in the management of arriving ships and in their berthing plans. The focus of this thesis is to study a BAP, but with tides constraints, to make some evaluations about future possible scenarios. In fact, megaships continue to grow in size and numbers and so, the future fleets will be different from the actual one and more and more problems could arise for port authorities, related to water depth and tides constraints. Defined this problem and solved through optimization in Python code, the thesis tries to define different configurations, created on the basis of different combinations of various factors considered. After the optimization of these different instances, an analysis on the results is provided, in order to define some possible policy advice for port authorities, taking into consideration two main performance measures: service level and service time. The thesis is organized in four chapters: in the first one an introduction of the shipping context and trends is presented, while in the second all the details related to optimization problems and Berth Allocation problem are provided. In the third chapter the model used for the analysis is described, accompanied with all the details related to data analysis and generation. Finally, in the fourth chapter a results analysis is presented, with some policy advice and conclusions.

Relators: Fabio Guido Mario Salassa
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 102
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/16423
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