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http://hdl.handle.net/10553/70823
Title: | Establishment of MoS in Chile: Pertinence Assessment Through an Analysis of Previous Scenarios | Authors: | Martínez López, Alba Trujillo Castellano, Lourdes Chica González, Manuel |
UNESCO Clasification: | 531212 Transportes y comunicaciones | Keywords: | Short sea shipping Motorways of the sea Intermodal chains Multi-objective optimization Evolutionary algorithms |
Issue Date: | 2020 | Publisher: | Springer | Conference: | Computation and Big Data in Transport (CM3 2017) | Abstract: | With the opening of the Panama Canal, Chile is adapting its transport logistics to the expected arrival of larger container vessels by assuming the establishment of a hub port in its central region. This paper tackles the feasibility of the intermodal chains through MoS to feed the North and the South regions from this central hub port. Due to the features of Chile, the intermodal distances are similar to the unimodal distances. This fact along with the remarkable imbalance of the cargo flows between the North and the South are an additional challenge for the success of the intermodality. In order to support the opportunities of success of the intermodality this study defines, through the optimization of a mathematical model, the most adequate fleets for MoS in the North and South of Chile. Likewise, assuming identical conditions for all Chilean ports (previous scenarios), the resolution of the model identifies the most suitable peripheral ports to articulate MoS from a large-scale hub port in the central region of Chile. The results show that, the intermodality is a competitive solution in the north, but it is not in the south when optimized fleets are used. | URI: | http://hdl.handle.net/10553/70823 | ISBN: | 978-3-030-37751-9 | ISSN: | 1871-3033 | DOI: | 10.1007/978-3-030-37752-6_11 | Source: | Computation and Big Data for Transport. Digital Innovations in Surface and Air Transport Systems/ Pedro Diez; Pekka Neittaanmäki; Jacques Periaux; Tero Tuovinen; Jordi Pons-Prats (eds), Computation and Big Data for Transport. Computational Methods in Applied Sciences, vol 54, p. 181-193 |
Appears in Collections: | Capítulo de libro |
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