Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90653
Title: Green technology adoption for fleet deployment in a shipping network
Authors: Zhen, L
Wu, Y
Wang, S 
Laporte, G
Issue Date: Sep-2020
Source: Transportation research. Part B, Methodological, Sept. 2020, v. 139, p. 388-410
Abstract: The Emission Control Areas (ECAs) established by the International Maritime Organization are beneficial to reduce the sulphur emissions in maritime transportation but bring a significant increase in operating cost for shipping liners. Low sulphur emissions are required when ships berth or sail within ECAs. It is an irreversible trend that green technologies such as scrubbers and shore power will be implemented in maritime shipping industry. However, the literature lacks a quantitative decision methodology on green technology adoption for fleet deployment in a shipping network in the context of ECAs. Given a shipping network with multiple routes connected by transshipment hubs, this study proposes a nonlinear mixed integer programming model to optimally determine fleet deployment along routes (including green technology adoption), sailing speeds on all legs, timetables, cargo allocation among routes for each origin-destination pair, and berth allocation considering the availability of shore power at different berths in order to minimize total five types of cost. A three-phase heuristic is also developed to solve this problem. Numerical experiments with real-world data are conducted to validate the effectiveness of the proposed model and the efficiency of the three-phase heuristic. Some managerial implications are also outlined on the basis of the numerical experiments.
Keywords: Emission control area
Fleet deployment
Green technology adoption
Liner shipping management
Scrubbers
Shore power
Publisher: Pergamon Press
Journal: Transportation research. Part B, Methodological 
ISSN: 0191-2615
EISSN: 1879-2367
DOI: 10.1016/j.trb.2020.06.004
Appears in Collections:Journal/Magazine Article

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Embargo End Date 2022-09-30
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