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Lookup NU author(s): Dr Arijit DeORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Elsevier, 2016.
For re-use rights please refer to the publisher's terms and conditions.
Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A Mixed Integer Non-Linear Programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission, etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Owing to the inherent complexity, such a problem is considered to be NP-Hard in nature and for solutions an effective meta-heuristics named Particle Swarm Optimization-Composite Particle (PSO-CP) is employed. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4–10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions.
Author(s): De A, Mamanduru VKR, Gunasekaran A, Subramanian N, Tiwari MK
Publication type: Article
Publication status: Published
Journal: Computers & Industrial Engineering
Year: 2016
Volume: 96
Pages: 201-215
Print publication date: 01/06/2018
Online publication date: 08/04/2016
Acceptance date: 01/04/2016
Date deposited: 31/10/2018
ISSN (print): 0360-8352
ISSN (electronic): 1879-0550
Publisher: Elsevier
URL: https://doi.org/10.1016/j.cie.2016.04.002
DOI: 10.1016/j.cie.2016.04.002
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