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Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization

Lookup NU author(s): Dr Arijit De

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by Elsevier, 2016.

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Abstract

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.


Publication metadata

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

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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