Hybridizing Basic Variable Neighborhood Search With Particle Swarm Optimization for Solving Sustainable Ship Routing and Bunker Management Problem

This paper studies a novel sustainable ship routing problem considering a time window concept and bunker fuel management. Ship routing involves the decisions corresponding to the deployment of vessels to multiple ports and time window concept helps to maintain the service level of the port. Reducing...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 21; no. 3; pp. 986 - 997
Main Authors: De, Arijit, Wang, Junwei, Tiwari, Manoj Kumar
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
bunker fuel management
Bunkering
Bunkers (fuel)
Carbon dioxide
Exact solutions
Fuels
Management
Marine transportation
Marine vehicles
Mathematical model
Mathematical models
Microsoft Windows
mixed integer linear programming model
Particle swarm optimization
Ports
Routing
Search algorithms
Ship routing
Sustainability
Transportation
variable neighborhood search algorithm
Windows (intervals)
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:This paper studies a novel sustainable ship routing problem considering a time window concept and bunker fuel management. Ship routing involves the decisions corresponding to the deployment of vessels to multiple ports and time window concept helps to maintain the service level of the port. Reducing carbon emissions within the maritime transportation domain remains one of the most significant challenges as it addresses the sustainability aspect. Bunker fuel management deals with the fuel bunkering issues faced by different ships, such as selection of bunkering ports and total bunkered amount at a port. A novel mathematical model is developed capturing the intricacies of the problem. A hybrid particle swarm optimization with a basic variable neighborhood search algorithm is proposed to solve the model and compared with the exact solutions obtained using Cplex and other popular algorithms for several problem instances. The proposed algorithm outperforms other popular algorithms in all the instances in terms of the solution quality and provides good quality solutions with an average cost deviation of 5.99% from the optimal solution.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2019.2900490