Multiobjective Approach for Sustainable Ship Routing and Scheduling With Draft Restrictions

• Published in: IEEE transactions on engineering management 2019-02, Vol.66 (1), p.35-51
• Main Authors: De, Arijit, Choudhary, Alok, Tiwari, Manoj Kumar
• Format: Article
• Language: English
• Subjects: Carbon; Carbon dioxide; Carbon emission; Classification; Companies; Energy consumption; Fuel consumption; Fuels; Genetic algorithms; High tide; Marine transportation; Marine vehicles; maritime logistics; Mathematical analysis; Mathematical models; Mixed integer; mixed integer nonlinear programming (MINLP) model; Multiple objective analysis; nondominated sorting genetic algorithm II (NSGA-II); Nonlinear programming; Particle swarm optimization; Ports; Ports (Computers); Robustness (mathematics); Routing; Safety; Scheduling; Sensitivity analysis; ship routing and scheduling; ship's draft restriction; Shipping; Ships; Sorting algorithms; Sustainability; Transportation; Transportation problem; Windows (intervals)
• ISSN: 0018-9391; 1558-0040
• DOI: 10.1109/TEM.2017.2766443

This research addresses the sustainability and safety related challenges associated with the complex, practical, and real-time maritime transportation problem, and proposes a multiobjective mathematical model integrating different shipping operations. A mixed integer nonlinear programming (MINLP) model is formulated considering different maritime operations, such as routing and scheduling of ships, time window concept considering port's high tidal scenario, discrete planning horizon, loading/unloading operation, carbon emission from the vessel, and ship's draft restriction for maintaining the vessel's safety at the port. The relationship between fuel consumption and vessel speed optimization is included in the model for the estimation of the total fuel consumed and carbon emission from each vessel. Time window concept considered in the problem aims to improve the service level of the port by imposing different penalty charges associated with the early arrival of the vessel before the starting of the time window and vessel failing to finish its operation within the allotted time window. Another practical aspect of the maritime transportation such as high tide scenario is included in the model to depict the vessel arrival and departure time at a port. Two novel algorithms-Nondominated sorting genetic algorithm II (NSGA-II) and Multiobjective particle swarm optimization have been applied to solve the multiobjective mathematical model. The illustrative examples inspired from the real-life problems of an international shipping company are considered for application. The experimental results, comparative, and sensitivity analysis demonstrate the robustness of the proposed model.