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Multiobjective Approach for Sustainable Ship Routing and Scheduling With Draft Restrictions

机译：具有吃水限制的可持续船舶调度和调度的多目标方法

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摘要

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.
机译：这项研究解决了与复杂，实用和实时海上运输问题相关的与可持续性和安全相关的挑战，并提出了一个集成了不同运输业务的多目标数学模型。混合整数非线性规划（MINLP）模型是根据不同的海上作业而制定的，例如船舶的路线和调度，考虑港口高潮情景的时窗概念，离散的计划范围，装卸作业，船舶的碳排放量以及船舶的排放量。起草限制港口维护船舶安全的限制措施。该模型中包含了油耗与船舶速度优化之间的关系，用于估算总耗油量和每个船舶的碳排放量。问题中考虑的时间窗概念旨在通过在时间窗开始之前和船舶未能在分配的时间窗内完成其操作之前征收与船舶提早到达有关的不同罚款，以提高港口的服务水平。该模型包括海上运输的另一个实际方面，例如涨潮方案，以描述船只在港口的到达和离开时间。为了解决多目标数学模型，应用了两种新颖的算法-非分类遗传算法II（NSGA-II）和多目标粒子群算法。考虑从国际运输公司的现实问题中得到启发的示例性例子。实验结果，比较和灵敏度分析证明了所提出模型的鲁棒性。

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