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.

中文翻译：

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将基本变量邻域搜索与粒子群优化相结合，以解决可持续的船舶航线和燃油管理问题

本文研究了一个考虑时间窗口概念和燃料管理的新型可持续船舶航线问题。船舶航线涉及与船舶部署到多个港口相对应的决策，时间窗口概念有助于维持港口的服务水平。减少海上运输领域的碳排放仍然是最重大的挑战之一，因为它解决了可持续性方面的问题。燃油管理处理不同船舶面临的燃油加注问题，例如加注港口的选择和港口的总加注量。开发了一种新颖的数学模型来捕捉问题的复杂性。提出了一种具有基本变量邻域搜索算法的混合粒子群优化算法来求解该模型，并与使用 Cplex 和其他流行算法针对多个问题实例获得的精确解进行比较。所提出的算法在解决方案质量方面在所有实例中都优于其他流行算法，并提供了与最佳解决方案的平均成本偏差为 5.99% 的优质解决方案。