With the growing emphasis on green shipping to reduce the environmental impact of maritime transportation, optimizing fuel consumption with maintaining high service quality has become critical in port operations. Ports are essential nodes in global supply chains, where tugboats play a pivotal role in the safe and efficient maneuvering of ships within constrained environments. However, existing literature lacks approaches that address tugboat scheduling under realistic operational conditions. To fill the research gap, this is the first work to propose the bi-objective dynamic tugboat scheduling problem that optimizes speed under stochastic and time-varying demands, aiming to minimize fuel consumption and manage service punctuality across a heterogeneous fleet. For the first time, we develop an extended Markov decision process framework that integrates both reactive task assignments and proactive waiting decisions, considering the dual objectives. Subsequently, an initial schedule for known requests is established using a mixed-integer linear programming model, and an anticipatory approximate dynamic programming method dynamically incorporates emerging demands through task assignments and waiting plans. This approach is further enhanced by an improved rollout algorithm to anticipate future scenarios and make decisions efficiently. Applied to the Singapore port, our methodology achieves a 12.8% reduction in the total sail cost compared to the tugboat company’s scheduling practices, resulting in significant daily savings. The results with benchmarking against three methods demonstrate improvements in cost efficiency and service punctuality, meanwhile, extensive sensitivity analysis provides managerial insights for operational practice.

中文翻译：

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在随机和时变服务需求下进行速度优化的双目标动态拖船调度


随着人们越来越重视绿色航运以减少海上运输对环境的影响，优化燃料消耗并保持高服务质量已成为港口运营的关键。港口是全球供应链中的重要节点，拖船在受限环境中安全高效地操纵船舶方面发挥着关键作用。然而，现有文献缺乏在实际操作条件下解决拖船调度的方法。为了填补研究空白，这是第一个提出双目标动态拖船调度问题的工作，该问题在随机和时变需求下优化速度，旨在最大限度地减少燃料消耗并管理异构船队的服务准点率。考虑到双重目标，我们首次开发了一个扩展的马尔可夫决策过程框架，该框架集成了反应式任务分配和主动等待决策。随后，使用混合整数线性规划模型建立已知请求的初始计划，并且预期近似动态规划方法通过任务分配和等待计划动态地合并新出现的需求。改进的推出算法进一步增强了这种方法，以预测未来场景并有效地做出决策。应用于新加坡港口，与拖船公司的调度做法相比，我们的方法使总航行成本降低了 12.8%，从而节省了大量日常成本。针对三种方法进行基准测试的结果表明，成本效率和服务准时性有所提高，同时，广泛的敏感性分析为运营实践提供了管理见解。