Container ports serve as crucial logistics hubs in global supply chains, but navigating ships within such ports is complex due to restricted waterways. Tugboats play a critical role in ensuring safety and efficiency by escorting and towing ships under these conditions. However, the tugboat deployment and scheduling problem has received little attention. To fill the research gap, we propose a new research problem - , in which not all requests are confirmed initially but dynamically confirmed over time and future tugging demands need to be anticipated when managing the utilization of tugboats. To formulate the problem, we propose an extended Markov decision process (MDP) that incorporates both reactive task assignment decisions and proactive tugboat waiting decisions, creating a reactive and proactive MDP. To solve the advanced MDP model efficiently for real-time decisions, we develop an anticipatory approximate dynamic programming method that incorporates appropriate task assignment and waiting strategies for deploying and scheduling a heterogeneous tugboat fleet and embed the method into an improved rollout algorithm to anticipate future scenarios. The effectiveness, efficiency, and performance sensitivity of the developed modeling and solution methods are demonstrated via extensive numerical experiments for the Singapore container port.

中文翻译：

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具有随机和时变服务需求的动态拖船部署和调度


集装箱港口是全球供应链中重要的物流枢纽，但由于水道有限，在这些港口内航行的船舶非常复杂。拖船在这些条件下护航和拖曳船舶，在确保安全和效率方面发挥着至关重要的作用。然而，拖船部署和调度问题却很少受到关注。为了填补研究空白​​，我们提出了一个新的研究问题 - ，其中并非所有请求都在最初得到确认，而是随着时间的推移动态确认，并且在管理拖船的使用时需要预测未来的拖船需求。为了解决这个问题，我们提出了一种扩展的马尔可夫决策过程（MDP），它结合了反应性任务分配决策和主动性拖船等待决策，创建了反应性和主动性 MDP。为了有效地解决先进的 MDP 模型以进行实时决策，我们开发了一种预期近似动态规划方法，该方法结合了适当的任务分配和等待策略来部署和调度异构拖船队，并将该方法嵌入到改进​​的推出算法中以预测未来场景。通过新加坡集装箱港口的大量数值实验证明了所开发的建模和解决方法的有效性、效率和性能敏感性。