基于多Agnet的散杂货港口驳船作业管理系统研究的中期报告.docxVIP

基于多Agnet的散杂货港口驳船作业管理系统研究的中期报告 Introduction 散杂货港口驳船作业在港口物流中占有非常重要的地位，但由于作业过程中存在多种不确定因素，如货物到港时间不确定、驳船等待时间长等，从而增加了海运公司和港口管理者的难度。因此，本研究旨在探索一种基于多Agent的散杂货港口驳船作业管理系统。 Literature Review Current research on port operations mainly focuses on the optimization of shipping schedules, container stacking, and crane dispatching procedures. For example, Guo et al.（2018）design a genetic algorithm to optimize the ship scheduling in the container terminal, reducing the waiting time of ships and increasing the utilization of port resources. Zhang et al.（2019）propose a distributed multi-layer container stacking system with a deep reinforcement learning algorithm to achieve efficient use of pallet jack in the container yard. However, there is limited research on the management of bulk and break-bulk cargo operations in ports. Methodology This research adopts the agent-based simulation (ABS) approach to simulate the port operations process. ABS is a modeling and simulation technique in which agents with autonomous behavior, decision-making ability, and interaction capability are embedded in a virtual environment to mimic the real-world system. The ABS approach has significant advantages over traditional optimization algorithms, such as the ability to capture complex interactions between entities, the ability to model both tangible and intangible factors, and the ability to simulate dynamic events. The research involves the following steps: Step 1: Define the system boundary and components: The system boundary includes all the entities and facilities involved in the bulk and break-bulk cargo operations, such as ships, barges, cranes, trucks, and storage yards. Step 2: Develop an agent-based simulation model: The model includes agents that represent the entities in the system, such as ship agents, barge agents, crane agents, and truck agents. These agents have different objectives, behaviors, and decision-making abilities. Step 3: Validate the simulation model: The simulation model is valid