An updatable and comprehensive global cargo maritime network and strategic seaborne cargo routing model for global containerized and bulk vessel flow estimation 2021

The global maritime system provides the backbone of logistics operations for global supply chains and international trade. This paper aims to develop a unifying global network representation and strategic, system-wide decision model, the Strategic Cargo Routing Model, incorporating both liner and bulk shipping markets to estimate real-world traffic flows and study traffic patterns at the global scale. Specifically, taking a shipper's perspective, containerized and bulk movements are jointly modelled within a mixed-integer linear program that includes inbound, outbound, and transshipment cargo flows at ports. An iterative approach that combines heuristic Gradient Descent and Relax-and-Fix Decomposition methods is proposed for the calibration and solution of the Strategic Cargo Routing Model over a proposed joint liner and bulk services Global Cargo Shipping Network representation. The Global Cargo Shipping Network contains 161 seaports covering 52 countries. It is created from updatable, publicly available, data sources, and all data needed for the network representation are made available. Sufficient network details, as well as data sources and methods for extracting needed inputs, are given to allow others to use and update the network. Using the developed maritime network, mathematical model and calibration-solution methodology, 2018 global maritime traffic flow patterns were estimated. The estimates were found to achieve a 91% fit overall to real-world average annual port throughputs. This strategic model provides support to evaluate future, real-world, worldwide changes, such as increased seaborne trade demand, new routes, shipping infrastructure expansion, and transport policies. * In the data subnetwork X=(0, 1, 2) refers to containerized cargo, liquid bulk and dry bulk respectively. for example, flowX shows flows in subnetworks of X=(0, 1, 2) accordingly. portThrouX.csv shows port throughputs in subnetworks of X=(0, 1, 2) accordingly. **In the uploaded data, the files starting with input... means they are inputs for the model, and other files are outputs. **Note that the uploaded materials can be used based on the uploaded paper "Wenjie Li, Ralph Pundt and Elise Miller-Hooks. (2021). An updatable and comprehensive global cargo maritime network and strategic seaborne cargo routing model for global containerized and bulk vessel flow estimation 2021."

全球海运系统是全球供应链与国际贸易物流运作的核心支柱。本研究旨在构建统一的全球网络表征与全系统战略决策模型——战略货物路由模型（Strategic Cargo Routing Model），整合班轮与散货航运市场，用以估算真实世界的交通流量并在全球尺度下研究交通模式。
具体而言，本研究从托运人视角出发，将集装箱货物与散货运输整合至混合整数线性规划（mixed-integer linear program）模型中，该模型涵盖港口的进口、出口与中转货流。针对所提出的班轮与散货服务联合全球货物航运网络（Global Cargo Shipping Network）表征下的战略货物路由模型，本研究提出一种结合启发式梯度下降（Gradient Descent）与松弛固定分解（Relax-and-Fix Decomposition）方法的迭代求解与校准方案。
全球货物航运网络（Global Cargo Shipping Network）涵盖52个国家的161个海港。该网络基于可更新的公开数据源构建，且网络表征所需的全部数据均已公开。本研究提供了足够的网络细节、数据源与所需输入变量的提取方法，以供其他研究者复用与更新该网络。
依托所构建的海运网络、数学模型与校准求解方法，本研究估算了2018年全球海运交通流模式，经检验，该估算结果与真实世界的港口年均吞吐量整体拟合度达91%。该战略模型可用于评估全球真实场景下的未来变化，例如海运贸易需求增长、新航线开辟、航运基础设施扩建以及运输政策调整等。
* 在数据子网中，X=(0, 1, 2)分别指代集装箱货物、液体散货与干散货。例如，flowX依次展示X=(0, 1, 2)对应子网的货流；portThrouX.csv则依次展示X=(0, 1, 2)对应子网的港口吞吐量。
** 上传数据中，文件名以input开头的文件为模型输入文件，其余文件均为模型输出文件。
** 请注意，本上传材料可基于已上传论文"Wenjie Li, Ralph Pundt and Elise Miller-Hooks. (2021). An updatable and comprehensive global cargo maritime network and strategic seaborne cargo routing model for global containerized and bulk vessel flow estimation 2021."进行使用。