Sustainable Maritime Transportation Network considering Sulphur Fuel Regulation - Application of Advanced Machine Learning and Optimization

国际海事组织(海事组织)强调了减缓碳排放增长的立即行动，碳排放增长是气候变化的一个主要因素。联合国贸易和发展会议指出，国际航运承担着世界贸易的80%，对二氧化碳排放量的增加做出了重大贡献。Zis等人。(2019)强调指出，2015年，许多船舶使用船用燃料油，占全球硫氧化物排放量的3.5%，从而大大增加了人口稠密的沿海地区周围的环境和健康问题(心脏病和肺病)。大气中二氧化碳和硫氧化物浓度的上升正在导致海洋吸收更多的气体，并变得更加酸性，从而对沿海和海洋生态系统造成重大影响。因此，IMO 2020通过了严格的排放控制区(ECA)规定，船舶必须使用含硫量不超过0.1%的燃料油。此外，为了减少海运物流的排放，现有文献强调了采取各种措施，如碳税、慢船政策和加油战略。这些措施的采用取决于燃料价格，而航运公司面临的一个主要问题是港口(和港口之间)燃料价格的波动。现有的专注于预测燃料价格的海运物流文献仍处于起步阶段，在开发预测船用燃料价格的先进机器学习算法方面存在空白。该项目将涉及与合作伙伴公司合作，使用包含二氧化碳排放和船用燃料价格数据的数据集，开发用于港口燃料价格预测的机器学习模型。过去的文献忽略了国际海事组织2020年关于使用低硫燃料油加油的规定。因此，目前的项目将通过开发一个多目标优化模型(混合整数线性规划模型)来最大限度地减少加油成本和排放量，从而促进考虑海事组织规则的航运公司的加油决策(即选择加油港口和确定加油数量)。几位作者强调了考虑慢速航行政策(或速度优化)和港口准确的燃油价格信息的必要性，以充分执行船用燃料管理。因此，当前的研究项目旨在考虑从机器学习模型获得的燃油价格信息，并将其整合到多目标优化模型中，以确定加油策略，同时最小化碳和硫的排放。以下是指导性的研究问题：1.我们能否建立一个可靠的预测模型来估计船用燃料价格和港口的二氧化碳排放量？2.我们能否提出一个整体的形式多目标优化模型(混合整数线性规划模型)来调整海上运输网络？这样的模型需要包括几个目标，包括与可持续性、成本和排放相关的目标，同时捕捉需要调整的合理约束和决策变量。3.我们如何将从预测模型获得的有关燃油价格信息的见解与优化模型结合起来，以确定加油战略，同时促进海上运输网络中的硫和碳排放的减少？4.什么是稳健的预测模型和多目标模型来优化问题？

The International Maritime Organization (IMO) highlighted immediate actions to mitigate carbon emission growth, a major factor in climate change. The United Nations Conference on Trade and Development has stated that international shipping carries 80% of world trade making a significant contribution to the rise in carbon dioxide emissions. Zis et al. (2019) highlighted that in 2015, many vessels used bunker fuel oil, which contributes 3.5% to global Sulphur oxides emissions, thereby significantly increasing the environmental and health problems (heart and lung diseases) around populated coastal areas. Rising atmospheric concentrations of carbon dioxide and sulphur oxides are causing oceans to absorb more of the gases and become more acidic leading to a significant impact on coastal and marine ecosystems. Consequently, IMO 2020 adopted strict regulations for emission control areas (ECAs), where ships must use fuel oil with a Sulphur content of no more than 0.1%. Furthermore, for mitigating the emission from maritime logistics, existing literature has highlighted the adoption of various measures such as carbon taxes, slow steaming policy and bunkering strategies. Adoption of such measures depends on fuel prices and a major issue for shipping companies is the fluctuation in fuel prices at (and between) ports.Existing literature on maritime logistics that focuses on predicting fuel prices is still at nascent stage, and there is a gap in the area of developing advanced machine learning algorithms that predict bunker fuel prices. This project will involve working with the partner company, to develop a machine learning model for fuel prices prediction at port using a dataset containing data on CO2 emissions and bunker fuel prices. Past literature has overlooked the IMO 2020 regulations related to the use of low-sulphur fuel oil for bunkering purpose. Therefore, the current project would facilitate bunkering decisions (i.e. choosing the refuelling port and determining the refuelling amount) of the shipping companies considering the IMO regulations by developing a multi-objective optimization model (mixed integer linear programming model) for minimizing the bunkering cost and emissions. Several authors have highlighted the need for considering slow steaming policy (or, speed optimization) and accurate fuel price information at the ports for adequately perform the bunker fuel management. Therefore, the current research project aims to consider bunker price information obtained from the machine learning model and integrate it within the multi-objective optimization model for determining the bunkering strategies while minimizing the carbon and sulphur emissions. The following are indicative research questions:1. Can we develop a reliable predictive model for estimating bunker fuel prices and CO2 emissions at ports?2. Can we propose a holistic formal multi-objective optimization model (mixed integer linear programming model) to tune maritime transportation networks? Such a model would need to comprise several objectives including ones related to sustainability, costs, and emissions, while capturing sensible constraints and decision variables to be tuned. 3. How can we integrate the insights pertaining to bunker price information obtained from the predictive model with the optimisation model for determining the bunkering strategies while facilitating sulphur and carbon emission reduction within the maritime transportation network?4. What would be robust predictive models and multi-objective models to optimize the problem?