Models and Algorithms for Container Vessel Stowage Optimization

Abstract

Containerized seaborne trade has played a key role in the transformation of the global economy in the last 50 years. In liner shipping companies, at the heart of this operation, several planning decisions are made based on the stowage capabilities of container vessels, from strategic decisions (e.g., selection of vessels to buy that satisfy specific demands), through to operational decisions (e.g., selection of containers that optimize revenue, and stowing those containers into a vessel). This thesis addresses the question of whether it is possible to formulate stowage optimization models, with acceptable trade-offs between accuracy and scalability, that can be used to assist solving planning problems in liner shipping companies. We consider two planning contexts to answer this question: Stowage Planning (SP) and the Cargo Composition Problem (CCP). SP is the process of deciding where each container of those to be loaded in a port should be placed in a vessel, i.e., to generate stowage plans. This thesis explores two different approaches to solve this problem, both follow a 2-phase decomposition that assigns containers to vessel sections in the first phase, i.e., master planning, and that stows the containers assigned by the master planning to each vessel section and into vessel slots in the second phase, i.e., slot planning. The first SP approach automatically generates representative stowage plans. This dissertation presents an accurate mathematical programming model for master planning that includes features of stowage planning that have not been considered in previous work. For slot planning, a fast and accurate representative model to optimally stow vessel sections is introduced. The second SP approach serves as the optimization component of a commercial decision support tool used for interactive planning of container vessels. Expert's know-how formulated as user preferences is integrated into the heuristic optimization component and used to tackle complex constraints and optimize combinatorial objectives. According to our experimental evaluation, stowage plans computed by our heuristic are competitive enough with respect to those made by experts under the same conditions.The CCP evaluates how the stowage characteristics of containers with different features affect important performance measures used in liner shipping companies, e.g., vessel intake and cargo revenue. This dissertation provides the first problem description and introduces the first mathematical model with variable displacement. Specifically, a model that assigns containers to vessel slots, and which is actually solvable on real instances. To increase scalability, a master planning model with variable displacement to solve the CCP is also presented. Analysis on vessel intake and revenue optimization are performed on single and multi-port scenarios for a benchmark suite of ten vessels facilitated by our industrial collaborators