Multi-objective optimization for stowage planning of large containership

• Main Author: Liu, Fan
• Other Authors: Low Yoke Hean, Malcolm
• Format: Theses and Dissertations
• Language: English
• Published: 2012
• Subjects: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
• Online Access: https://hdl.handle.net/10356/50631
• Institution: Nanyang Technological University

The Master Bay Plan Problem (MBPP), which is to decide the stowage plan of large containerships, has been studied since 1990. Since the size of containerships has been growing dramatically from 1970’s, this problem becomes one of the most important issues in shipping lines as the quality of a stowage plan affects the profit of the shipping lines significantly. Many different research works have been carried out to solve this problem, including mathematical modeling, decision support systems, rule-based expert systems and heuristic driven approaches. However, none of these works provide a satisfactory solution to the stowage planning problem. The focus of this project is to develop an automated multi-objective optimization stowage planning system which is capable of generating optimized stowage plans for large containerships in a short amount of time. Optimized stowage plans here refer to: (1) the number of unnecessary shifts of containers is minimized, hence the operation cost is reduced; (2) the amount of workload (both loading and unloading of containers) across the ship is well distributed for every port, thus the efficiency of quay cranes for handling the containers is improved; (3) the number of unusable container slots is minimized, therefore there are more space to load other containers; (4) the weight distribution of containers on the containership is well arranged, so that the amount of ballast used to balance the ship is reduced and the fuel oil requirement is reduced. The optimality of these objectives is difficult to be obtained simultaneously for one single stowage plan. Therefore instead of aiming to generate an optimal stowage plan for a containership, in this project a randomized block stowage algorithm with Tabu Search is proposed to obtain a set of stowage plans that emphasize on different objectives for the shipping lines, and help them make better decisions in the real world. An automated stowage planning system is developed in this project. A randomized block stowage algorithm with Tabu Search is proposed and implemented in this system. The basic idea of the block stowage algorithm is to divide the containers in groups and partition the containership into blocks. The algorithm breaks the stowage planning process into two stages: (1) obtaining a set of stowage plans by selecting different blocks combinations in a randomized manner; (2) applying Tabu Search to each of the stowage plans to adjust every container’s location in order to achieve an optimized final stowage plan.