Evaluation heuristics for tug fleet optimisation algorithms: a computational simulation study of a receding horizon genetic algorithm

Abstract

A fleet of tugs along the northern Norwegian coast must be dynamically positioned to minimise the risk of oil tanker drifting accidents. We have previously presented a receding horizon genetic algorithm (RHGA) for solving this tug fleet optimisation (TFO) problem. Here, we first present an overview of the TFO problem, the basics of the RHGA, and a set of potential cost functions with which the RHGA can be configured. The set of these RHGA configurations are effectively equivalent to a set of different TFO algorithms that each can be used for dynamic tug fleet positioning. In order to compare the merit of TFO algorithms that solve the TFO problem as defined here, we propose two evaluation heuristics and test them by means of a computational simulation study. Finally, we discuss our results and directions forward.