An improved receding horizon genetic algorithm for the tug fleet optimisation problem

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. In this paper, we begin by presenting an overview of the TFO problem and the details of the RHGA. Next, we identify and correct a flaw in the original cost function of the RHGA. In addition, we present several new cost functions that can be used for dynamic resource allocation by an algorithm such as the RHGA. In a preliminary simulation study, we correct and extend the simulation scenarios used in our previous work and examine the merit of each of the suggested cost functions. Finally, we discuss the potential for an objective evaluation method for comparing various TFO algorithms and briefly present our TFO simulator.