# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 2 Related Work  
Previously presented computer-based models for the simulation of winter navigation operations are listed in Table 1. As per the table, the models are compared with regard to their features and capabilities, including the following: (a) whether the model can visualize the simulated operations, (b) whether the model determines the speed of ships as a function of the prevailing ice conditions, (c) whether the model provides the means to optimize the simulated system (e.g., with regard to icebreaker waiting times), (d) whether the model can consider dynamically changing ice conditions. Tarovik 17 June 2022 (2017) [13] present a multi-disciplinary approach for the modelling of winter navigation traffic, with a focus on the Arctic region. Both the authors' work and the work by [PERSON] et al. model ports that use geo-information system (GIS) environments for agents and object-oriented (OO) programming for coding independent sub-models that are executed dynamically during a simulation run. The authors of this article as well as [PERSON] et al. present a dynamic simulation model to capture vessel and icebreaker movements along with ice conditions. Their primary aim, however, is to demonstrate an integrated software platform and its potential in addressing maritime transport system problems. They describe in detail the different disciplines and software systems that are used and how they are integrated in their application in two case studies. In comparison, the authors' work is focused on modelling the winter navigation system in detail to develop a decision-support tool. To this end, we consider several additional parameters related to ice and vessel-icebreaker interactions. [PERSON] et al. presents a generalized tool that has the potential to address a wide range of maritime topics on a holistic level. The authors' work, on the other hand, is a highly specialized tool, considering a multitude of local factors, that addresses specific issues in winter navigation. Both models have optimization capabilities. In [PERSON] et al. 's model, vessel fleet optimization, ice route optimization, and ship-design optimization capabilities are present. The authors' work's main emphasis is on icebreaker behavior. The focus is on optimizing tactical, strategic, and operational decisions that icebreakers need to make throughout the winter based on changing operating and traffic conditions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Simulation Model** & **Visualization** & HV Curves & Icebreaker Optimization & Dynamic Ice & Traffic Flows \\ \hline [PERSON] (1976) [10] & ✓ & ✗ & ✗ & ✗ & ✓ & ✗ \\ \hline [PERSON] et al. (1994) [11] & ✓ & ✗ & ✗ & ✗ & ✓ & ✗ \\ \hline [PERSON] et al. (2011) [12] & ✓ & ✗ & ✓ & ✗ & ✗ & ✗ \\ \hline [PERSON] et al. (2017) [13] & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ \\ \hline [PERSON] et al. (2018) [14] & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ \\ \hline [PERSON] and [PERSON] (2020) [15] & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ \\ \hline Proposed work & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Prior work in winter navigation simulation. [PERSON] et al. (2018) presented a deterministic simulation tool for analysis of the performance of the FSWNS in terms of ships' cumulated waiting time for icebreaker assistance under specific operating conditions. A validation of the tool based on real-life data indicated that it is reasonably accurate. An apparent weakness of the tool is that it is impractical for the analysis of a wide range of different operating scenarios, both because modification of the assumed ice conditions and airway configuration appears laborious and because the run time of a typical simulation is long (e.g., a simulation of one month of operations takes around 24 h). Other limitations of the tool include limited insight into the simulation process and no optimization capabilities.