# Fuzzy Logic in Selection of Maritime Search and Rescue Units
## 5 Discussion and Conclusions  
The results obtained clearly indicate that there is a possibility to support the vessel selection process using fuzzy techniques. Fuzzy logic allows solving problems of missing or uncertain data, which are common in maritime rescue. In the case studied, the experts defined the criteria (ship parameters) necessary to assess the ships. They had full knowledge of some parameters and partial knowledge of other parameters. The known data included ship dimensions (length, beam draught), speed, navigational status and ship type. This information was provided and decoded from the AIS. However, information on medical equipment and observation altitude was missing. The principles of multi-criteria analysis (MCDA) were used to conduct the experiments. In the first experiment, the potential capabilities of five vessels were investigated based on known parameters. In the second experiment, in addition to the known values, the results obtained during the fuzzy test for the last two criteria were used. Significant differences in the outcomes were shown. In the first experiment, potentially weaker ships obtained better results because their assessment was based on a limited number of criteria. In the second case, more information was taken into account and therefore the results better represent the preferences of the coordinator. The development of fuzzy rules requires expert knowledge. The role of experts can be played by action coordinators and researchers. In the case studied, a fuzzy rule base was created for two linguistic variables. The altitude of the observations was defined in sets of low, medium and high. The degree of medical equipment was defined in the basic and advanced sets. An output value in the form of quality was developed. The quality value represents missing data. The popular centre of gravity method was used to sharpen the final value. The obtained result became a new additional criterion for testing with the MCDA method. The experiment shows that the method is uncomplicated, requires little computational effort and gives good results. Knowledge of the precise parameters of vessels in the vicinity of the incident may be limited. Data may be unknown or incomplete. Making an analysis based on a small number of criteria does not give a complete picture. The selection of vessels using MCDA is very effective. However, it requires specific data. Combining this method with the advantages of fuzzy logic can improve the effectiveness of SAR planning. It is recommended to develop a wide range of event scenarios and to define appropriate assessment criteria for different action profiles. Next, it is recommended to build a database of fuzzy rules. All information can be used to create a decision support system for the selection of vessels for the rescue task based on MCDA principles. To summarise the study carried out, a scheme for the operation of the ship selection module in the DSS decision support system was developed (Figure 7). The system consists of the following components:  
1. Expert knowledge base. Data collection consists of modelling the preferences of the decision maker. The expert research concerns the determination of the correct set of criteria, the weighting of the criteria and the preference function (or other indicators depending on the method applied). 2. Vessel database. The vessel parameters shall be determined during the initial stages of a search and rescue operation. Data can be retrieved from various monitoring systems, e.g., AIS. 3. Parameter matrix. The matrix represents the vessel data for each evaluation criterion defined by the expert. 4. Choice of calculation method. Computational methods require specific data. MCDA methods require a full parameter matrix. Fuzzy logic copes with missing, uncertain or inexact data. 5. Carry out testing (computational experiment). If the parameter matrix is complete, it is possible to obtain results using both MCDA and fuzzy logic.