A model for evaluating performance and reliability of the voluntary ...

A model for evaluating performance and reliability of the voluntary maritime rescue system in the Gulf of Finland F. Goerlandt, F. Torabihaghighi, P. Kujala Aalto University, Department of Applied Mechanics, Marine Technology, Research Group on Maritime Risk and Safety, Espoo, Finland

ABSTRACT: The Finnish Lifeboat Institution operates an extensive network of search and rescue units (SRUs) across the Finnish maritime and lacustrine areas, operated by volunteers. Recent interest in fleet renewal sparked the question which SRU types would be most suitable for the operations in the Gulf of Finland. There is a desire to operate a cost-effective, reliable and highly performant fleet. This study gives an account of advances in the construction of a simulation model aimed at evaluating the response characteristics of the maritime voluntary sea rescue system. The simulation model is driven by a historical incident data, augmented with wave data and expert-elicited SRU characteristics. The model aims at providing some elements of decision support with regard to the required number of vessels in each station, given certain performance requirements.

1 INTRODUCTION In areas with high activity of recreational boating such as the Gulf of Finland, an important and societally visible element of safety of life at sea is the operation of a system of maritime search and rescue units (SRUs). These SRUs perform search and rescue (SAR) missions and respond to needs for technical assistance. In Finland, the final responsibility for the sea rescue lays with the Finnish Border Guard (Rajavartiolaitos, RAJA). As the border guard has several other duties, including customs, policing and pollution response, a voluntary system of sea rescue, represented by the Finnish Lifeboat Institution (FLI) in practice performs a large number of the SAR activities, especially during ice-free conditions. The current fleet, covering both the Finnish maritime and lacustrine areas, consists both of older vessels and newly commissioned units. Moreover, the rationale of vessel allocation has in the past been made in an ad-hoc fashion. As new vessel orders are planned in the future, the organization has adopted a capability building program aimed at an evidence-based resource allocation (Nordström 2012). One effort in this process is a formalized risk management strategy, both in the planning, training and operation phases. As the organization is supported by public finances and operated by volunteers, it is desired to rationalize re-

sources and allocate SRUs to the stations based on actual needs. Apart from other concerns related to local preferences, volunteering activity and customs, this involves selecting SRUs suitable for the local conditions and for the desired operational performance. This performance is mainly driven by response time, but fleet reliability is an important concern as well. Presently, waterborne incidents are usually handled by a single unit. The organization is planning to change the incident response procedure to two SRUs, as is customary in e.g. Sweden. In a desire to support the different scenarios and vessel newbuilding options with evidence of actual needs and corresponding fleet performance, the FLI has supported the development of a simulation model aimed at giving elements of decision support. This paper describes advances in the development of this model. 2 PREVIOUS WORK ON MARITIME SAR PLANNING Azofra et al. (2007) propose a gravitational model to evaluate the suitability of the location of a rescue vessel in a given location. Several factors such as type of incidents and the severity, type of vessel involved in the incident and amount of damage, distribution of the rescue resources such as helicopters, tug-boat and rescue boat, placement of the resource and its suitability and cost are taken in to account. Li

(2006) applied optimization models in search and rescue for the location of rescue vessels on the Canadian east coast and a simple simulation model to validate, compare and improve the statistics such as response time and workload balance. The SARPlan method proposed by Abi-Zeid and Frost (2005) is a geographic Decision Support System (DSS) and applied in the optimal planning of search missions in Canadian Forces. The methodology is based on the search theory which identifies where to put the available resources in order to minimize the time of finding search object. Norrington et al. (2008) applied Bayesian Belief Networks (BBN) to model the reliability of search and rescue operations in the UK coastguard. The rescue service system is in essence a typical resource-allocation problem. Similar systems are the operations of fire departments (Badri et al. 1998), police departments (McEwen et al. 1974) and medical emergency systems (Hill et al. 1984). There exists an extensive literature to deal with modeling and analyzing these systems. An extensive review is given by e.g. Goldberg (2004). Nonetheless, sea rescue has certain typical features warranting the need for a problem-specific model. Special features are the need for a land-avoidance routing routine and the influence of environmental conditions, in particular wave conditions. The capabilities of various available SRU classes depends strongly on the wave conditions, which should be accounted for. 3 MODEL DESCRIPTION A popular method to study a complex system and its functioning is simulation. Certain variables are introduced to mimic the real system as closely as needed to obtain realistic results. The dynamic change of these variables represents the system state, which is of interest. This study is based on the discrete event simulation, meaning that the system evolves over time by changing the state of the variables at separate points in time, i.e. at so-called events (Law and Kelton 2000). The sea rescue response model is in essence a multi-server, multi-customer queuing system. A queuing system consists of one or several servers and customers. In this problem, the SRU is the server and the incident is the customer. A queuing system consists of the arrival rate, the service mechanism and the queuing discipline (Law and Kelton 2000). The simulation is done in three stages, which are discussed in more detail below: • Input modeling: generate incident occurrence with relevant features • SAR simulation: model the assignment of a rescue vessel to a given incident

• Output analysis: measure the effectiveness of the overall system 3.1 Input modeling The model input is taken directly from available historic incident records from the year 2007 to 2010. Even though the yearly number of incidents is rising and historic data cannot account for this, the current lack of knowledge regarding the interdependence between weather conditions, vessel intensity and traffic patterns and incident rate makes it too uncertain to model this based on generalized assumptions. Mindful of the oft-quoted ‘Garbage In, Garbage Out’ idiom in simulation modeling, a trace-driven simulation approach with respect to the incident occurrence is adopted, based on available incident data. As the model aim is to study the performance of various SRU allocation options, this is considered meaningful. The relevant fields in the available incident data are: time, location and type of incident. The incident type is related to a certain urgency factor, which is also based on the available incident data. This urgency factor is used for prioritizing response to incidents in case multiple incidents occur simultaneously. This occurs mainly during special holidays, such as the Midsummer weekend. The incident data is furthermore augmented with an estimate of the prevailing wave conditions at the time and location of incident, which are an important factor in the response capability of the various SRU types. This wave condition estimate is based on interpolations between wave measurements at a measurement buoy off Helsinki and seasonal wave hindcast statistical maps obtained from Tuomi et al. (2011). Figure 1 shows the overall incident density map over the four years of data. Figure 2 illustrates the seasonal intensity of incidents over the four years. From this figure, it is seen that mainly during the summer boating activity is intense. Mostly, incidents occur at a rate of 10 per day, but peaks up to just under 25 per day occur. Figure 3 shows the 50% exceedance percentile of wave conditions in the study area for spring, summer and autumn. The criticality of the incidents (CI), defined as the urgency by which response was performed and shown in Table 1, is dependent on the incident type as obtained from the incident data. It is seen that most incidents (around 80%) are critical, but some differences between incident types exist: e.g. man over board is more urgent than technical assistance missions (fuel depletion or engine failure). The 29 search and rescue units (SRUs) are assigned a base station and wave-dependent speed curve. Thus, the operability of the various SRUs can account for the prevalent wave conditions at the incident time. This is necessary as not all SRU types perform well in all weather conditions, which may influence the SAR system performance.

There are 11 rescue stations in the Gulf of Finland: Hanko, Tammisaari, Inkoo, Bågaskär, Inkoo, Kirkkonummi, Espoo, Helsinki, Porvoo, Loviisa, Kotka and Hamina. A detailed description of the current allocation of SRUs to the various stations is given in Table 2.

Figure 1. Incident density map, data from 2007 to 2010, gridsize 1x1 nm

Table 1. Probability of incident criticality by incident type Incident type Grounding or bottom contact Collision Collision with other object Man over board Fire or explosion onboard Sinking or leakage Listing or capsizing Drifting vessel or object Getting lost or uncertain location Suspected distress signal Engine failure Propeller or power failure Sail- or rigging damage Rudder damage Rope in propeller or rudder Fuel depletion or disruption Other technical error Archipelagic incident Medical transport | first aid Environmental incident Unknown

P[CI] 0.79 1 0.80 0.80 0.83 0.93 0.81 0.86 0.67 0.67 0.87 0.83 0.60 0.84 0.78 0.88 0.85 0.75 0.91 0.80 0.81

Table 2. SRU characteristics in studied area

Figure 2. Seasonal incident intensity histogram, data from 2007 to 2010

Figure 3. Wave height in Gulf of Finland, 50% exceedance probability, based on Tuomi et al. (2011)

ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Base station Hanko Hanko Tammisaari Tammisaari Bågaskär Bågaskär Bågaskär Bågaskär Inkoo Porkkala Porkkala Espoo Espoo Espoo Helsinki Helsinki Helsinki Helsinki Helsinki Porvoo Porvoo Porvoo Loviisa Loviisa Kotka Kotka Kotka Hamina Hamina

Vessel name Betty Russarö Ajax III PV224 Bogge Pörsö Boistö 1 Reijo Fagerö AV17 Aktia Meriaura V Mikrolog II Westhouse Jenny Wihuri Rautaoma Nihti Antti Aava R Mac Elliot AV25 AV54 Degerby AV23 AV51 Kotka Lassi Pikkumusta Hailikari

Max speed [kn] 29 15 25 29 30 9 30 26 30 25 27 29 28 29 18 28 25 40 20 18 25 20 28 25 20 28 29 32 30

3.2 Simulation of the Search and Rescue The rescue service simulation, the overall logic of which is shown in Figure 4, is the core of the model. It is a significantly simplified representation of the real system, but the main features needed to evaluate overall performance, in particular the operability of various SRUs under different wave conditions, are accounted for. The BBN model by Norrington et al. (2008) clearly shows that a reliable SAR service involves much more than just getting proper material, which is also clear from the building capability and risk management plans in the Finnish Lifeboat Institution. However, it was agreed with field experts that the wave conditions were the single most determining factor governing the SRU performance in the Gulf of Finland. Other factors such as competence, organizational response and communication are not modeled. The purpose of the simulation model is only to assess how the system capability changes if a different number or different SRUs are allocated to certain stations. For this specific element of decision support, a simplified model is deemed sufficient.

noted that the delay in response due to rescue crew travel time to the rescue station and vessel preparation time are not taken into account. It is assumed that the response time is equal across all stations. The start time and operation time are two constant terms for each incident whereas the travel time is a function of SRU speed and the distance between the SRU and the incident. This travel time is dependent on the attainable vessel speed in the prevailing wave conditions, which are location- and time dependent as shown in Fig. 3. Moreover, a land-avoidance algorithm based on the A* star algorithm (Hart et al. 1968) is implemented to generate realistic travel paths. The travel time is determined as: N Ttravel

x i

1

i

v i

(2)

where vi is the speed of the SRU in cell i of the shortest path between the initial and target location of the SRU and xi the distance between consecutive cells. The speed vi is conditional to the prevalent wave conditions in the specific cell and the attainable speed under those conditions for the selected SRU, see Fig. 5.

Figure 5. Wave-speed relation of selected SRUs, IDs according to Table 2

Figure 4. Rescue service simulation model: general flowchart

The main issue is the selection of the rescue vessel (RV). The criterion for this selection is minimizing the End Time (ET) of the rescue operation, which is a function of start time of incident, travel time and operation time: (1) The minimum end time is taken as decision criterion as this way, the SRU which is first available to perform a new operation, is selected. It should be

Figure 6. Probability density of operation duration

The probability density of the operation time, based on the available incident data, is shown in Figure 6. The operation for a given incident is sampled from this histogram. In the simulation program, the location of a SRU is determined based on its operational status. If the SRU is idle, it is always taken to be in its base station. If it is busy with an operation, it is either at the location of the incident it is assigned to or it is heading there. In the selection of the appropriate SRU to perform a mission, based on the minimum operation end time as shown in Eq. 1, the location of the SRU is determined at the time the new incident occurs. Based on the criticality of the new incident, a number of cases are possible in case the start time of the current incident in the event list is before the end time of the previous incident. The possibilities are shown in Table 3.

These performance indicators provide a measure of how well the system performs. The number of incidents performed by each SRU is interesting as it shows the relative importance of the various SRUs. This can also be related to the system reliability: if highly utilized material becomes unavailable, the system performance may change significantly. The travel time is a direct indicator of the adequacy of the rescue services. Even though it does not the full time between receiving the emergency call and the time of assistance, the lower the response time, the better. The queue time is a secondary indicator of response adequacy: incidents placed in queue due to other incidents needing prioritized attention are generally speaking undesired – always if these incidents are critical.

Table 3. Cases for incident scheduling logic

Figure 7 shows the number of operations performed by each SRU in the Gulf of Finland. It is seen that while some vessels perform no single operation, most vessels perform some operations over the course of a year. The faster SRUs are more often selected to perform operations as the wave conditions in the Gulf of Finland are often low enough for even smaller SRU classes to perform the operations. However, in contract to the results of a more simplified model presented in Goerlandt et al. (2012), other SRUs are seen to perform missions as well. This may be due to the operability of the SRU in more adverse weather conditions and due to the application of the A* algorithm. Figure 8 shows the travel time distribution, from which it is seen that most incidents are responded to in less than 15 minutes after departure from the SAR station. However, longer travel times up to 50 minutes also occur. The travel time distribution per SAR station can provide deeper insight in the performance of the SAR response in various areas. It should be noted that the presented time is not the total response time. The queue time distribution is shown in Figure 9. It is interesting to note that most queue times are relatively short, in line with the mission abortion to attend another critical mission. However, some queue instances, up to 90 minutes, are surprisingly large. Despite the rather stringent model simplifications, it is taken that these performance indicators provide a reasonable approximation of the real system’s performance. In particular, the model is expected to be useful to study changes in the system. The absolute values are in this sense less important than changes in values. The study of changes in the system are however left for further work and analysis.

Case 1 2 3 4

First incident Critical Critical Non-critical Non-critical

Latter incident Critical Non-critical Non-critical Critical

Critical incidents (CIs) have priority over noncritical incidents (NCIs). Thus if the SRU is busy with a CI, and an incident occurs concurrently, regardless of the severity of this incident, the SRU must first complete the rescue operation for the CI and then travel to the other incident. However, for the fourth case, due to the priority of the CI to the NCI, the SRU must immediately travel to the location of CI. These cases are accounted for in the simulation logic, see Figure 4. If there is more than one incident in the operation list of a SRU, it implies that there is one or more incident in the queue and waiting to get the rescue operation. This time is defined as the queueing time, and is calculated from the start time of the incident to the time and the end time of the rescue operation for the previous incident in the operation list. 3.3 Output – performance criteria The output of the search and rescue simulation consists of the selected SRU, the start time of incident, the travel time, the operation time, the end time of the rescue operation, the prevailing wave conditions, the severity of the incident, the location of incident, the location of rescue vessel and the queuing time. As the incident severity and the queuing time are sampled stochastically, a Monte Carlo loop of 10 runs is performed to evaluate the output. The main system performance indicators are the number of incidents performed by a given SRU, the distribution of travel time and the queueing time.

4 RESULTS AND DISCUSSION

SRU resources and system reliability studies are possible applications of the presented model. ACKNOWLEDGEMENTS

Figure 7. Expected number of operations per SRU, ID numbers as in Table 2

The work in this paper is performed as part of the RescOp project in association with the Kotka Maritime Research Centre. This project is co-funded by the European Union, the Russian Federation and the Republic of Finland. The authors wish to thank Jori Nordström from the Finnish Lifeboat Institution and Petteri Leppänen from the Finnish Border Guard for their valuable support and guidance in understanding the Finnish sea rescue system, and for valuable modeling comments. REFERENCES

Figure 8. Travel time distribution over entire Gulf of Finland

Figure 9. Queue time distribution over entire Gulf of Finland

5 CONCLUSION A discrete-event simulation model for the maritime search and rescue operations in the Gulf of Finland is outlined. The inclusion of wave conditions and SRU type specific attainable speed distributions, alongside a realistic land avoidance path planning algorithm, lead to a simplified but adequate representation of the system’s performance. This model is expected to provide elements of decision support to the Finnish Lifeboat Institution, e.g. in the required number of SRUs in each station, and which SRU types to invest in. The effect of reallocation or modernization of

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