Flexible design of a network of heliports for forest

firefighting helicopters applied to the case of Sardinia

Hugo Silva1, Abílio P. Pacheco

1*, João Claro

1, Michele Salis

2, Matthew P. Thompson

3,

Crystal S. Stonesifer3, Gavino Diana

4, Silvio Cocco

4

1 INESC TEC and Faculdade de Engenharia, Universidade do Porto;

2 University of Sassari – DSNER and

EMCCC – IAFES DS; 3

USDA Forest Service, Rocky Mountain Research Station; 4 Sardinia Forest Service

*corresponding author email: app@fe.up.pt

Country: Italy

Area: about 24,000 Km2

Municipalities: 8

Helicopter bases: 11

Helicopters: 11 mo

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This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project «POCI-01-0145-FEDER-006961», and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. FCT also supported the research performed by Abílio Pereira Pacheco (Grant SFRH/BD/92602/2013).

ACKNOWLEDGEMENTS

The authors are deeply grateful to Eng.º Rui Almeida (ICNF) and Professor Richard de Neufville (MIT, ESD) for their enthusiasm and advice.

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Uncertainty: variability of fires arrival; thus, in the helicopter demand.

Source of flexibility: reallocate helicopters during the operational window.

Databases (2006-10* and 2009**):

• about fires location, weather conditions, and others (later completed with the fire duration);

• helicopter flights (flight duration, fire location and water drops);

• unfortunately, the two databases, are not connected.

Approach • analyze if the demand is synchronous for each base; • If not, combine bases’ demand and helicopters in “zones”; • explore which combination (territory partition) provides the best coverage.

Research question: can bases’ coverage reorganization improve helicopters usage efficiency?

______ * the longest and latest period of time with all data available. ** an average year, in fires and flights.

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# of non-dominated solutions

Results (partition number)

(the number of possible partitions and consequence of the connectivity constrain and four distance thresholds)

CONCLUSION • results seem to favor a flexible design over the current fixed design; • gains in the number of attended fires and gini index; • however, more coverage of fires, decreases HIP value, thus, with higher operating costs; • the design that can be periodically changed, on an annual or weekly basis; • “no improvement” on HIP… an opportunity for (optimize) a trade-off between HIP and other criteria.

LIMITATIONS • lack of recent data; • distance thresholds were defined by us (SFS chose 80 and 100 Km); • fires and flights are not related.

Statistical analysis: • more accurate probability values when using 2006-2010 (always under 50%); • different bases, different levels of demand; • low correlation values for fires and flights between bases: • demand not synchronous; • supply is not being delivered synchronously as well. • unbalanced system: gains in a design where helicopters can cover areas they

are not allocated to; • by aggregating bases in a way that approximates the level of service and the

level of demand.

Partitions*: • with a simple criteria set: significant limitation in the # of admissible

partitions; • 40 Km reveals to be uninteresting, with a very small diversity of results; • threshold higher than 80 Km would produce zones with more bases.

Multi-criteria analysis: • improvements in all criteria except in HIP (Helicopter Idleness Percentage); • Helicopter demand probability seems to turn the system more stable, with

lower gini index; • no predominance of any of the partitions.

Non-dominated solutions • all solutions are non-dominated for 40 Km; • optimal values mostly located in similar partitions; • smaller zones provide better values for HIP. ______

* SFS required modifications: (1) any base can receive both models, thus, eliminate constraint (iii); (2) at least 2, and 3 bases in zones located on south/north of the island (additional constraint).

References: Pacheco, A.P., R. de Neufville, J. Claro, and H. Fornés. 2014. Flexible design of a cost-effective network of fire stations, considering uncertainty in the geographic distribution and intensity of escaped fires (http://dx.doi.org/10.14195/978-989-26-0884-6_203):

• presented at 7th ICFFR (VII International Conference on Forest Fire Research); • flexible design of a network of regional depots for vehicles mostly used in the extended attack; • simulation model developed at MIT and applied to Porto district in Portugal.

Stonesifer, C., M. Thompson, M. Salis, A.P. Pacheco, and J. Claro. 2015. Flexible design of Helicopter basing strategies: a case study of Sardinia : • presented at ICFBR 2015 (II International Conference on Fire Behaviour and Risk); • explore the possibility of applying the same model to Sardinia; a simple first optimization model (with observed improvements).

Available data about forest fire Helicopters:

• mostly used on initial attack;

• two models of helicopters;

• different helicopters require different staff and maintenance (tools and parts);

• same bases are prepared for the two models, other for only one.

Restrictions:

• helicopters can only land and stay in bases prepared for its model;

• helicopters can only fly during the operational window (from sunrise until sunset);

• eleven helicopter, one must be assigned to each of the eleven bases…

Source of flexibility?! Where?!

Portugal

Sardinia

Methods (in R)

Statistical analysis: time series, fires and flights per hour; bases' demand correlation; simultaneous fires analysis; helicopter probability demand (as proxy of the difficulty of the fires around each base).

Partition selection (constraints): (i) in the same zone, every base must be adjacent to at least another base; (ii) the maximum distance between bases in the same zone must be under a specified threshold; by using an “adjacency matrix” and a “distance matrix”; and (iii) helicopter model.

Multi-criteria analysis (selected criteria): • unattended fires per day, mean (UFM) – minimize • unattended fires per day, value at risk (95%) (UFVaR) – minimize • helicopter idleness percentage (HIP) – maximize • gini index for each of the above criterion– minimize

AHP analysis: • test phase – 6 volunteers from GIPS, CB, ICNF, ANPC (Portugal); • work in progress – 38 experts of Sardinia Forest Service (SFS).

An example with a territory with 3 bases

(A, B, and C);

- 5 possible partitions;

- 4 partitions are viable (#1-4).

- 1 partition is not viable (#5);

obtained from expert elicitation

Final (visual) results – after AHP analysis:

best! according SFS