References
- Afshartous, D., Guan, Y., & Mehrotra, A. (2009). US Coast Guard air station location with respect to distress calls: A spatial statistics and optimization based methodology. European Journal of Operational Research, 196(3), 1086–1096. https://doi.org/https://doi.org/10.1016/j.ejor.2008.04.010
- Ai, Y., Lu, J., & Zhang, L. (2015). The optimization model for the location of maritime emergency supplies reserve bases and the configuration of salvage vessels. Transportation Research Part E: Logistics and Transportation Review, 83, 170–188. https://doi.org/https://doi.org/10.1016/j.tre.2015.09.006
- Akbari, A., Pelot, R., & Eiselt, H. A. (2018). A modular capacitated multi-objective model for locating maritime search and rescue vessels. Annals of Operations Research, 267(1–2), 3–26. (https://doi.org/https://doi.org/10.1007/s10479-017-2593-1
- Aringhieri, R., Bruni, M. E., Khodaparasti, S., & Van Essen, J. (2017). Emergency medical services and beyond: Addressing new challenges through a wide literature review. Computers & Operations Research, 78, 349–368. https://doi.org/https://doi.org/10.1016/j.cor.2016.09.016
- Armstrong, R. D., & Cook, W. D. (1979). Goal programming models for assigning search and rescue aircraft to bases. Journal of the Operational Research Society, 30(6), 555–561. https://doi.org/https://doi.org/10.1057/jors.1979.137
- Arthur, D., & Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 1027–1035). Society for Industrial and Applied Mathematics.
- Azofra, M., Pérez-Labajos, C., Blanco, B., & Achutegui, J. (2007). Optimum placement of sea rescue resources. Safety Science, 45(9), 941–951. https://doi.org/https://doi.org/10.1016/j.ssci.2006.09.002
- Baker, J. R., Clayton, E. R., & Taylor, B. W. (1989). A non-linear multi-criteria programming approach for determining county emergency medical service ambulance allocations. Journal of the Operational Research Society, 40(5), 423–432. https://doi.org/https://doi.org/10.1057/jors.1989.69
- Batta, R., & Mannur, N. R. (1990). Covering-location models for emergency situations that require multiple response units. Management Science, 36(1), 16–23. https://doi.org/https://doi.org/10.1287/mnsc.36.1.16
- Bélanger, V., Ruiz, A., & Soriano, P. (2019). Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles. European Journal of Operational Research, 272(1), 1–23. https://doi.org/https://doi.org/10.1016/j.ejor.2018.02.055
- Benigni, M., & Furrer, R. (2012). Spatio-temporal improvised explosive device monitoring: Improving detection to minimise attacks. Journal of Applied Statistics, 39(11), 2493–2508. https://doi.org/https://doi.org/10.1080/02664763.2012.719222
- Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming, 88(3), 411–424. https://doi.org/https://doi.org/10.1007/PL00011380
- Berlin, G. N., & Liebman, J. C. (1974). Mathematical analysis of emergency ambulance location. Socio-Economic Planning Sciences, 8(6), 323–328. https://doi.org/https://doi.org/10.1016/0038-0121(74)90036-6
- Berman, O., Huang, R., Kim, S., & Menezes, M. B. (2007). Locating capacitated facilities to maximize captured demand. IIE Transactions, 39(11), 1015–1029. https://doi.org/https://doi.org/10.1080/07408170601142650
- Bertsimas, D., & Ng, Y. (2019). Robust and stochastic formulations for ambulance deployment and dispatch. European Journal of Operational Research, 279(2), 557–571. https://doi.org/https://doi.org/10.1016/j.ejor.2019.05.011
- Church, R. L., & Murray, A. (2018). Location covering models: History, applications and advancements. Springer.
- Church, R., & ReVelle, C. S. (1974). The maximal covering location problem. Papers of the Regional Science Association, 32(1), 101–118. https://doi.org/https://doi.org/10.1007/BF01942293
- Czyzyk, J., Mesnier, M. P., & Moré, J. J. (1998). The NEOS Server. IEEE Computational Science and Engineering, 5(3), 68–75. https://doi.org/https://doi.org/10.1109/99.714603
- Dark, S., & Bram, D. (2007). The modifiable areal unit problem (maup) in physical geography. Progress in Physical Geography: Earth and Environment, 31(5), 471–479. https://doi.org/https://doi.org/10.1177/0309133307083294
- Daskin, M. S. (2011). Network and discrete location: Models, algorithms, and applications. John Wiley & Sons.
- Daskin, M. S., Hogan, K., & ReVelle, C. (1988). Integration of multiple, excess, backup, and expected covering models. Environment and Planning B: Planning and Design, 15(1), 15–35. https://doi.org/https://doi.org/10.1068/b150015
- Daskin, M. S., & Stern, E. H. (1981). A hierarchical objective set covering model for emergency medical service vehicle deployment. Transportation Science, 15(2), 137–152. https://doi.org/https://doi.org/10.1287/trsc.15.2.137
- Deb, K. (2014). Multi-objective optimization. In E. Burke & G. Kendall (Eds.), Search methodologies (pp. 403–449). Boston, MA: Springer. https://doi.org/https://doi.org/10.1007/978-1-4614-6940-7_15
- Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/https://doi.org/10.1109/4235.996017
- Dolan, E. D. (2001). The NEOS Server 4.0 administrative guide. Technical Memorandum ANL/MCS-TM-250 Mathematics and Computer Science Division, Argonne National Laboratory.
- Doornbos, C. (2019). Coast Guard planning to base three fast-response cutters on Guam, commandant says. https://www.stripes.com/news/pacific/coast-guard-planning-to-base-three-fast-response-cutters-on-guam-commandant-says-1.604094.
- Drezner, Z., & Hamacher, H. W. (2001). Facility location: Applications and theory. Springer Science & Business Media.
- Ehrgott, M. (2005). Multicriteria optimization. Springer Science & Business Media.
- Farahani, R. Z., Asgari, N., Heidari, N., Hosseininia, M., & Goh, M. (2012). Covering problems in facility location: A review. Computers & Industrial Engineering, 62, 368–407. https://doi.org/https://doi.org/10.1016/j.cie.2011.08.020
- Ferrari, J. F., & Chen, M. (2020). A mathematical model for tactical aerial search and rescue fleet and operation planning. International Journal of Disaster Risk Reduction, 50, 101680. https://doi.org/https://doi.org/10.1016/j.ijdrr.2020.101680
- Frihida, A., Marceau, D. J., & Theriault, M. (2002). Spatio-temporal object-oriented data model for disaggregate travel behavior. Transactions in Gis, 6(3), 277–294. https://doi.org/https://doi.org/10.1111/1467-9671.00111
- Gehlke, C., & Biehl, K. (1934). Certain effects of grouping upon the size and correlation coefficient in census tract material. Journal of the American Statistical Association, 29(185A), 169–170. https://doi.org/https://doi.org/10.1080/01621459.1934.10506247
- Gholami, S., Ford, B., Fang, F., Plumptre, A., Tambe, M., Driciru, M., Wanyama, F., Rwetsiba, A., Nsubaga, M., & Mabonga, J. (2017). Taking it for a test drive: A hybrid spatio-temporal model for wildlife poaching prediction evaluated through a controlled field test. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 292–304). Springer.
- Gholami-Zanjani, S. M., Pishvaee, M. S., & Torabi, S. A. (2018). OR models for emergency medical service (EMS) management. In C. Kahraman & Y. Topcu (Eds.),Operations research applications in health care management. International Series in Operations Research & Management Science (pp. 395–421). Cham: Springer. https://doi.org/https://doi.org/10.1007/978-3-319-65455-3_16
- Gropp, W., & Moré, J. J. (1997). Optimization Environments and the NEOS Server. In M. D. Buhman, & A. Iserles (Eds.), Approximation theory and optimization (pp. 167–182). Cambridge University Press.
- Haimes, Y. (1971). On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man, and Cybernetics, 1, 296–297. https://doi.org/10.1109/TSMC.1971.4308298
- Hall, W. K. (1972). The application of multifunction stochastic service systems in allocating ambulances to an urban area. Operations Research, 20(3), 558–570. https://doi.org/https://doi.org/10.1287/opre.20.3.558
- Hashimoto, S., Matsuura, T., Nanko, K., Linkov, I., Shaw, G., & Kaneko, S. (2013). Predicted spatio-temporal dynamics of radiocesium deposited onto forests following the fukushima nuclear accident. Scientific Reports, 3(1), 1–5. https://doi.org/https://doi.org/10.1038/srep02564
- Hillsman, E., & Rhoda, R. (1978). Errors in measuring distances from populations to service centers. Annals of Regional Science, 1, 74–88. https://doi.org/https://doi.org/10.1007/BF01286124
- Hornberger, Z. T., Cox, B. A., & Hill, R. R. (2019). Effects of aggregation methodology on uncertain spatiotemporal data. arXiv preprint arXiv:1910.05125, 1–26. https://arxiv.org/abs/1910.05125.
- Jenkins, P. R., Lunday, B. J., & Robbins, M. J. (2020). Robust, multi-objective optimization for the military medical evacuation location-allocation problem. Omega, 97, 102088. https://doi.org/https://doi.org/10.1016/j.omega.2019.07.004
- Jia, H., Ordóñez, F., & Dessouky, M. (2007). A modeling framework for facility location of medical services for large-scale emergencies. IIE Transactions, 39(1), 41–55. https://doi.org/https://doi.org/10.1080/07408170500539113
- Karatas, M. (2021). A dynamic multi-objective location-allocation model for search and rescue assets. European Journal of Operational Research, 288(2), 620–633. https://doi.org/https://doi.org/10.1016/j.ejor.2020.06.003
- Karatas, M., Razi, N., & Gunal, M. M. (2017). An ILP and simulation model to optimize search and rescue helicopter operations. Journal of the Operational Research Society, 68(11), 1335–1351. https://doi.org/https://doi.org/10.1057/s41274-016-0154-7
- Laporte, G., Nickel, S., & da Gama, F. S. (2015). Location science. Springer.
- Madadgar, S., & Moradkhani, H. (2014). Spatio-temporal drought forecasting within bayesian networks. Journal of Hydrology, 512, 134–146. https://doi.org/https://doi.org/10.1016/j.jhydrol.2014.02.039
- Mandal, J. K., Mukhopadhyay, S., & Dutta, P. (2018). Multi-objective optimization: Evolutionary to hybrid framework. Springer.
- Marchione, E., & Johnson, S. D. (2013). Spatial, temporal and spatio-temporal patterns of maritime piracy. Journal of Research in Crime and Delinquency, 50(4), 504–524. https://doi.org/https://doi.org/10.1177/0022427812469113
- McLay, L. A. (2009). A maximum expected covering location model with two types of servers. IIE Transactions, 41(8), 730–741. https://doi.org/https://doi.org/10.1080/07408170802702138
- National Search and Rescue Committee. (2018). United States National Search and Rescue Supplement to the International Aeronautical and Maritime Search and Rescue Manual, Version 2.0. https://www.dco.uscg.mil/Portals/9/CG-5R/nsarc/NSS_2018_Version/National\%20SAR\%20Plan\%202018.pdf. Last visited on 09 April 2019.
- Openshaw, S. (1984). The modifiable areal unit problem. Geo Books.
- Prasannakumar, V., Vijith, H., Charutha, R., & Geetha, N. (2011). Spatio-temporal clustering of road accidents: Gis based analysis and assessment. Procedia - Social and Behavioral Sciences, 21, 317–325. https://doi.org/https://doi.org/10.1016/j.sbspro.2011.07.020
- Razi, N., & Karatas, M. (2016). A multi-objective model for locating search and rescue boats. European Journal of Operational Research, 254(1), 279–293. https://doi.org/https://doi.org/10.1016/j.ejor.2016.03.026
- Ross, S. M. (2014). Introduction to probability models. Academic press.
- Sabbaghtorkan, M., Batta, R., & He, Q. (2020). Prepositioning of assets and supplies in disaster operations management: Review and research gap identification. European Journal of Operational Research, 284(1), 1–19. https://doi.org/https://doi.org/10.1016/j.ejor.2019.06.029
- Simpson, N., & Hancock, P. (2009). Fifty years of operational research and emergency response. Journal of the Operational Research Society, 60, S126–S139. https://doi.org/https://doi.org/10.1057/jors.2009.3
- Sirvio, K., & Hollmén, J. (2008). Spatio-temporal road condition forecasting with Markov chains and artificial neural networks. In International Workshop on Hybrid Artificial Intelligence Systems (pp. 204–211). Springer.
- Snyder, L. V. (2006). Facility location under uncertainty: A review. IIE Transactions, 38(7), 547–564. https://doi.org/https://doi.org/10.1080/07408170500216480
- Snyder, L. V., Daskin, M. S., & Teo, C.-P. (2007). The stochastic location model with risk pooling. European Journal of Operational Research, 179(3), 1221–1238. https://doi.org/https://doi.org/10.1016/j.ejor.2005.03.076
- Soldo, F., Le, A., & Markopoulou, A. (2011). Blacklisting recommendation system: Using spatio-temporal patterns to predict future attacks. IEEE Journal on Selected Areas in Communications, 29(7), 1423–1437. https://doi.org/https://doi.org/10.1109/JSAC.2011.110808
- Tascikaraoglu, A., Sanandaji, B. M., Chicco, G., Cocina, V., Spertino, F., Erdinc, O., Paterakis, N. G., & Catalão, J. P. (2016). Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic power. IEEE Transactions on Sustainable Energy, 7(3), 1295–1305. https://doi.org/https://doi.org/10.1109/TSTE.2016.2544929
- Tastu, J., Pinson, P., Kotwa, E., Madsen, H., & Nielsen, H. A. (2011). Spatio-temporal analysis and modeling of short-term wind power forecast errors. Wind Energy, 14(1), 43–60. https://doi.org/https://doi.org/10.1002/we.401
- Teixeira, J. C., & Antunes, A. P. (2008). A hierarchical location model for public facility planning. European Journal of Operational Research, 185(1), 92–104. https://doi.org/https://doi.org/10.1016/j.ejor.2006.12.027
- Toregas, C., Swain, R., ReVelle, C., & Bergman, L. (1971). The location of emergency service facilities. Operations Research, 19(6), 1363–1373. https://doi.org/https://doi.org/10.1287/opre.19.6.1363
- United States Coast Guard District 14. (2014). CGD14INST M16130.1A - Fourteenth Coast Guard District search and rescue plan.
- United States Coast Guard. (2019a). Archived budgets, posture statements, and supporting documents. https://www.uscg.mil/Budget/Archive/. Last visited on 09 April 2019.
- United States Coast Guard. (2019b). Posture statement: 2018 performance highlights & 2020 budget overview. https://www.uscg.mil/Portals/0/documents/budget/FY2020_Budget_Overview_Web.pdf. Last visited on 09 April 2019.
- Wagner, M. R., & Radovilsky, Z. (2012). Optimizing boat resources at the US Coast Guard: Deterministic and stochastic models. Operations Research, 60(5), 1035–1049. https://doi.org/https://doi.org/10.1287/opre.1120.1085
- Wang, Q., Batta, R., & Rump, C. M. (2002). Algorithms for a facility location problem with stochastic customer demand and immobile servers. Annals of Operations Research, 111(1–4), 17–34. https://doi.org/https://doi.org/10.1023/A:1020961732667
- Wang, X., & Brown, D. E. (2011). The spatio-temporal generalized additive model for criminal incidents. In Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics (pp. 42–47). IEEE.
- Wang, X., & Brown, D. E. (2012). The spatio-temporal modeling for criminal incidents. Security Informatics, 1(1), 1–17. https://doi.org/https://doi.org/10.1186/2190-8532-1-2
- Wang, X., Brown, D. E., & Gerber, M. S. (2012). Spatio-temporal modeling of criminal incidents using geographic, demographic, and twitter-derived information. In 2012 IEEE International Conference on Intelligence and Security Informatics (pp. 36–41). IEEE.
- Wyatt, O. (2020). Guam Coast Guard receives first of three fast-response cutters planned for the Island. https://www.stripes.com/news/pacific/guam-coast-guard-receives-first-of-three-fast-response-cutters-planned-for-the-island-1.646378.
- Xie, L., Gu, Y., Zhu, X., & Genton, M. G. (2014). Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch. IEEE Transactions on Smart Grid, 5(1), 511–520. https://doi.org/https://doi.org/10.1109/TSG.2013.2282300
- Zadeh, L. (1963). Optimality and non-scalar-valued performance criteria. IEEE Transactions on Automatic Control, 8(1), 59–60. https://doi.org/https://doi.org/10.1109/TAC.1963.1105511
- Zhang, J., Zheng, Y., & Qi, D. (2017). Deep spatio-temporal residual networks for citywide crowd flows prediction. In Thirty-First AAAI Conference on Artificial Intelligence (pp. 1–7). AAAI.
- Zhou, X., Cheng, L., Min, K., Zuo, X., Yan, Z., Ruan, X., Chu, S., & Li, M. (2020). A framework for assessing the capability of maritime search and rescue in the South China Sea. International Journal of Disaster Risk Reduction, 47, 101568. https://doi.org/https://doi.org/10.1016/j.ijdrr.2020.101568
- Zhou, X., Cheng, L., Zhang, F., Yan, Z., Ruan, X., Min, K., & Li, M. (2019). Integrating island spatial information and integer optimization for locating maritime search and rescue bases: A case study in the South China Sea. ISPRS International Journal of Geo-Information, 8(2), 88. https://doi.org/https://doi.org/10.3390/ijgi8020088