Decision alternatives between expected cost minimization and worst case scenario in emergency supply - Second revision

International Journal of Production Economics

Volumn 157, Issue 1, 2014, Pages 250-260

  Kelle, Peter ^a   Schneider, Helmut ^a   Yi, Huizhi ^a  

^a LOUISIANA STATE UNIVERSITY   (United States)

Author keywords

Decision criteria; Disaster supply; Emergency supply chain management; Stochastic programming; Supply chain management

Indexed keywords

CANNING; CHAINS; COSTS; DECISION MAKING; STOCHASTIC PROGRAMMING; STOCHASTIC SYSTEMS;

DECISION CRITERIONS; DECISION MAKERS; DECISION MODELING; PRE-POSITIONING; PREPAREDNESS AND RESPONSE; PUBLIC SCRUTINY; RELIABILITY LEVEL; WORST CASE SCENARIO;

SUPPLY CHAIN MANAGEMENT;

EID: 84922653901     PISSN: 09255273     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijpe.2014.06.009     Document Type: Conference Paper

References (30)

1. Ahmed, S., 2006. Convexity and decomposition of mean-risk stochastic programs. Math. Program. 106 (3), 433-446.
2. Altay, N., Green, W.G., 2006. OR/MS research in disaster operations management. Eur. J. Oper. Res. 175 (1), 475-493.
3. American Red Cross, 2009. 〈http://itunes.apple.com/us/app/american-red-cross-shelter〉.
4. An, S., Cui, N., Li, X., Ouyang, Y., 2013. Location planning for transit-based evacuation under the risk of service disruptions. Transp. Res. Part B: Methodol. 54 (8), 1-16.
5. Balcik, B., Beamon, B.M., 2008. Facility location in humanitarian relief. Int. J. Logist.: Res. Appl. 11 (2), 101-121.
6. Barbarosoglu, G., Arda, Y., 2004. A two-stage stochastic programming framework for transportation planning in disaster response. J. Oper. Res. Soc. 55 (11), 43-53.
7. Birge, J.R., Louveaux, F.V., 1997. Introduction to Stochastic Programming. Springer, New York.
8. Boin, A., Kelle, P., Whybark, D.C., 2010. Resilient supply chains for extreme situations: outlining a new field of study. Int. J. Prod. Econ. 126 (1), 1-6.
9. Campbell, A.M., Jones, P.C., 2011. Prepositioning supplies in prepositioning for disaster. Eur. J. Oper. Res. 209 (2), 156-165.
10. Daskin, M.S., Hesse, M.H., ReVelle, C.S., 1997. a-reliable P-Minimax regret: a new model for facility location modeling. Locat. Sci. 5 (4), 227-246.
11. Fábián, C.I., 2008. Handling CVAR objectives and constraints in two-stage stochastic models. Eur. J. Oper. Res. 191 (3), 888-911.
12. Galindo, G., Batta, R., 2013. Prepositioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies. Socio-Econ. Plan. Sci. 47 (1), 20-37.
13. Hadas, Y., Laor, A., 2013. Network design model with evacuation constraints. Transp. Res. Part A: Policy Pract. 47 (1), 1-9.
14. Holguín-Veras, J., Pérez, N., Jaller, M., Van Wassenhove, L.N., Aros-Vera, F., 2013. On the appropriate objective function for post-disaster humanitarian logistics models. J. Oper. Manag. 31 (5), 262-280.
15. Kelle, P., Schneider, H., Yi, H., 2011. Optimization for a Disaster Management Problem of the Louisiana Gulf Coast, Working Paper. Louisiana State University, ISDS.
16. Klotzbach, P., Gray, W., Shama, U., Harman, L., Fitch, D., 2009. United States landfalling hurricane probability project. 〈http://www.e-transit.org/hurricane/welcome.html〉 (accessed 15.06.12).
17. Li, L., Jin, M., Zhang, L., 2011. Sheltering network planning and management responding to natural disasters. Int. J. Prod. Econ. 131 (2), 431-440.
18. Liu, C., Fan, Y., Ordonez, F., 2009. A two-stage stochastic programming model for transportation network protection. Comput. Oper. Res. 36 (5), 1582-1590.
19. Mete, H.O., Zabiski, Z.B., 2010. Stochastic optimization of medical supply location and distribution in disaster management. Int. J. Prod. Econ. 126 (1), 76-84.
20. Murray-Tuite, P., Wolshon, B., 2013. Evacuation transportation modeling: an overview of research, development, and practice. Transp. Res. Part C: Emerg. Technol. 27 (2), 25-45.
21. Neumann, C.J., Jarvinen, B.R., McAdie, C.J., Hammer, G.R., 1999. Tropical cyclones of the North Atlantic Ocean, 1871-1998. Historical Climatology Series 6-2. National Climatic Data Center, 206 pp.
22. Noyan, N., 2012. Risk-averse two-stage stochastic programming with an application to disaster management. Comput. Oper. Res. 39 (3), 541-559.
23. Osei-Bryson, K.-M., Joseph, A., 2009. Operations research approaches for disaster recovery planning. Comput. Oper. Res. 36 (1), 1-230.
24. Rawls, C.G., Turnquist, M.A., 2010. Pre-positioning of emergency supplies for disaster response. Transp. Res. Part B: Methodol. 44 (4), 521-534.
25. Rawls, C.G., Turnquist, M.A., 2012. Pre-positioning and dynamic delivery planning for short-term response following a natural disaster. Socio-Econ. Plan. Sci. 46 (1), 46-54.
26. Saadatseresht, M., Mansourian, A., Taleai, M., 2009. Evacuation planning using multi-objective evolutionary optimization approach. Eur. J. Oper. Res. 198 (1), 305-314.
27. Stepanov, A., Smith, J.M., 2009. Multi-objective evacuation routing in transportation networks. Eur. J. Oper. Res. 198 (2), 435-446.
28. Tang, C.S., 2006. Perspectives in supply risk management. Review. Int. J. Prod. Econ. 103 (1), 451-488.
29. Taskin, S., Lodree, E.J., 2010. Inventory decisions for emergency supplies based on hurricane count predictions. Int. J. Prod. Econ. 126 (1), 66-75.
30. Yi, W., Ozdamar, L., 2007. A dynamic logistics coordination model for evacuation and support in disaster response activities. Eur. J. Oper. Res. 179 (3), 1177-1193.