Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Regional Science: What Matters? Which Matters? Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

15. An Integrated Disaster Relief Supply Chain Network Model with Time Targets and Demand Uncertainty

verfasst von : Anna Nagurney, Amir H. Masoumi, Min Yu

Erschienen in: Regional Science Matters

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

As the number of natural disasters and their impacts increase across the globe, the need for effective preparedness against such events becomes more vital. In this paper, we construct a supply chain network optimization model for a disaster relief organization in charge of obtaining, storing, transporting, and distributing relief goods to certain disasterprone regions. Our system-optimization approach minimizes the total operational costs on the links of the supply chain network subject to the uncertain demand for aid at the demand points being satisfied as closely as possible. A goal programming approach is utilized to enforce the timely delivery of relief items with respect to the pre-specified time targets at the demand points. A solution algorithm for the model is also provided. A spectrum of numerical examples illustrates the modeling and computational framework, which integrates the two policies of pre-positioning relief supplies as well as their procurement once the disaster has occurred.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Representing Negotiation Space
Nächstes Kapitel Regional Dynamics Under Adverse Physical and Behavioral Shocks: The Economic Consequences of a Chlorine Terrorist Attack in the Los Angeles Financial District
Literatur
Alexander D (1993) Natural disasters. Kluwer, Dordrecht
Balcik B, Beamon BM (2005) Facility location in humanitarian relief. Int J Logist Res Appl 11(2):101–121CrossRef
Barbarosoglu G, Arda Y (2004) A two-stage stochastic programming framework for transportation planning in disaster response. J Oper Res Soc 55(1):43–53CrossRef
Beamon B, Kotleba S (2006) Inventory management support systems for emergency humanitarian relief operations in South Sudan. Int J Logist Manag 17(2):187–212CrossRef
Beckmann MJ, McGuire CB, Winsten CB (1956) Studies in the economics of transportation. Yale University Press, New Haven, CT
Bertsekas D, Tsitsiklis J (1989) Parallel and distributed computation: numerical methods. Prentice Hall, Englewood Cliffs, NJ
Blaikie P, Cannon T, Davis I, Wisner B (1994) At risk: natural hazards, people’s vulnerability and disasters. Routledge, London
Charles A, Lauras M (2011) An enterprise modelling approach for better optimisation modelling: application to the humanitarian relief chain coordination problem. OR Spectrum 33:815–841CrossRef
Cho S, Gordon P, Moore JE II, Richardson HW, Shinozuka M, Chang SE (2001) Integrating transportation network and regional economic models to estimate the costs of a large urban earthquake. J Reg Sci 41(1):39–65CrossRef
Dacy DC, Kunreuther H (1969) The economics of natural disasters: implications for federal policy. Free, New York, NY
Dafermos SC, Sparrow FT (1969) The traffic assignment problem for a general network. J Res Natl Bur Stand 73B:91–118CrossRef
Dong J, Zhang D, Nagurney A (2004) A supply chain network equilibrium model with random demands. Eur J Oper Res 156:194–212CrossRef
Dupuis P, Nagurney A (1993) Dynamical systems and variational inequalities. Ann Oper Res 44:9–42CrossRef
Falasca M, Zobel CW (2011) A two-stage procurement model for humanitarian relief supply chains. J Humanitarian Logist Suppl Chain Manage 1(2):151–169CrossRef
Greenberg MR, Lahr M, Mantell N (2007) Understanding the economic costs and benefits of catastrophes and their aftermath: a review and suggestions for the U.S. federal government. Risk Anal 27(1):83–96CrossRef
Grubesic TH, Matisziw TC, Murray AT, Snediker D (2008) Comparative approaches for assessing network vulnerability. Int Reg Sci Rev 31(1):88–112CrossRef
Haghani A, Oh SC (1996) Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations. Transport Res A 30(3):231–250
Hale T, Moberg C (2005) Improving supply chain disaster preparedness: a decision process for secure site location. Int J Phys Distrib Logist Manag 35(3):195–207CrossRef
Ham H, Kim TJ, Boyce D (2005) Assessment of economic impacts from unexpected events with an interregional commodity flow and multimodal transportation network model. Transport Res A 39:849–860
Huang M, Smilowitz K, Balcik B (2012) Models for relief routing: equity, efficiency and efficacy. Transport Res E 48:2–18CrossRef
Isard W, Azis IJ, Drennan MP, Miller RE, Saltzman S, Thorbecke E (1998) Methods of interregional and regional analysis. Ashgate, Aldershot
Israelevich PR, Hewings GJD, Sonis M, Schindler GR (1997) Forecasting structural change with a regional econometric input-output model. J Reg Sci 37(4):565–590CrossRef
Karush W (1939) Minima of functions of several variables with inequalities as side constraints. M.Sc. Dissertation. Department of Mathematics, University of Chicago, Chicago, IL
Kuhn HW, Tucker AW (1951) Nonlinear programming. In: Proceedings of 2nd Berkeley symposium. University of California Press, Berkeley, CA, p 481–492
Kunreuther H (1967) The peculiar economics of disaster. Pap Nonmarket Decis Making 3(1):67–83
Kunreuther H, Michel-Kerjan E (2012) Policy options for reducing losses from natural disasters: Allocating $75 billion. Revised version for Copenhagen Consensus 2012
Lin Y-H (2010) Delivery of critical items in a disaster relief operation: centralized and distributed supply strategies. Ph.D. thesis in Industrial and Systems Engineering, The University at Buffalo, State University of New York
Liu Z, Nagurney A (2013) Supply chain networks with global outsourcing and quickresponse production under demand and cost uncertainty. Ann Oper Res 208(1):251–289CrossRef
MacKenzie CA, Barker K (2011) Conceptualizing the broader impacts of industry preparedness strategies with a risk-based input-output model. In: Lahr M, Hubacek K (eds) Proceedings of the 19th international input-output conference. Alexandria, VA, June 13–17
Mete HO, Zabinsky ZB (2010) Stochastic optimization of medical supply location and distribution in disaster management. Int J Prod Econ 126:76–84CrossRef
Nagurney A (1999) Network economics: a variational inequality approach, 2nd and revised edn. Kluwer, Dordrecht
Nagurney A, Masoumi A (2012) Supply chain network design of a sustainable blood banking system. In: Boone T, Jayaraman V, Ganeshan R (eds) Sustainable supply chains: models, methods and public policy implications, vol 174, International series in operations research & management science. Springer, London, pp 49–70CrossRef
Nagurney A, Qiang Q (2009) Fragile networks: identifying vulnerabilities and synergies in an uncertain world. Wiley, Hoboken, NJCrossRef
Nagurney A, Qiang Q (2012) Fragile networks: identifying vulnerabilities and synergies in an uncertain age. Int Trans Oper Res 19:123–160CrossRef
Nagurney A, Ramanujam P (1996) Transportation network policy modeling with goal targets and generalized penalty functions. Transport Sci 30(1):3–13CrossRef
Nagurney A, Yu M (2014) A supply chain network game theoretic framework for timebased competition with transportation costs and product differentiation. In: Butenko S, Floudas CA, Rassias ThM (eds) Optimization in science and engineering—in honor of the 60th birthday of Panos M. Pardalos. Springer, New York, NY
Nagurney A, Zhang D (1996) Projected dynamical systems and variational inequalities with applications. Kluwer, Boston, MACrossRef
Nagurney A, Thore S, Pan J (1996) Spatial market policy modeling with goal targets. Oper Res 44(2):393–406CrossRef
Nagurney A, Yu M, Qiang Q (2011) Supply chain network design for critical needs with outsourcing. Pap Reg Sci 90(1):123–143CrossRef
Nagurney A, Masoumi A, Yu M (2012a) Supply chain network operations management of a blood banking system with cost and risk minimization. Comput Manag Sci 9(2):205–231CrossRef
Nagurney A, Yu M, Qiang Q (2012b) Multiproduct humanitarian healthcare supply chains: a network modeling and computational framework. In: Proceedings of the 23rd annual POMS conference, Chicago, IL
Nagurney A, Yu M, Masoumi AH, Nagurney LS (2013) Networks against time: supply chain analytics for perishable products. Springer Business + Science Media, New York, NYCrossRef
Okuyama Y (2003) Economics of natural disasters: a critical review. Paper presented at the 50th North American meeting, regional science association international Philadelphia, Pennsylvania
Okuyama Y (2004) Sequential interindustry model (SIM) and impact analysis: application for measuring economic impact of unscheduled events. In: Okuyama Y, Chang S (eds) Modeling spatial and economic impacts of disasters. Springer, HeidelbergCrossRef
Okuyama Y, Hewings GJD, Sonis M (1999) Economic impacts of an unscheduled, disruptive event: a Miyazawa multiplier analysis. In: Hewings GJD, Sonis M, Madden M, Kimura Y (eds) Understanding and interpreting economic structure. Springer, Heidelberg
Oloruntoba R, Gray R (2006) Humanitarian aid: an agile supply chain? Suppl Chain Manag 11(2):115–120CrossRef
Ortuño MT, Tirado G, Vitoriano B (2011) A lexicographical goal programming based decision support system for logistics of humanitarian aid. TOP 19(2):464–479CrossRef
Ortuño MT, Cristóbal P, Ferrer JM, Martin-Campo FJ, Muñoz S, Tirado G, Vitoriano B (2013) Decision aid models and systems for humanitarian logistics: a survey. In: Vitoriano B, Montero J, Ruan D (eds) Decision aid models for disaster management and emergencies, vol 7, Atlantis computational intelligence systems. Springer Business + Science Media, New York, NY, pp 17–44CrossRef
Qiang Q, Nagurney A (2012) A bi-criteria indicator to assess supply chain network performance for critical needs under capacity and demand disruptions. Transport Res A 46(5):801–812
Reggiani A, Nijkamp P (2009) Simplicity in complex spatial systems: introduction. In: Reggiani A, Nijkamp P (eds) Complexity and spatial networks: in search of simplicity. Springer, HeidelbergCrossRef
Rose A (2009) Economic resilience to disasters. Final report to Community and Regional Resilience Institute, CARRII Research Report 8, Oak Ridge National Lab, Tennessee
Rose A, Liao S-Y (2005) Modeling regional economics resilience to disasters: a computable general equilibrium analysis of water service disruptions. J Reg Sci 45(1):75–112CrossRef
Rose A, Benavides J, Chang SE, Szczesniak P, Lim D (1997) The regional economic impact of an earthquake: direct and indirect effects of electricity lifeline disruptions. J Reg Sci 37(3):437–458CrossRef
Rottkemper B, Fischer K, Blecken A (2012) A transshipment model for distribution and inventory relocation under uncertainty in humanitarian operations. Socioecon Plann Sci 46:98–109CrossRef
Schultz C, Koenig K, Noji E (1996) A medical disaster response to reduce immediate mortality after an earthquake. N Engl J Med 334:438–444CrossRef
Sheu J-B (2010) Dynamic relief-demand management for emergency logistics operations under large-scale disasters. Transport Res E 46:1–17CrossRef
Simpson N, Hancock P (2009) The incident commander’s problem: resource allocation in the context of emergency response. Int J Serv Sci 2(2):102–124
Tomasini R, Van Wassenhove L (2009) Humanitarian logistics. Palgrave Macmillan, BasingstokeCrossRef
Tzeng G-H, Cheng H-J, Huang T (2007) Multi-objective optimal planning for designing relief delivery systems. Transport Res E 43(6):673–686CrossRef
UNHCR (2007) United Nations High Commissioner for Refugees UNHCR Handbook for emergencies, 3rd edn
USAID (2005) United States Agency for International Development, Office of Foreign Disaster Assistance, Field operations guide for disaster assessment and response, Version 4.0
Vitoriano B, Ortuño M, Tirado G, Montero M (2011) A multi-criteria optimization model for humanitarian aid distribution. J Global Optim 51:189–208CrossRef
Vitoriano B, Montero J, Ruan D (eds) (2013) Decision aid models for disaster management and emergencies, vol 7, Atlantis computational intelligence systems. Springer Business + Science Media, New York, NY
Walton R, Mays R, Haselkorn M (2011) Defining “fast”: factors affecting the experience of speed in humanitarian logistics. In: Proceedings of the 8th International ISCRAM Conference, May, Lisbon, Portugal
West CT, Lenze DG (1994) Modeling the regional impact of natural disaster and recovery: a general framework and an application to Hurricane Andres. Int Reg Sci Rev 17(2):121–150CrossRef
Zhenling L (2009) Integrated supply chains of the natural disaster relief substances. International Conference on Management and Service Science (Mass’09), Wuhan, China
Metadaten
Titel
An Integrated Disaster Relief Supply Chain Network Model with Time Targets and Demand Uncertainty
verfasst von
Anna Nagurney
Amir H. Masoumi
Min Yu
Copyright-Jahr
2015
Buch
Regional Science Matters

Print ISBN: 978-3-319-07304-0

Electronic ISBN: 978-3-319-07305-7

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-07305-7_15