Your SlideShare is downloading. ×
0
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations

104

Published on

Authors: Beate Rottkemper …

Authors: Beate Rottkemper
Kathrin Fischer

Institute for Operations Research and Information Systems
Hamburg University of Technology

Published in: Education, Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
104
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Decision Making in Humanitarian Logistics A multi-objective optimization model for relocating relief goods during disaster recovery operations Beate Rottkemper Kathrin Fischer Institute for Operations Research and Information Systems Hamburg University of Technology
  • 2. Agenda Overlapping disaster situations Scenario planning and model building Solutions and decision support
  • 3. Overlapping disasters What happens if the relief action does not proceed smoothly? Relief action Disruption Disruption Disruption Overlapping disasters
  • 4. Overlapping disastersCharacteristics • Relief infrastructure established • Relief items in stock • Aid workers in field • Budget is tight • Transportation network is known • Infrastructure damages (partly) known • Shorter time horizon • Huge uncertainty
  • 5. Problem description Imbalance of needs and supply Disruption Relief item relocation Risk of Decision making Leads to
  • 6. Distribution structure Problem description
  • 7. Future A Future B Future C Future D Future E Scenario planning Factors influencing the future development Future A Future B Future C Future D Future E Initial situation
  • 8. Key Biological factors Environmental factors Human related factors Predictor variable Dependent variable Influencing factors Scenario planning Protopopoff et al., 2009 Longevity Density Malaria infection Immunity Intermittent preventive treatment Drug resistance Health status Age Health access Treatment Migration Gender Socio economic status Breeding sites Insecticide resistance Insecticide treated nets Indoor residual spraying Livestock Land use Human-vector contact Biological factors Human related factors Precipitation Temperature Altitude
  • 9. Risk states Scenario planning 0 disruption 1 disruption 2 disruptions 3 disruptions RD 3, Period 2 RD 3, Period 2, RD 4, Period 4 RD 2, Period 4, RD 3, Period 4, RD 4, Period 6 RD 3, Period 2, RD 4, Period 5, RD 5, Period 7 RD 4, Period 5 ... Sub-scenariosMain scenarios Initial situation Low risk High risk
  • 10. Modelling uncertainty • Forecasted needs for the relief action • Risk of disruptions and therefore uncertain part of needs …1 2 3 4 T Uncertain needs Certain needs period
  • 11. Unmet needs Met needs Certain needs Period 1 Period 2 Uncertain needs Handling unmet needs Modelling uncertainty
  • 12. Optimization process
  • 13. Network flow model Network model based on Herer et al. (2006): Mathematical model
  • 14. Mathematical modelThree objective functions 1. Unmet certain needs 2. Unmet uncertain needs 3. Logistics costs Transportation costsTransshipment costs Inventory holding costsReplenishment costs
  • 15. Mathematical modelConstraints • Stock balance constraints • For each depot, each period and total inventory balance • Balance of needs (each region, each period) • Capacity limitations (of trucks) for all relations and periods • Calculation of unmet need • Certain and uncertain needs
  • 16. Mathematical modelConstraint method 2. Cost constraint 1. Weighted objective function
  • 17. Dynamic solution selectionConstraint method
  • 18. Dynamic solution selectionConstraint method
  • 19. Computational results
  • 20. Computational results
  • 21. Decision making rules Replenishments Reference model Critical value = 1 Critical value = 0.7 Low risk 20,770 21,220 29,943 High risk 40,539 41,094 42,490 • The decision depends on the available budget and on the risk state • Low risk state: Results of reference model and critical value = 1 similar • High risk state: All results similar
  • 22. • Scenario planning helps to deal with uncertainties in overlapping disaster situations • Multiobjective optimization model with a rolling horizon solution approach integrates uncertainties in the planning process • Decisions can be made mainly based on environmental settings and on the monitoring of the relevant risk factors • Future research concentrates on the development of rules to support decision making in overlapping disaster situations Conclusion and outlook beate.rottkemper@tu-harburg.de
  • 23. Title: http://www.flickr.com/photos/icrc/4520631916/in/pool-relief#/photos/icrc/4520631916/in/pool-108 Slide 8: Protopopoff, N.; Van Bortel, W.; Speybroeck, N.; Van Geertruyden, J.-P.; Baza, D.; D’Alessandro U. & Coosemans, M.: Ranking Malaria Risk Factors to Guide Malaria Control Efforts in African Highlands. In: PLoS ONE 4 (2009), number 11, P. 1–10 Slide 13: Herer, Y. T.; Tzur, M. & Yücesan, E.: The multilocation transshipment problem. In: IIE Transactions 38 (2006), P. 185-200 Sources
  • 24. Mathematical model Decision variables • Flow variables to balance the stocks • Integer variables to calculate the required vehicles • Variables to calculate the penalty costs
  • 25. Mathematical model Objective function Grid point constraint
  • 26. Mathematical model Stock-balance at every regional depot (2) and at the central depot (3) Demand-balance
  • 27. Mathematical model Total inventory balance Calculation of fix transportation costs
  • 28. Mathematical model Calculation of certain unsatisfied demand in period t Calculation of unsatisfied demand which occurred before period t + 1 Calculation of max(0, UDitk) and max(0, UD2 itk)
  • 29. Mathematical model Calculation of the unsatisfied demand in period k which occured in period t

×