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Decision Making in Humanitarian Logistics
A multi-objective optimization model for relocating relief goods during
disaster...
Agenda
Overlapping disaster
situations
Scenario planning and
model building
Solutions and decision
support
Overlapping disasters
What happens if the relief action does not proceed smoothly?
Relief action
Disruption
Disruption
Dis...
Overlapping disastersCharacteristics
• Relief infrastructure established
• Relief items in stock
• Aid workers in field
• ...
Problem description
Imbalance of
needs and
supply
Disruption
Relief item
relocation
Risk of
Decision making
Leads to
Distribution structure Problem description
Future A
Future B
Future C
Future D
Future E
Scenario planning
Factors influencing the future development
Future A
Future ...
Key
Biological factors
Environmental factors
Human related factors
Predictor variable
Dependent variable
Influencing facto...
Risk states Scenario planning
0 disruption
1 disruption
2 disruptions
3 disruptions
RD 3, Period 2
RD 3, Period 2,
RD 4, P...
Modelling uncertainty
• Forecasted needs for the relief action
• Risk of disruptions and therefore uncertain part of needs...
Unmet needs
Met needs
Certain needs
Period 1 Period 2
Uncertain needs
Handling unmet needs Modelling uncertainty
Optimization process
Network flow model
Network model based on Herer et al. (2006):
Mathematical model
Mathematical modelThree objective functions
1. Unmet certain needs
2. Unmet uncertain needs
3. Logistics costs
Transportat...
Mathematical modelConstraints
• Stock balance constraints
• For each depot, each period and total inventory balance
• Bala...
Mathematical modelConstraint method
2. Cost constraint
1. Weighted objective function
Dynamic solution selectionConstraint method
Dynamic solution selectionConstraint method
Computational results
Computational results
Decision making rules
Replenishments Reference model Critical value = 1 Critical value = 0.7
Low risk 20,770 21,220 29,943...
• Scenario planning helps to deal with uncertainties in overlapping disaster
situations
• Multiobjective optimization mode...
Title:
http://www.flickr.com/photos/icrc/4520631916/in/pool-relief#/photos/icrc/4520631916/in/pool-108
Slide 8: Protopopof...
Mathematical model
Decision variables
• Flow variables to balance the stocks
• Integer variables to calculate the required...
Mathematical model
Objective function
Grid point constraint
Mathematical model
Stock-balance at every regional depot (2) and at the central depot (3)
Demand-balance
Mathematical model
Total inventory balance
Calculation of fix transportation costs
Mathematical model
Calculation of certain unsatisfied demand in period t
Calculation of unsatisfied demand which occurred ...
Mathematical model
Calculation of the unsatisfied demand in period k which occured in
period t
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ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations

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Authors: Beate Rottkemper
Kathrin Fischer

Institute for Operations Research and Information Systems
Hamburg University of Technology

Published in: Education, Technology, Business
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ISCRAM 2013: A multi-objective optimization model for relocating relief goods during disaster recovery operations

  1. 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. 2. Agenda Overlapping disaster situations Scenario planning and model building Solutions and decision support
  3. 3. Overlapping disasters What happens if the relief action does not proceed smoothly? Relief action Disruption Disruption Disruption Overlapping disasters
  4. 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. 5. Problem description Imbalance of needs and supply Disruption Relief item relocation Risk of Decision making Leads to
  6. 6. Distribution structure Problem description
  7. 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. 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. 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. 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. 11. Unmet needs Met needs Certain needs Period 1 Period 2 Uncertain needs Handling unmet needs Modelling uncertainty
  12. 12. Optimization process
  13. 13. Network flow model Network model based on Herer et al. (2006): Mathematical model
  14. 14. Mathematical modelThree objective functions 1. Unmet certain needs 2. Unmet uncertain needs 3. Logistics costs Transportation costsTransshipment costs Inventory holding costsReplenishment costs
  15. 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. 16. Mathematical modelConstraint method 2. Cost constraint 1. Weighted objective function
  17. 17. Dynamic solution selectionConstraint method
  18. 18. Dynamic solution selectionConstraint method
  19. 19. Computational results
  20. 20. Computational results
  21. 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. 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. 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. 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. 25. Mathematical model Objective function Grid point constraint
  26. 26. Mathematical model Stock-balance at every regional depot (2) and at the central depot (3) Demand-balance
  27. 27. Mathematical model Total inventory balance Calculation of fix transportation costs
  28. 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. 29. Mathematical model Calculation of the unsatisfied demand in period k which occured in period t

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