• Save
ISCRAM 2013: Building robust supply networks for effective and efficient disaster response
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

ISCRAM 2013: Building robust supply networks for effective and efficient disaster response

on

  • 859 views

Authors: Tina Comes, Frank Schätter, Frank Schultmann

Authors: Tina Comes, Frank Schätter, Frank Schultmann

Statistics

Views

Total Views
859
Views on SlideShare
356
Embed Views
503

Actions

Likes
0
Downloads
0
Comments
0

7 Embeds 503

http://www.disasterresiliencelab.org 266
http://ciem.uia.no 172
http://ciem.prosjekt.uia.no.2.erkunde.no 41
http://www.weebly.com 19
http://translate.googleusercontent.com 3
http://webcache.googleusercontent.com 1
http://unjobs.org 1
More...

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

ISCRAM 2013: Building robust supply networks for effective and efficient disaster response Presentation Transcript

  • 1. INSTITUTE FOR INDUSTRIAL PRODUCTION (IIP) CENTER FOR DISASTER MANAGEMENT AND RISK REDUCTION TECHNOLOGY (CEDIM) KIT – University of the State of Baden-Württemberg and National Research Center of the Helmholtz Association1 Institute for Industrial Production - Risk Management Research Unit 06 July INSTITUTE FOR INDUSTRIAL PRODUCTION (IIP) Building robust supply networks for effective and efficient disaster response KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association Tina Comes, Frank Schätter, Frank Schultmann
  • 2. Institute for Industrial Production (IIP)15.05.2013 Outline ISCRAM 2013 – Baden-Baden Introduction An iterative dynamic approach for decision support Use case: the facility location problem (FLP) in humanitarian relief logistics Conclusion
  • 3. Institute for Industrial Production (IIP)15.05.2013 Outline ISCRAM 2013 – Baden-Baden Introduction An iterative dynamic approach for decision support Use case: the facility location problem (FLP) in humanitarian relief logistics Conclusion
  • 4. Institute for Industrial Production (IIP)15.05.2013 Decision support in SCM Business supply chain management and logistics  Focus on efficiency, e.g. cost reduction Crisis management  Focus on effectiveness, e.g. service level What about efficiency in crisis management? Limited resources Avoidance of a waste of resources  Trade-off between effectiveness and efficiency! ISCRAM 2013 – Baden-Baden1
  • 5. Institute for Industrial Production (IIP)15.05.2013 Humanitarian relief logistics Humanitarian relief logistics (Thomas 2003) “is defined as the process of planning, implementing and controlling the efficient, cost-effective flow and storage of goods and materials […] for the purpose of alleviating the suffering of vulnerable people.” Humanitarian relief supply networks (SNs) Objective: Distribution of relief goods from different sources to the destinations where they are needed Challenges: Complexity and uncertainty Response to critical infrastructure (CI) failures (e.g. food, water, medicine, transportation) and cascading effects propagating via interlaced CI networks Lacking or uncertain information about the needs of the population, CI system and available resources ISCRAM 2013 – Baden-Baden2
  • 6. Institute for Industrial Production (IIP)15.05.2013 Decision cycle  No explicit description on how to generate alternatives ISCRAM 2013 – Baden-Baden3 Problem structuring and preference elicitation Determination of consequences Evaluation of alternatives Decision and implementation Monitoring and control
  • 7. Institute for Industrial Production (IIP)15.05.2013 Decision support for robust humanitarian relief SNs Robustness indicators Stability of the performance Quality of the performance Evaluation criteria Efficiency: SN’s capacity to perform in a sufficiently organized manner Effectiveness: Creating flexible SNs that allow meeting the exigencies of unforeseen disturbances Alternatives for a specific decision problem (e.g. facility locations) Consideration of complexity and uncertainty by using scenarios Identification of an alternative that performs relatively well when compared to further alternatives across a wide range of scenarios ISCRAM 2013 – Baden-Baden4
  • 8. Institute for Industrial Production (IIP)15.05.2013 Outline ISCRAM 2013 – Baden-Baden Introduction An iterative dynamic approach for decision support Use case: the facility location problem (FLP) in humanitarian relief logistics Conclusion
  • 9. Institute for Industrial Production (IIP)15.05.2013 Rationale for an iterative dynamic approach ISCRAM 2013 – Baden-Baden Dynamic descriptions of the disaster‘s development (e.g. CI disruptions) Taking into account interdependencies and adaptations to changes in information and preferences Iterative search for better alternatives Decision is always based on the best currently available information Individual Decision Problem Problem structuring Optimisation Dynamic scenario construction Evaluation Decision & Monitoring 5
  • 10. Institute for Industrial Production (IIP)15.05.2013 Iterative dynamic approach ISCRAM 2013 – Baden-Baden Problem structuring Decision problem and context assumptions Initial scenario construction Optimisation Generation of alternatives to be further investigated Use of a simulation model to identify a finite set of promising alternatives Dynamic scenario construction What could go wrong for each alternative? Generation of a set of scenarios per alternative Evaluation Evaluation of scenarios and comparison of alternatives Ranking of alternatives 6
  • 11. Institute for Industrial Production (IIP)15.05.2013 Outline ISCRAM 2013 – Baden-Baden Introduction An iterative dynamic approach for decision support Use case: the facility location problem (FLP) in humanitarian relief logistics Conclusion
  • 12. Institute for Industrial Production (IIP)15.05.2013 Use case: Facility location problem (FLP) ISCRAM 2013 – Baden-Baden Haiti Earthquake 2010 Identification of best locations for warehouses (health care centres) Objectives: Effectiveness: Guarantee the distribution of health care services to all in need Efficiency: Reduction of travelling and transportation times 7
  • 13. Institute for Industrial Production (IIP)15.05.2013 Application of the iterative dynamic approach ISCRAM 2013 – Baden-Baden Step 1 Problem structuring & initial scenarios Step 2 Optimisation Step 3 Dynamic scenario construction & optimal locations Step 4 Evaluation & scenario selection: significance Step 3 Dynamic scenario construction & optimal locations … … 8
  • 14. Institute for Industrial Production (IIP)15.05.2013 Step 1: Problem structuring ISCRAM 2013 – Baden-Baden Initial scenario construction Scenario variable Description Characteristics Context variables • Background information • Example: Epicentre Values are constant across scenarios Strategies • Variables that can be controlled by the decision-makers • Example: Transportation mode Values vary across scenarios Specifying variables • Variables are prone to uncertainties • Example: Population’s behaviour and migration Multiple values across scenarios 9
  • 15. Institute for Industrial Production (IIP)15.05.2013 Step 2: Optimisation ISCRAM 2013 – Baden-Baden Minimising the sum of transportation and fixed costs of warehouses  durations Trade-off between rapid computation time and precision  heuristics Heuristics enable integrating of new information and updates, which is important for dynamic situations Optimisation procedure Computation of the service level (effectiveness) and duration (efficiency) for each alternative Dijkstra algorithm (shortest paths) ADD-heuristic (solving FLP) Optimal allocation 10
  • 16. Institute for Industrial Production (IIP)15.05.2013 Step 3: Dynamic scenario construction & optimal locations ISCRAM 2013 – Baden-Baden Introduction of environments that disturb the functions of most critical parts of the SNs Two disruptive environments E2 and E3 are created per scenario, describing harmful disruptions of the road network E2 : Doubled durations to all neighbouring sections of an alternative E3 : Critical path is assumed to fail Optimisation: Generation of best alternatives for the new scenarios 11
  • 17. Institute for Industrial Production (IIP)15.05.2013 Step 4: Evaluation ISCRAM 2013 – Baden-Baden Selecting most significant scenarios Complexity reduction because of an overwhelming number of scenarios Significance of a scenario is measured by stability and quality indicators Stability indicators (1) Number of location changes (2) Relative loss Quality indicator (3) Regret Most significant scenarios are the basis for the scenario construction in the next iteration Evaluation of facilities and decision by using techniques of multi-attribute decision- making (MADM) such as MAVT 12
  • 18. Institute for Industrial Production (IIP)15.05.2013 Results – Dynamically changed scenarios ISCRAM 2013 – Baden-Baden critical path Initial scenarios: Promising alternatives a1=[32,34,37] and a2=[32,34,36] Changes of locations in E3 13
  • 19. Institute for Industrial Production (IIP)15.05.2013 Results - Evaluation ISCRAM 2013 – Baden-Baden Promising alternatives a1=[20, 32, 34] a2=[32, 34, 37] a3=[32, 34, 42] 14
  • 20. Institute for Industrial Production (IIP)15.05.2013 Outline ISCRAM 2013 – Baden-Baden Introduction Iterative dynamic approach Use Case: The Facility Location Problem (FLP) in humanitarian relief Conclusion
  • 21. Institute for Industrial Production (IIP)15.05.2013 Conclusion ISCRAM 2013 – Baden-Baden Iterative approach for robust decision support in the design of humanitarian relief supply networks (SNs) Robustness comprises an achievement of stability and quality in terms of effectiveness and efficiency Combination of an optimisation model, scenario-based techniques and approaches from Multi-Criteria Decision Analysis (MCDA) Dynamic approach is targeted at unveiling the most important weaknesses of the alternatives and selecting the most significant scenarios Illustration of the approach by referring to one of the most well documented disasters: the 2010 Haiti earthquake 15
  • 22. Institute for Industrial Production (IIP)15.05.2013 Future research ISCRAM 2013 – Baden-Baden Integration of information from local sources Additional iteration steps Intervention points Number of warehouses Detailed warehouse planning within the selected regions 16
  • 23. Institute for Industrial Production (IIP)15.05.2013 ISCRAM 2013 – Baden-Baden Thank you very much for your attention!
  • 24. Institute for Industrial Production (IIP)15.05.2013 References ISCRAM 2013 – Baden-Baden Afshar, A. and Haghani, A. (2012) Modeling integrated supply chain logistics in real-time large-scale disaster relief operations, Socio-Economic Planning Sciences, 46, 327–33. Ben-Haim, Y. (2000) Robust rationality and decisions under severe uncertainty, Journal of the Franklin Institute, 337(2-3), 171–199. Boin, A. and McConnell, A. (2007) Preparing for Critical Infrastructure Breakdowns: The Limits of Crisis Management and the Need for Resilience, Journal of Contingencies and Crisis Management, 15(1), 50–59. Comes, T. et al. (2010) Enhancing Robustness in Multi-Criteria Decision-Making: A Scenario-Based Approach, Proceedings of the 2nd International Conference on Intelligent Networking and Collaborative Systems. Hites, R. et al. (2006) About the applicability of MCDA to some robustness problems, European Journal of Operational Research, 174(1), 322–332. Kotabe, M. (1998) Efficiency vs. effectiveness orientation of global sourcing strategy: A comparison of U.S. and Japanese multinational companies, Academy of Management Perspectives, 12(4), 107–119. Kovacs, G.L. and Paganelli, P. (2003) A planning and management infrastructure for large, complex, distributed projects--beyond ERP and SCM, Computers in Industry, 51(2), 165–183. Tang, C. (2006) Robust strategies for mitigating supply chain disruptions, International Journal of Logistics, 9(1), 33–45. Tomasini, R.M. and Van Wassenhove, L.N. (2009) From preparedness to partnerships: case study research on humanitarian logistics, International Transactions in Operational Research, 16(5), 549–559. Vincke, Philippe (1999) Robust solutions and methods in decision-aid, Journal of Multi-Criteria Decision Analysis, 8(3), 181–187.
  • 25. Institute for Industrial Production (IIP)15.05.2013 Backup ISCRAM 2013 – Baden-Baden Initial scenario construction  Construction of 72 initial scenarios S Class Description Characteristics Context variables • Background information • Information about the triggering event • Information about goals and preferences of actors • Examples: Epicentre, disaster phase, constraints for facility locations Values are constant across scenarios Strategies • Combinations of alternatives • Variables that can be controlled by the decision-makers • Examples: Transportation mode, number of facilities Values vary across scenarios Specifying variables • Variables are prone to uncertainties • Variables describe events or developments that affect the effectiveness and efficiency of any SN • Examples: Initial demand level, population’s behaviour and migration, environmental developments, possible aftershocks Multiple values across scenarios
  • 26. Institute for Industrial Production (IIP)15.05.2013 Backup Stability indicators (1) Location changes to achieve the optimum aij *(E2,3) for the new scenario Sij(E2,3) (2) Relative loss, measured by the deviation of the performance of ai in Sij(E2,3) from the initial performance of ai in Si Quality indicator (3) Regret: Loss of performance due to the implementation of ai instead of aij * ISCRAM 2013 – Baden-Baden