Building Robust Supply Networks for Effective Disaster Response

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

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

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

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

Decision cycle
 No explicit description on how to generate alternatives
Problem structuring and preference elicitation
Determination of consequences
Evaluation of alternatives
Decision and implementation
Monitoring and control

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

Rationale for an iterative dynamic approach
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

Iterative dynamic approach
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

Use case: Facility location problem (FLP)
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

Application of the iterative dynamic approach
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

Step 1: Problem structuring
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

Step 2: Optimisation
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

Step 3: Dynamic scenario construction &
optimal locations
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

Step 4: Evaluation
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

Results – Dynamically changed scenarios
critical path
Initial scenarios: Promising alternatives a1=[32,34,37] and a2=[32,34,36]
Changes of locations in E3
13

Results - Evaluation
Promising alternatives
a1=[20, 32, 34]
a2=[32, 34, 37]
a3=[32, 34, 42]
14

Outline
Introduction
Iterative dynamic approach
Use Case: The Facility Location Problem (FLP) in humanitarian relief
Conclusion

Conclusion
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

Future research
Integration of information from local sources
Additional iteration steps
Intervention points
Number of warehouses
Detailed warehouse planning within the selected regions
16

Institute for Industrial Production (IIP)15.05.2013 ISCRAM 2013 – Baden-Baden
Thank you very much for your attention!

References
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.

Backup
 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

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
*

Building Robust Supply Networks for Effective Disaster Response

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