Prepositioning relief supplies
Facility location models place warehouses and stocks to minimise delivery time across hazard scenarios, subject to budget and road access.
GCRI · Disaster Resilience and Recovery
Operations research for disaster logistics
Optimisation and operations research decide how to use limited resources when time and access are constrained. In this field they place relief stocks, plan evacuation routes and shelters, and sequence restoration and reconstruction work.
01Contents
Operations research for disaster logistics in five parts.
02Application
What optimisation and operations research does here.
Optimisation and operations research develop mathematical methods for allocating resources and making decisions under constraints. They support planning and logistics in complex, interconnected systems. In disaster and crisis and climate and hazards, optimisation supports prepositioning of supplies, network investment and recovery sequencing. Robust formulations help choose options that perform acceptably across many scenarios. The canonical description is on the STEM Commons.
Evacuation and shelter planning
Network flow models estimate evacuation times and shelter loads, identifying bottlenecks in routes and capacity.
Restoration sequencing
Scheduling models order repair work on roads, power and water to restore the most critical services first, given crews and dependencies.
03Monitor
Optimisation run.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Optimisation run
Disaster resilience · Operations research for disaster · Run R-2049
Feasible, gap closingGCRI · Disaster Resilience and Recovery
Convergence
Method card
- Method
- Optimisation and operations research
- Variables
- 54,781
- Optimality gap
- 0.1 pct
- Label
- MODELLED
Binding constraints
| Capacity | 0 | 23 | Binding |
| Budget | 0 | 0.7 | Binding |
| Service level | 8 | 0 | Slack |
04Data
What the method consumes.
Datasets and records used by optimisation and operations research on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Road network data (OpenStreetMap and national) | Access and routes | OBSERVED |
| Hazard scenarios | Demand and disruption | MODELLED |
| Population grids | Demand | ESTIMATED |
| Warehouse and shelter inventories | Capacity | DECLARED |
05Simulator
A shock through an input-output economy.
A loss of output in one sector propagates through the technical coefficients to its suppliers and customers, and recovers at the rate you set; the coefficients are teaching values, not national accounts. Teaching model with stated illustrative values, not a prediction.
Controls
- Sector hit first (position in the list)
- 0
- Size of the shock
- 30% of its output
- Duration of the shock
- 30 days
- Recovery rate
- 0.08 per day
- Days simulated
- 180 days
Readouts
- Output lost over the run
- 0.59% of a year’s output
- Total over direct (multiplier)
- 1.91 times
- Most affected other sector
- Trade
- Its peak inoperability
- 4.7%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Agriculture | 30.39 | 9.8 |
| Energy | 1.01 | 22.9 |
| Manufacturing | 0.50 | 17.5 |
| Transport and ports | 3.20 | 12.4 |
| Trade | 4.66 | 16.2 |
| Services | 3.18 | 21.2 |
Methods
- Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
- Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
- Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Seaports Natural Earth
- Maritime chokepoints Nexus (named straits and canals at their published coordinates)
- Railways Natural Earth
- Major roads Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Disaster Resilience and Recovery in Foundry
- Tested at Disaster Resilience and Recovery in Universe
- Codified in Hazards and Disaster Risk Reduction in Standards
- Published in Disaster Resilience and Recovery in Reports
- Taught in Faculty of Hazards, Disasters and Resilience in Academy
06Validation
How results are checked.
Solutions are checked for feasibility, tested through simulation and compared with historical decisions. Sensitivity analysis examines how results change with parameters.
| Check | How it is done |
|---|---|
| Scenario robustness | Solutions are tested across many scenarios rather than one |
| Field validation | Travel times and capacities are checked with logistics staff |
| Constraint review | Real constraints such as road closures and customs are confirmed with operators |
Optimal solutions can be fragile when assumptions are wrong. Objectives may omit values that are hard to quantify.
07Methods and evidence
How operations research for disaster logistics is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Uncertainty bands
How far the outlook for operations research for disaster logistics could spread, shown as ranges rather than a single line.
- Central estimate
- median of the ensemble
- Ranges
- likely and very likely, in IPCC calibrated language
- Ensemble
- members, scenarios and initial conditions stated
- Read with
- the sensitivity of the result to each input
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
08Connections
Topics that rely on this method.
09Stewardship
Who maintains the method.
Systems Science and Modelling stewards optimisation and operations research for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of optimisation and operations research in this field?
Optimal solutions can be fragile when assumptions are wrong. Objectives may omit values that are hard to quantify.
How are results checked?
Solutions are checked for feasibility, tested through simulation and compared with historical decisions. Sensitivity analysis examines how results change with parameters.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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Disaster Resilience and Recovery
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