Humanitarian supply and prepositioning
Network models choose warehouse locations, stock levels and routes that minimise delivery time and cost across a set of shock scenarios, with access constraints and road conditions included.
GCRI · Food Nexus
Optimisation for food supply chains
Optimisation and operations research help allocate scarce food, water, storage and transport under constraints. In food systems they design reserve networks, route relief, schedule irrigation and plan supply chains that keep working when a port, road or supplier fails, with the trade-offs between cost, speed and robustness stated.
01Register
Optimisation for food supply chains on the record.
Method recordSTEM method
- MethodOptimisation and operations research
- Applied inFood Nexus
- Data4 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
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 food and agriculture, trade, resources and supply chains 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.
Strategic reserve design
Stochastic programming sizes and places grain reserves against harvest and import scenarios, balancing storage cost, spoilage and the cost of a shortfall.
Irrigation scheduling and allocation
Optimisation allocates water between schemes and crops under supply, energy and environmental-flow constraints, stating the loss each user bears in a dry year.
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
Food · Optimisation for food supply · Run R-2497
Feasible, gap closingGCRI · Food Nexus
Convergence
Method card
- Method
- Optimisation and operations research
- Variables
- 67,403
- Optimality gap
- 1.1 pct
- Label
- MODELLED
Binding constraints
| Capacity | 0 | 27 | Binding |
| Budget | 0 | 1.0 | Binding |
| Service level | 4 | 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 |
|---|---|---|
| Logistics network and road condition data | Network topology and costs | REPORTED |
| Needs and demand estimates | Targets and constraints | ESTIMATED |
| Storage capacity and cost records | Facility parameters | Published statistics |
| Shock scenario sets | Supply uncertainty | MODELLED |
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
- 0.82% of a year’s output
- Total over direct (multiplier)
- 1.46 times
- Most affected other sector
- Food processing
- Its peak inoperability
- 1.6%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Farming | 33.87 | 23.7 |
| Fertiliser and inputs | 0.22 | 22.6 |
| Food processing | 1.56 | 11.3 |
| Transport | 0.11 | 12.2 |
| Wholesale and retail | 0.05 | 16.2 |
| Energy | 0.11 | 14.0 |
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 Food Nexus in Foundry
- Tested at Food Nexus in Universe
- Codified in Water and Food Systems in Standards
- Published in Food Nexus in Reports
- Taught in Faculty of Earth and Environmental Sciences 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 |
|---|---|
| Feasibility and simulation | Solutions are simulated under scenarios not used in the optimisation to test how they perform |
| Comparison with past decisions | Plans are compared with what operators actually did in past events |
| Sensitivity to assumptions | Costs, demand and capacity are varied to show how fragile a recommended plan is |
Optimal solutions can be fragile when assumptions are wrong. Objectives may omit values that are hard to quantify.
07Sector signature
The sector signature of optimisation for food supply chains.
The sectors optimisation for food supply chains touches and how strongly they depend on each other: what a study has to observe and model together.
Select corners · hover lines6 / 15 relations
Signature
Food and its partners
Food gives nutrition to health (0.7), land conversion to biodiversity (0.6) and withdrawals and runoff to water (0.3).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Food → Biodiversity · land conversion0.6
- Water → Food · irrigation0.8
- Water → Biodiversity · environmental flows0.6
- Food → Health · nutrition0.7
08Connections
Topics that rely on this method.
09Methods and evidence
How optimisation for food supply chains 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.
System model
The couplings between the systems in optimisation for food supply chains: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
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
10Stewardship
Who maintains the method.
Systems Science and Modelling stewards optimisation and operations research for the Consortium and reviews how it is applied across platforms.
11Connected
Where this page leads across the Consortium.
12Questions
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.
ReportsContribute
Contribute evidence from this page.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRIFood NexusGlobal Sector Risk and ResilienceCountry Systems Risk and ResilienceEvidence Brief
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
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Food Nexus
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