Food Bank Route Optimization Study

ISEF Category: Engineering Technology: Statics and Dynamics

Subcategory: Industrial Engineering-Processing  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

A food truck that takes the long way costs more fuel, more time, and more missed deliveries. The same thing happens in food-bank logistics, but the stakes are higher because every extra mile can affect how many people get served. You can test whether smarter routing saves mileage and cuts carbon emissions. That gives you a real engineering problem with real impact.

What Is It?

This project studies vehicle routing, the problem of finding the best set of delivery stops for one or more vehicles. Think of it like planning the smartest order to visit several classrooms before the bell rings. If you pick the wrong route, you waste time and fuel. If you pick a better route, you save both.

OR-Tools is a free optimization library from Google that can search for efficient routes. OSRM is a routing engine that turns map data into road distances and travel times. In your project, you compare the food bank’s current manual routes with routes produced by an algorithm. You then measure outcomes like total mileage, estimated CO2, number of stops per route, and route balance across vehicles.

Why This Is a Good Topic

This is a strong science fair topic because it has clear inputs, clear outputs, and real constraints. You can test how route quality changes when you change the number of vehicles, the stop order, the distance metric, or the delivery time limits. It also connects to a real-world problem, food insecurity, while teaching you optimization, map data, and impact analysis. A student can build a meaningful project without inventing a new algorithm from scratch.

Research Questions

• How does OR-Tools route optimization change total mileage compared with manual food-bank routes?
• What is the effect of adding vehicle capacity limits on route length and route balance?
• Does using travel time instead of road distance change the best route order?
• To what extent do optimized routes reduce estimated CO2 emissions across a delivery day?
• Which routing constraint, stop count, time windows, or vehicle count, has the largest effect on total mileage?
• How does route efficiency change when you group deliveries by neighborhood instead of serving stops in the current order?

Basic Materials

• Laptop or desktop computer with internet access.
• Free OR-Tools software.
• Free OSRM routing access or a local OSRM setup.
• Spreadsheet software such as Google Sheets or Excel.
• GPS coordinates or street addresses for delivery stops, with permission from the food bank.
• Map data source such as OpenStreetMap.
• Calculator or spreadsheet for mileage and CO2 calculations.

Advanced Materials

• Laptop or desktop computer with enough memory to run optimization tests.
• Python environment with OR-Tools, pandas, and numpy.
• Local OSRM server or another routing engine for repeatable distance queries.
• Geographic information system software such as QGIS for map checks.
• PubMed or NOAA emission factor sources for documenting CO2 assumptions.
• OpenStreetMap data extracts for the study region.
• Statistical analysis software or Python packages for comparing route sets.

Software & Tools

• OR-Tools: Solves vehicle routing problems and generates optimized stop sequences.
• OSRM: Converts map data into road distance and travel time estimates between stops.
• Python: Organizes location data, runs optimization, and calculates route metrics.
• QGIS: Checks whether routes and stop locations make sense on a map.
• Google Sheets: Tracks manual routes, optimized routes, and summary statistics.

Experiment Steps

1. Define the routing question you want to answer, such as mileage savings, CO2 reduction, or route balance.
2. Collect a clean set of delivery stops, vehicle limits, and the manual routes you will compare against.
3. Choose one distance metric, such as road mileage or travel time, and keep it consistent across all tests.
4. Build an optimization model that includes the real constraints you want to study, such as vehicle capacity or stop ordering.
5. Plan comparison metrics that convert route output into numbers you can defend, such as total distance, estimated emissions, and fairness across vehicles.
6. Design a sensitivity test that changes one routing assumption at a time, so you can see which factor drives the result.

Common Pitfalls

• Using incomplete or outdated food-bank address data, which makes the route model compare the wrong stops.
• Mixing straight-line distance with road distance, which can make the optimized route look better or worse than it really is.
• Ignoring vehicle capacity or stop-time limits, which can produce routes that work on paper but fail in practice.
• Comparing optimized routes to a manual route without matching the same day, service area, or delivery load.
• Estimating CO2 with a vague emission factor and no citation, which weakens the credibility of your results.

What Makes This Competitive

A stronger project goes past one before-and-after comparison. You can test several routing constraints, compare multiple neighborhoods, or measure how sensitive the answer is to small data changes. You can also defend your CO2 method and explain why one optimization choice matters more than another. That kind of analysis looks much closer to applied industrial engineering.

Project Variations

• Compare OR-Tools routes against a simple nearest-neighbor route heuristic instead of only the food bank’s current method.
• Test how route efficiency changes when you add delivery time windows for shelters, pantries, or community centers.
• Reframe the study around equity by measuring whether optimized routes reduce the longest drive for any single vehicle or neighborhood.

Learn More

• OR-Tools Documentation: Search the official Google OR-Tools docs for vehicle routing examples and constraint setup.
• OpenStreetMap Wiki: Learn how map data is structured and how routing engines use road networks.
• OSRM Project Pages: Read about routing tables, travel time estimates, and map preprocessing.
• US EPA Greenhouse Gas Emissions Factors Hub: Find emission factor guidance for estimating transportation CO2.
• NOAA Climate.gov: Search for background on carbon emissions and transportation-related climate impacts.
• MIT OpenCourseWare, Operations Research: Look for free lectures on optimization, linear programming, and routing problems.