Optimizing Organic Waste Diversion Using a Vehicle Routing Model

Table of Contents

  1. Optimizing Organic Waste Diversion Using a Vehicle Routing Model
  2. Setting up Your Workspace
  3. Preparing the Data
  4. Skill Drill: Clip the Roads Layer to the City of Eureka
  5. Skill Drill: Add a Time Cost Attribute to the Roads Layer
  6. Skill Drill: Adding XY Data
  7. Skill Drill: Geocoding an Address  and Creating a CSV Table to Import As XY Data
  8. Creating A Network Dataset
  9. Setting up a Vehicle Routing Problem (VRP)
  10. Loading Orders into the Model
  11. Loading the Depot into the Model
  12. Adding Route Parameters into the Model
  13. Adding a Route Renewal into the Model
  14. Adjusting the Analysis Setting of Model
  15. Running the Vehicle Routing Model
  16. Skill Drill: Adding a Second Garbage Truck to the Model
  17. Skill Drill: Adjusting the Model to include Rear-Loading Trucks
  18. Skill Drill: Creating a Map of the Results

Adjusting the Analysis Setting of Model

The vehicle routing problem also has global properties that must be set. So far you have been working with attributes for individual model parameters such as orders, routes, and depots. Here you will adjust the analysis settings of the entire vehicle routing problem. These settings will influence all of the model parameters. Click on the Vehicle Routing Problem Properties button in the Network Analyst Window. 

An image of the the Vehicle Routing Problem Properties button in the Network Analyst Window

Navigate to the Analysis Settings tab. Verify that the Time Attribute is set to Minutes (Minutes). Set the Distance Attribute to Length (Meters). Verify that the Distance Field Units are set to Miles. This setting determines the units for other model attributes such as cost per unit distance, which in this scenario is dollars per mile. Set the U-Turns at Junctions to Allowed Only at Dead Ends. Leave all other settings as default and click OK.

An image of the Vehicle Routing Problem Layer Properties Window
The Analysis Settings control the global settings across many of the model parameters.