cnpryer/logistics

Logistics engineering tool built with rust.

logistics

Rust Logistics Engineering Client

About

Development project to build CLI Application bundled with filesystem processing and server clients. logistics builds and manages projects for logistics engineering models.

• project_name
  • .logistics
  • demand
    • file1
    • file2

.logistics

origin [OPTIONAL]:
  - "origin_city"
  - "origin_state"
  - "origin_zip"
origin [OPTIONAL]:
  - origin_city: "city"
  - origin_state: "state"
  - origin_zip: "zip code"
destination [REQUIRED]:
  - "dest_city"
  - "dest_state"
  - "dest_zip"
capacity [OPTIONAL]:
  - "pallets"
  - weight: "lbs"

CLI: logistics demand/file1 --vrp

Server: logistics run server --port=9999

Shipment Routing

The first target of the client is to provide an efficient method for generating optimized VRP solutions from csv data.

Scope

Models will be limited to single-origin, one-dimension capacities and demands, and optimized only using the inital distance and capacity constraints.

Constraints

• Maximum Vehicle Capacity: The total number of units allowed in a vehicle for a given route.

• Maximum Route Distance: The maximum cummulative travel distance for a route.

Inputs

• Constraint Setup
• Depot Latitude and Longitude
• Demand Latitudes and Logitudes
• Demand Units

logistics's Issues

haversine method

Create function for calculating haversine distances between geocode pairs.

optimization for maximizing capacity

integer constraint optimization for maximizing the total capacity utilization per route (per vehicle).

optimization for minimize distance

integer constraint optimization for minimizing the cumulative distance per route.

code performance optimizations

go back and tweak the code to better perform (use as rust learning instrument)

data initialization

• Constraints
• General Configuration Parameters
• Depot origin lat and lon
• Demand lat and lon
• Geo Matrix
• Demand Units

initial solver module design

Initial module required to run optimization for two constraints:

1. Distance
2. Capacity

Keep in mind of the following future requirements:

• Time Windows and Travel Time
• 2-Dimension Capacities
• Soft Constraints (think incentivizing better vehicle utilization such as fulfilling less demand to retain better distance minimization)

getting started Shipment Routing project docs

I'll create some docs to explain the target of this project and its context within the bigger picture for the logistics library.

first refactor

• Set up initialized data as abstracted, reusable test-case (module?).
• Abstract geocode_matrix logic from create_distance_matrix into its own function.
• Abstract tests and test-data into separate file