https://github.com/marcobendinelli/optimized-store-installation-and-truck-routing

Optimization of store installation and truck routing for cost efficiency. Includes implementations for the Generalized Assignment Problem and Vehicle Routing Problem, with detailed solutions and interactive visualization
https://github.com/marcobendinelli/optimized-store-installation-and-truck-routing

ampl generalized-assignment-problem jupyter-notebook vehicle-routing-problem

Last synced: 13 days ago
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Optimization of store installation and truck routing for cost efficiency. Includes implementations for the Generalized Assignment Problem and Vehicle Routing Problem, with detailed solutions and interactive visualization

• Host: GitHub
• URL: https://github.com/marcobendinelli/optimized-store-installation-and-truck-routing
• Owner: MarcoBendinelli
• Created: 2024-04-21T10:21:06.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-04-25T15:15:02.000Z (7 months ago)
• Last Synced: 2024-04-25T17:33:59.939Z (7 months ago)
• Topics: ampl, generalized-assignment-problem, jupyter-notebook, vehicle-routing-problem
• Language: Jupyter Notebook
• Homepage:
• Size: 3.07 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# StoreNet :convenience_store: :truck:

StoreNet is a program that addresses two key challenges in retail logistics: the [*Generalized Assignment Problem*](https://en.wikipedia.org/wiki/Generalized_assignment_problem) (GAP), which determines optimal store locations to minimize building costs, and the [*Vehicle Routing Problem*](https://en.wikipedia.org/wiki/Vehicle_routing_problem) (VRP), which optimizes truck routes for store restocking to minimize driving costs.

## Learn More

Find the **full problem description** [here](/StoreNet_Problem.pdf).

Explore the **solution explanation** [here](/deliverables/report.pdf). In solving the GAP, linear programming techniques using **AMPL** were employed. For the VRP, a heuristic method was utilized to achieve polynomial execution time.

An **interactive Python notebook** is available [here](/StoreNet_Notebook.ipynb), offering detailed insights into the solution process.

## Plot Example