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This project presents an approach to solving the Vehicle Routing Problem (VRP) using various metaheuristics. It explores the creation of efficient delivery routes to serve customers while respecting specific constraints, implementing algorithms such as Tabu Search, Simulated Annealing, and Gradient Descent.

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IssamSisbane/discrete-optimization-vrp

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DISCRETE OPTIMISATION PROJECT

This project presents an approach to solving the Vehicle Routing Problem (VRP) using various metaheuristics. It explores the creation of efficient delivery routes to serve customers while respecting specific constraints, implementing algorithms such as Tabu Search, Simulated Annealing, and Gradient Descent.

LINKS :

USAGE :

To launch the project, you need to run the FileChooserWindow file and follow the instructions on the window that appears.

ANALYSE RESULTS :

The analysis results are available in the analysis folder:

  • Recuit_Params.xlsx: contains the most used parameters for simulated annealing
  • Recuit_Params_analysis.xlsx: contains the results of the analysis of the parameters for simulated annealing
  • Tabou_Params.xlsx: contains the most used parameters for tabu search
  • Tabou_Params_analysis.xlsx: contains the results of the analysis of the parameters for tabu search
  • Tabou_Recuit_Params.xlsx: contains the most used parameters for both simulated annealing and tabu search
  • Comparaison_Tabou_Recuit.xlsx: contains the results of the comparison between simulated annealing and tabu search.

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This project presents an approach to solving the Vehicle Routing Problem (VRP) using various metaheuristics. It explores the creation of efficient delivery routes to serve customers while respecting specific constraints, implementing algorithms such as Tabu Search, Simulated Annealing, and Gradient Descent.

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