CVRP_heuristics

An algorithm practice repository for multiple variants of Capacitated Vehicle Routing Problem (CVRP) problems. This repository uses instances from the CVRPLIB: CVRPLIB

Overview

This repository is dedicated to exploring and developing various heuristics and algorithms for solving CVRP problems. The CVRP is a well-known NP-hard problem in combinatorial optimization, where the objective is to determine the optimal set of routes for a fleet of vehicles to deliver goods to a given set of customers, subject to vehicle capacity constraints.

Potential Studies

1. Developing Construction Algorithms

   • Design and implement algorithms for initial feasible solutions.

2. Developing Improvement Heuristics and Efficient Local Search Algorithms

   • Implement and test local search techniques such as 2-opt, 3-opt, and Lin-Kernighan.

3. Developing Mathematical Models and Getting Solver Solutions and Performances

   • Formulate the CVRP using integer programming
   • Compare performance metrics such as runtime and solution quality.

4. Applying Metaheuristics on Solutions and Performance Comparison

   • Implement metaheuristics such as Genetic Algorithms, Ant Colony Optimization, and Particle Swarm Optimization.
   • Compare the performance of different metaheuristics on various instances.

5. Working with Multiple Objectives and Focusing on Finding All Efficient Solutions

   • Formulate and solve multi-objective CVRP (e.g., minimizing distance and balancing load).
   • Use Pareto optimization techniques to identify efficient solutions.

6. Applying Clustering Algorithms and Comparison

   • Apply clustering techniques to partition the customer set before routing (e.g., k-means, hierarchical clustering).
   • Compare the efficiency and effectiveness of different clustering methods.

7. Hybrid Algorithms

   • Combine different heuristics and metaheuristics to leverage their strengths.

8. Adaptive and Reactive Search Strategies

   • Develop algorithms that adapt their parameters or strategies based on the problem instance or search history.
   • Explore learning-based methods to guide the search process.

9. Benchmarking and Performance Analysis

   • Create a benchmarking framework to systematically evaluate and compare the performance of various algorithms.
   • Analyze the impact of different problem characteristics (e.g., customer density, demand variability) on algorithm performance.

10. Scalability and Parallelization

    • Investigate the scalability of different algorithms to larger instances.
    • Implement parallel and distributed computing techniques to enhance performance.