Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem

Area of Science:

• Operations Research
• Logistics and Supply Chain Management
• Computational Optimization

Background:

• The Vehicle Routing Problem (VRP) is a critical challenge in optimizing modern logistics systems.
• Existing VRP models often require efficient methods to handle constraints like hard time windows.
• The need for robust algorithms that balance solution speed and quality is paramount in logistics optimization.

Purpose of the Study:

• To develop a modified Vehicle Routing Problem (VRP) model incorporating hard time window constraints.
• To introduce a novel Hybrid Optimization Algorithm (HOA) for solving the enhanced VRP.
• To demonstrate the effectiveness and efficiency of the proposed HOA through experimental validation.

Main Methods:

• Development of a modified VRP model specifically addressing hard time window requirements.
• Introduction of a Hybrid Optimization Algorithm (HOA) integrating Fractal Space Filling Curves (SFC) for initial solution generation and Genetic Algorithms (GA) for refinement.
• Implementation of experimental software and extensive testing using Solomon's benchmark dataset for VRP.

Main Results:

• The SFC method within the HOA provides rapid generation of initial feasible solutions.
• The GA component effectively improves upon the initial solutions, enhancing overall solution quality.
• Experimental results confirm the feasibility and high effectiveness of the proposed HOA for the hard time window VRP.

Conclusions:

• The Hybrid Optimization Algorithm (HOA) offers a significant advancement in solving the Vehicle Routing Problem with hard time windows.
• The synergistic combination of SFC and GA provides a powerful approach for efficient and effective logistics optimization.
• The developed methodology is validated and recommended for practical applications in logistics and transportation management.