Modified artificial bee colony for the vehicle routing problems with time windows.

Area of Science:

• Operations Research
• Artificial Intelligence
• Swarm Intelligence

Background:

• Honeybee swarm behavior inspires optimization algorithms.
• The artificial bee colony (ABC) algorithm is used for optimization problems.
• The vehicle routing problem with time windows (VRPTW) is a complex logistical challenge.

Purpose of the Study:

• To introduce a Modified Artificial Bee Colony (ABC) algorithm for the vehicle routing problem with time windows (VRPTW).
• To enhance the solution quality and convergence speed of the original ABC algorithm for VRPTW.

Main Methods:

• A Modified ABC algorithm is proposed, incorporating a memory mechanism for abandoned solutions.
• Scout bees in the Modified ABC use roulette wheel selection from a list of abandoned solutions.
• New solutions are generated by modifying routes from the best-found solution.

Main Results:

• The Modified ABC algorithm demonstrates superior performance compared to the original ABC for VRPTW.
• The proposed algorithm achieves competitive results against existing best-known solutions on Solomon benchmark datasets.
• Computational results validate the effectiveness of the Modified ABC algorithm.

Conclusions:

• The Modified ABC algorithm is a promising approach for solving the vehicle routing problem with time windows.
• The enhancements to the ABC algorithm improve solution quality and efficiency for VRPTW.
• This research contributes to the field of swarm intelligence applications in logistics optimization.