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A linearly decreasing deterministic annealing algorithm for the multi-vehicle dial-a-ride problem.

Amir Mortazavi¹, Milad Ghasri¹, Tapabrata Ray¹

¹School of Engineering and Technology, UNSW Canberra, Canberra, ACT, Australia.

|February 8, 2024

Summary

This summary is machine-generated.

This study introduces an enhanced deterministic annealing algorithm for solving large-scale dial-a-ride problems (DARPs). The new method efficiently finds optimal solutions, outperforming existing algorithms in speed and solution quality for transportation logistics.

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Area of Science:

Operations Research
Transportation Science
Computer Science

Background:

The dial-a-ride problem (DARP) is a complex variant of pick-up and delivery problems with significant real-world transportation applications.
Solving large-scale multi-vehicle DARPs efficiently remains a challenge in logistics and operations research.

Purpose of the Study:

To propose and evaluate an enhanced deterministic annealing algorithm for large-scale multi-vehicle DARPs.
To demonstrate the algorithm's ability to guarantee feasible solutions and accelerate the search for optimal outcomes.

Main Methods:

Development of an enhanced deterministic annealing algorithm incorporating advanced local search operators.
Implementation of a linearly decreasing deterministic annealing schedule to manage search space exploration.
Systematic experimental comparison against state-of-the-art methods like ALNS, ELS, and DA on standard benchmarks.

Main Results:

The proposed algorithm consistently explores the feasible search space, ensuring a valid solution at any termination point.
Experimental results indicate the enhanced algorithm achieves competitive objective values.
The method demonstrates superior average speed compared to leading algorithms like ALNS, ELS, and DA across various benchmarks.

Conclusions:

The enhanced deterministic annealing algorithm offers a robust and efficient approach for solving large-scale multi-vehicle DARPs.
This method provides a valuable tool for improving transportation logistics and operational efficiency.