Open Access

ARTICLE

Demand-Responsive Transportation Vehicle Routing Optimization Based on Two-Stage Method

Jingfa Ma, Hu Liu^*, Lingxiao Chen

School of Railway Transportation, Shanghai Institute of Technology, Shanghai, 201418, China

* Corresponding Author: Hu Liu. Email:

(This article belongs to the Special Issue: The Latest Deep Learning Architectures for Artificial Intelligence Applications)

Computers, Materials & Continua 2024, 81(1), 443-469. https://doi.org/10.32604/cmc.2024.056209

Received 17 July 2024; Accepted 11 August 2024; Issue published 15 October 2024

Abstract

Demand-responsive transportation (DRT) is a flexible passenger service designed to enhance road efficiency, reduce peak-hour traffic, and boost passenger satisfaction. However, existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs. Consequently, there is a need to develop real-time DRT route optimization methods that integrate both initial and real-time requests. This paper presents a two-stage, multi-objective optimization model for DRT vehicle scheduling. The first stage involves an initial scheduling model aimed at minimizing vehicle configuration, and operational, and CO₂ emission costs while ensuring passenger satisfaction. The second stage develops a real-time scheduling model to minimize additional operational costs, penalties for time window violations, and costs due to rejected passengers, thereby addressing real-time demands. Additionally, an enhanced genetic algorithm based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed, incorporating multiple crossover points to accelerate convergence and improve solution efficiency. The proposed scheduling model is validated using a real network in Shanghai. Results indicate that real-time scheduling can serve more passengers, and improve vehicle utilization and occupancy rates, with only a minor increase in total operational costs. Compared to the traditional NSGA-II algorithm, the improved version enhances convergence speed by 31.7% and solution speed by 4.8%. The proposed model and algorithm offer both theoretical and practical guidance for real-world DRT scheduling.

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Keywords

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