Enhancing Service Quality of On-Demand Transportation Systems Using a Hybrid Approach with Customized Heuristics

• Published in: Smart cities (Basel) Vol. 7; no. 4; pp. 1551 - 1575
• Main Authors: Nasri, Sonia, Bouziri, Hend, Mtalaa, Wassila
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2024
• Subjects: Automobile customizing; Cost analysis; Cost control; Customer satisfaction; Customer services; Customers; Customization; Design; dial-a-ride problem; Efficiency; evolutionary algorithm; Evolutionary computation; Genetic algorithms; Heuristic; Literature reviews; Methods; Operating costs; optimization method; Optimization techniques; Passengers; Quality of service; Search methods; Smart cities; smart transportation; Tabu search; Transportation systems; User satisfaction
• ISSN: 2624-6511; 2624-6511
• DOI: 10.3390/smartcities7040063

As customers’ expectations continue to rise, advanced on-demand transport services face the challenge of meeting new requirements. This study addresses a specific transportation issue belonging to dial-a-ride problems, including constraints aimed at fulfilling customer needs. In order to provide more efficient on-demand transportation solutions, we propose a new hybrid evolutionary computation method. This method combines customized heuristics including two exchanged mutation operators, a crossover, and a tabu search. These optimization techniques have been empirically proven to support advanced designs and reduce operational costs, while significantly enhancing service quality. A comparative analysis with an evolutionary local search method from the literature has demonstrated the effectiveness of our approach across small-to-large-scale problems. The main results show that service providers can optimize their scheduling operations, reduce travel costs, and ensure a high level of service quality from the customer’s perspective.