A new hybrid evolutionary search for transport on-demand optimization

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Abstract Today, there is a growing need for advancements that can enhance transport accessibility. As customers’ expectations rise, on-demand transport services must cater to higher requirements. Consequently, this study addresses a specific problem belonging to the class of Dial-A-Ride Problems, which incorporates constraints focused on meeting customers’ needs. To address this problem, we propose a novel hybrid evolutionary search method. This method combines the strengths of a Tabu Search approach with an evolutionary schema, resulting in a more effective solution. Additionally, we introduce two exchanged mutation operators and a crossover mechanism as part of the proposed method. These optimization techniques have been proven to support advanced designs and significantly reduce operational costs while improving service quality. A comparative analysis with an evolutionary local search method from the existing literature has demonstrated the effectiveness of our approach. With this approach, service providers can optimize their operations, reduce costs, and provide a higher level of service quality, ensuring satisfaction from both the service provider and customer perspectives.

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