Abstract
The developments of electric vehicle (EV) technology and mobile internet technology have made the EV-oriented ride-hailing service a trend in smart cities. In the service scenario, a high-quality order allocation approach is in great need to quickly process a series of customer request orders, so as to reduce total customer waiting time and transportation cost. To simulate real-world customer-EV allocation scenarios, in this paper, a dynamic EV dispatch (DEVD) model is established by considering multi-source data association from five sources, including customer, vehicle, charging, station, and service. To solve the proposed multi-source data associated DEVD model, a memory-based ant colony optimization (MACO) approach is developed. MACO maintains a memory archive to store the historically good solutions, which not only can be used to update pheromone to guide the search, but also can be used to help the reactions to environmental changes. In response to dynamic changes, a partial reassignment strategy is also proposed to re-optimize some of the assigned customer-EV pairs in the historically best solution. Moreover, an exchange or replace local search procedure is designed to enhance the performance. The MACO algorithm is applied to a set of dynamic test cases with different customer request and EV sizes. Experimental results show that MACO generally outperforms the first-come-first-served approach and some state-of-the-art ACO-based dynamic optimization algorithms.
Highlights
W ITH the development of mobile internet technology, online car hailing services (e.g., Didi and Uber) have become popular in people’s travel in smart cities [1]
When a change occurs in dynamic EV dispatch (DEVD) problem, P-ant colony optimization (ACO) repairs the solutions in the population list by releasing the cancelled customer-electric vehicle (EV) pairs and re-assigning EVs to new coming customer requests and re-optimizes all currently valid customer requests
To simulate real-world dynamic EV dispatch application scenarios, a dynamic EV dispatch model is established by considering multi-source data association from five sources, including customer, vehicle, charging, station, and service
Summary
W ITH the development of mobile internet technology, online car hailing services (e.g., Didi and Uber) have become popular in people’s travel in smart cities [1]. Didi Chuxing, China’s largest online ride-hailing platform, launched the first customized car, an EV called D1, in 2020 through cooperation with the BYD company [4]. A recent study based on Uber shows that the use of EVs in online ride-hailing services is greatly beneficial for reducing emissions and has no statistical difference for services when compared with using fuel vehicles [5]. Nowadays EVs have gradually become an important part of the online ride-hailing services. The significance of online ride-hailing services and the universality of EVs raise the urgent need for research into EVs operations. Eng., vol 7, no. 3, pp. 607–616, Jul. 2010
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have