Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
GVR: A New Genetic Representation for the Vehicle Routing Problem
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
On the influence of GVR in vehicle routing
Proceedings of the 2003 ACM symposium on Applied computing
A genetic algorithm for unmanned aerial vehicle routing
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Computers and Operations Research
A Tabu Search Algorithm for the Split Delivery Vehicle Routing Problem
Transportation Science
Ant colony optimization for real-world vehicle routing problems
ACM SIGEVOlution
Multi-strategy grouping genetic algorithm for the pickup and delivery problem with time windows
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Parallel simulated annealing for the vehicle routing problem with time windows
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
An intelligent route management system for electric vehicle charging
Integrated Computer-Aided Engineering
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In this paper, we focus on a new transport system called on-demand bus system which is introduced on a trial basis to local cities in Japan. In the system, share-ride buses transport customers door-to-door according to user's requests. A user can specify the position and time to get the bus in the service area, thus the on-demand bus is more flexible and profitable system compared to traditional transport systems (i.e., fixed route bus). Electrical vehicles are also attracting attention as a new transportation device in these years. The electric vehicles are environmentally friendly because they produce zero emissions and do not pollute the air. However, there is some issues to be solved for practical use of electrical vehicles, i.e., the price of charger and the mileage per charge. Therefore, we adopt an evolutionary approach to solve a path optimization problem for the on-demand bus with electrical vehicles. It is very important to reduce the amount of recharge time for effective operation of electrical vehicles. An evolutionary algorithm minimizes the traveling distance of vehicles by a mutation operation (i.e., the exchange of sub-routes of vehicles) in order to reduce the amount of recharge time. We will show some comparison experiments by computer simulation, and show the performance of our algorithm for the on-demand bus with electrical vehicles.