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Cellular Genetic Algorithm for Solving a Routing On-Demand Transit Problem
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DOI：10.1145/2908812.2908921.png)
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Abstract
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To provide sustainable and efficient urban logistics and transportation services, urban mobility tools are facing challenges on reducing carbon emission, waiting time for passengers and transit time. The emergence of many new intelligent and electric transportation system offers many new possible solutions to achieve urban sustainability. This paper proposes to treat the Personal Rapid Transit System (PRT) as an efficient sustainable transportation tool for urban areas. This paper proposes to deal with static problem of routing PRT'vehicles to minimize total energy consumption while considering the battery capacity of vehicles. For that purpose, we describe a multiple crossover Cellular Genetic Algorithm combined with a local search. Numerical experiments on 1320 instances show that our hybrid algorithm is efficient in which the average percent deviations relative to the lower bound over 1320 instances is about 1.632%, and the average running time is about 26.3 seconds.
Keywords:
Personal Rapid Transit
Evolutionary Computation
Vehicle Routing Problem
Cellular Genetic Algorithm
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