2023
DOI: 10.3390/su15032261
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Multi-Objective Intercity Carpooling Route Optimization Considering Carbon Emission

Abstract: In recent years, intercity carpooling has been vigorously developed in China. Considering the differences between intercity carpooling and intracity carpooling, this paper first defines the intercity carpooling path optimization problem with time window. Based on the balance of interests among passengers, platform, and government, a multi-objective function is constructed to minimize passenger cost, maximize platform revenue, and minimize carbon emission cost, with vehicle capacity, boarding and alighting poin… Show more

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Cited by 3 publications
(2 citation statements)
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“…Based on the balance of interests among passengers, platforms, and government, a multi-objective function is constructed with the objectives of minimizing passenger costs, maximizing platform revenue, and minimizing carbon emission costs, and constraints are imposed on the number of passengers carried, boarding and alighting points, and vehicle services. Secondly, in order to further improve the coordination ability and search speed of the operator, particle swarm optimization algorithm is used to help the operator remember the previous search position and iteration information, and PSO (particle swarm optimization) improved NSGA-II (nondominated sorting genetic algorithm [8] ) algorithm is designed to solve the multi-objective model.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Based on the balance of interests among passengers, platforms, and government, a multi-objective function is constructed with the objectives of minimizing passenger costs, maximizing platform revenue, and minimizing carbon emission costs, and constraints are imposed on the number of passengers carried, boarding and alighting points, and vehicle services. Secondly, in order to further improve the coordination ability and search speed of the operator, particle swarm optimization algorithm is used to help the operator remember the previous search position and iteration information, and PSO (particle swarm optimization) improved NSGA-II (nondominated sorting genetic algorithm [8] ) algorithm is designed to solve the multi-objective model.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The particle swarm optimization (PSO) is used to solve the model established in this paper. In PSO, multiple particles in the search space guide the update of speed and position according to the global optimization and individual optimization, and co-evolve to find the optimal solution of the problem [28]. The speed update equation is as follows [29]:…”
Section: Solution Of Mathematical Modelmentioning
confidence: 99%