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 points, vehicle service, and other constraints. Secondly, in order to further improve the coordination ability and search speed of the operator, this paper uses the particle swarm optimization algorithm to help the operator remember the previous search position and iterative information, and designs the PSO (Particle Swarm Optimization) improved NSGA-II (Non-dominated Sorting Genetic Algorithm) algorithm to solve the multi-objective model. Finally, the feasibility of the model is verified by numerical analysis of Xi'an–Xianyang intercity carpool. The results show that the path of vehicle 1 is 5-8-O-D-16-13, the path of vehicle 2 is 7-3-6-O-D-15-11-14, and the path of vehicle 3 is 2-1-4-O-D-12-10-9. Compared with NSGA-II algorithm, the PSO-NSGA-II algorithm designed in this paper has significant advantages in global search ability and convergence speed.

Optimization of Cold Chain Path forS company of C city based on Dijkstra's Algorithm

“…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.…”

Power Flow Optimization Strategy of Distribution Network with Source and Load Storage Considering Period Clustering

“…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]:…”