Xiong Huang
ABSTRACT
In order to solve the school bus route planning problem of primary and secondary schools in major cities in China, to ensure the safety of student transportation and to optimize the transportation mode, the improved simulated annealing algorithm (ISA) method is used to study the school bus route planning problem of a secondary school in a certain place, taking into account the time constraints of each station, the constraints on the number of passengers and the constraints on the number of school vehicles. The results are compared with the traditional simulated annealing algorithm (SA), which is based on the ISA construction framework and incorporates the tempering operation. The results show that the ISA algorithm reduces the total distance of distribution by 8935 m; the number of distribution school vehicles is reduced by 1; and the vehicle loading rate is better than the SA algorithm. It can be seen that the model perturbation strategy of global search of ISA algorithm improves the solution accuracy and locks the optimal solution; the tempering operation for constraints such as the number of vehicles, school bus load and time window further improves the solution quality of ISA algorithm; the construction of ISA algorithm framework facilitates the study of the deployment of functional modules to obtain the optimal solution of school bus path planning problem for programming.
- ZHANG J, LI Y. School Bus Problem and its Algorithm[J]. IERI Procedia, 2012,2:8-11.Google Scholar
Cross Ref
- J D. a multi-period school bus routing and scheduling systems[M]. Elsevier Science Publishers, 1986.Google Scholar
- THANGIAH S R, OSMAN I H, VINAYAGAMOORTHY R, Algorithms for the Vehicle Routing Problems with Time Deadlines[J]. American journal of mathematical and management sciences,1993,13(3-4):323-355.Google Scholar
- SPADA M, BIERLAIRE M, LIEBLING T M. Decision-Aiding Methodology for the School Bus Routing and Scheduling Problem[J]. Transportation science, 2005,39(4):477-490.Google ScholarDigital Library
- Li Jingwen, Li Xu, Jang Jianwu, Application of Improved Simulated Annealing Algorithm in Tourism Route Customization[J]. Science Technology and Engineering, 2020,20(26):10808-10814.Google Scholar
- WEI L, ZHANG Z, ZHANG D, A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints[J]. European journal of operational research, 2018,265(3):843-859.Google Scholar
- Lu Yuting, Lin Yuyou, Peng Qiaozi, A Review of Improvement and Research on Parameters of Simulated Annealing Algorithm[J]. College Mathematics, 2015,31(6):96-103.Google Scholar
- Shi Quansheng, Wang Zixuan, Ren Hongbo, Economical Operation of Regional Integrated Energy System Based on Improved Simulated Annealing-particle Swarm Optimization[J]. Science Technology and Engineering, 2020, 20(26):10801-10807.Google Scholar
- Chen Xiaopan, Dang Lanxue, Kong Yunfeng. A metaheuristic algorithm for solving large-scale school bus scheduling problem[J]. Journal of Geoinformation Science, 2013,15(06):879-886.Google Scholar
- Shang Zhengyang, Gu Jinan, Wang Jianping. Improved simulated annealing algorithm for solving vehicle path optimization problems with capacity constraints[J]. Computer Integrated Manufacturing Systems, 2020:1-16.Google Scholar
- Ding Changyong. Study on the optimization of cooperative school bus paths[D]. Dalian Maritime University, 2012.Google Scholar
- Wu Y, Liu Y, Ahmed S H, Dominant data set selection algorithms for electricity consumption time-series data analysis based on affine transformation[J]. IEEE Internet of Things Journal, 2019, 7(5): 4347-4360.Google ScholarCross Ref
- Liu Y, James J Q, Kang J, Privacy-preserving traffic flow prediction: A federated learning approach[J]. IEEE Internet of Things Journal, 2020, 7(8): 7751-7763.Google ScholarCross Ref
Recommendations
Global path planning for mobile robot based genetic algorithm and modified simulated annealing algorithm
GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary ComputationGlobal path planning for mobile robot using genetic algorithm and simulated annealing algorithm is investigated in this paper. In view of the slow convergence speed of the conventional simulated annealing algorithm, a modified simulated annealing ...
Path Planning for Mobile Robot Based on Improved Simulated Annealing Artificial Neural Network
ICNC '07: Proceedings of the Third International Conference on Natural Computation - Volume 03A mobile robot path planning method based on improved simulated annealing algorithm and artificial neural network is proposed. First the simulated annealing algorithm with the best reserve mechanism is introduced and it is combined with Powell algorithm ...
An Effective Hybrid Genetic Simulated Annealing Algorithm for Process Planning Problem
ICNC '09: Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 05Process planning is an essential part for a Computer Aided Process Planning (CAPP) system in the dynamic workshop environment. It is a combinatorial optimization problem to conduct operations selection and operations sequencing simultaneously with ...
Comments