Journal of Geo-information Science >
Optimization of Rural Primary and Secondary School Location based on Traffic Network
Received date: 2019-08-13
Request revised date: 2019-12-07
Online published: 2020-07-25
Supported by
National Natural Science Foundation of China(41671396)
Copyright
The rational planning of school location is an important way to optimize the allocation of educational resources, improve the efficiency of school running and realize the balanced development of education. The accessibility must be considered in optimizing the location of rural schools, which is an important difference from the location of urban schools. Many scholars have studied the location problem of rural schools, but most of them have neglected the impact of transportation network conditions on the location of schools. On the basis of previous studies, this study will consider the impact of traffic network on the optimization location of primary schools in mountainous environment. The object of this study is to minimize the total transportation costs for students, construction costs for new schools, construction and upgrade costs for roads on a traffic network. We regard a set of villages as demand nodes exists in a geographical region, a set of roads as transfer links. The road links in the network contain existing and new potential road links. A set of schools exists in the region and it is clearly desired to locate a set of new schools, to construct new road links, to improve the existing road links such that the total investment costs (including the travel costs for students, construction costs for school facilities, construction and upgrading costs for roads) are minimized. Thus, a multi-objective optimization model for this problem is proposed. The multi-objective facility location-network design model constructed in this paper is an extension of FLNDP problem, and also belongs to NP-hard problem. In order to facilitate the model application, this study use geographic information science method, take Visual Studio. Net 2010 as the development platform, use C# language, MATLAB language and ArcGIS Engine 10 component library to develop a location optimization system of rural primary and secondary school based on transportation network. Finally, the system is applied to the optimization of the primary school location of a town in Guizhou province. Aiming at the location optimization problem of rural primary, this paper considers three scenarios (only consider the existing road, assume that the road can be upgraded and the road can be built or upgraded) which are different from the problem of urban primary school location. In geographic calculation, this paper explores an improved multi-objective simulated annealing algorithm to determine the best location of new schools, as well as the construction of new roads and the upgrading of existing roads.
CHEN Yulong , LAI Zhizhu , WANG Zheng . Optimization of Rural Primary and Secondary School Location based on Traffic Network[J]. Journal of Geo-information Science, 2020 , 22(5) : 1120 -1132 . DOI: 10.12082/dqxxkx.2020.190443
表1 3种算法性能指标值对比Tab. 1 Comparisons of performance indicators of three algorithms |
算法 | NN | MGD | SP | 平均时间/s | |||||
---|---|---|---|---|---|---|---|---|---|
平均值/个 | 总和/个 | 平均值 | 标准方差 | 平均值 | 标准方差 | ||||
MOSA | 7 | 216 | 537.41 | 246.30 | 924.29 | 639.11 | 84.77 | ||
NSGA-II | 15 | 456 | 276.30 | 106.20 | 726.30 | 315.65 | 68.62 | ||
IMOSA | 22 | 647 | 130.60 | 40.12 | 525.14 | 170.04 | 59.53 |
表2 情景1 Pareto最优解对应的方案及目标值Tab. 2 Scheme and objective function value of Pareto optimal solution in scenario 1 |
方案编号 | 新建设施个数 | 设施选址位置 | 旅行成本(第一目标值) | 设施建设成本(第二目标值) |
---|---|---|---|---|
1 | 1 | 1 642 335 | 400 000 | |
2 | 15 | 1 488 424 | 450 000 | |
3 | 1,22 | 1 495 064 | 800 000 | |
4 | 1,15 | 1 331 460 | 850 000 | |
5 | 15,21 | 1 315 524 | 900 000 | |
6 | 15,17 | 1 303 802 | 1 000 000 | |
7 | 15,17,21 | 1 130 902 | 1 450 000 | |
8 | 1,15,21 | 1 158 560 | 1 300 000 | |
9 | 1,11,22 | 1 379 636 | 1 200 000 | |
10 | 1,11,15 | 1 216 032 | 1 250 000 | |
11 | 9,15,17 | 1 121 438 | 1 550 000 | |
12 | 1,15,17 | 1 146 838 | 1 400 000 |
表3 情景2 Pareto最优解对应的方案及目标值Tab. 3 Scheme and objective function value of Pareto optimal solution in scenario 2 |
方案编号 | 新建设施个数 | 设施选址位置 | 旅行成本(第一目标值) | 建设成本(第二目标值) |
---|---|---|---|---|
1 | 15 | 1 295 687 | 559 000 | |
2 | 15 | 1 132 810 | 787 155 | |
3 | 15 | 1 068 135 | 890 765 | |
4 | 15 | 1 002 049 | 991 840 | |
5 | 15 | 864 630 | 1 271 470 | |
6 | 15 | 807 618 | 1 431 305 | |
7 | 15,17 | 1 000 392 | 1 267 605 | |
8 | 15,17 | 855 648 | 1 466 570 | |
9 | 15,17 | 742 158 | 1 687 960 | |
10 | 15,17 | 713 800 | 1 736 255 | |
11 | 15,17 | 655 768 | 1 865 215 | |
12 | 15,17 | 622 996 | 1 981 305 | |
13 | 15,17 | 610 746 | 2 078 610 | |
14 | 15,17 | 593 241 | 2 195 025 | |
15 | 15,17 | 553 786 | 2 313 000 | |
16 | 1,15,17 | 857 898 | 1 712 325 | |
17 | 1,15,17 | 750 337 | 1 874 305 | |
18 | 1,15,17 | 692 305 | 2 003 265 | |
19 | 1,15,17 | 580 291 | 2 118 835 | |
20 | 1,15,17 | 548 536 | 2 199 760 | |
21 | 1,15,17 | 536 285 | 2 297 065 | |
22 | 1,15,17 | 508 400 | 2 458 200 |
表4 情景3 Pareto最优解对应的方案及目标值Tab. 4 Scheme and objective function value of Pareto optimal solution in scenario 3 |
方案编号 | 新建设施个数 | 设施选址位置 | 旅行成本(第一目标值) | 建设成本(第二目标值) |
---|---|---|---|---|
1 | 15 | 969 011 | 1 049 911 | |
2 | 15 | 909 984 | 1 176 466 | |
3 | 15 | 797 970 | 1 292 036 | |
4 | 15 | 764 372 | 1 420 996 | |
5 | 15 | 752 155 | 1 479 340 | |
6 | 15 | 720 408 | 1 560 265 | |
7 | 15 | 689 716 | 1 781 031 | |
8 | 15,17 | 861 953 | 1 458 445 | |
9 | 15,17 | 805 014 | 1 580 320 | |
10 | 15,17 | 717 808 | 1 753 675 | |
11 | 15,17 | 650 227 | 1 782 035 | |
12 | 15,17 | 567 661 | 1 932 035 | |
13 | 15,17 | 549 277 | 2 012 960 | |
14 | 15,17 | 521 388 | 2 174 095 | |
15 | 15,17 | 506 368 | 2 292 070 | |
16 | 15,17,21 | 897 560 | 1 859 931 | |
17 | 15,17,21 | 736 467 | 2 098 156 | |
18 | 15,17,21 | 646 687 | 2 308 041 | |
19 | 15,17,21 | 522 456 | 2 481 955 | |
20 | 15,17,21 | 515 037 | 2 549 215 | |
21 | 15,17,21 | 400 220 | 2 724 416 | |
22 | 15,17,21 | 372 331 | 2 885 551 |
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