A. A. N. P. Redi, Anak Agung Ngurah Redioka
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引用次数: 3

摘要

公共自行车共享系统存在一个问题,即需要平衡停靠站的自行车数量,以确保系统用户满意度。通常的解决方案是分发自行车,这样系统用户仍然可以将自行车停在通常有很多自行车的地方,或者在通常没有自行车的地方取车。解决这一问题的目的是得到一个与车辆的总运行成本相符的车辆路线。整车运营成本与车辆分配自行车所花费的全部时间有关。此外,在公共共享单车设施运营时,还会有与缺乏自行车或停车位相关的罚款。在本研究中,开发了模拟退火(SA)算法的两种变体SA_BF和SA_CF来解决SBRP问题。使用的数据来自法国巴黎的Velib自行车共享系统案例研究。实验结果表明,SA_BF和SA_CF算法均能成功求解SBRP问题。在第一个数据集上,该算法与Arc-Indexed算法(AI)的平均差异为2.21%和0.36%。对于第二个数据集,禁忌搜索算法、SA_BF和SA_CF获得的最优结果平均差值分别为0.65%、1.08%和0.38%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Algoritma Simulated Annealing untuk Optimasi Rute Kendaraan dan Pemindahan Lokasi Sepeda pada Sistem Public Bike Sharing
The public bike-sharing system has a problem where the number of bicycles at the docking station needs to be balanced to ensure system user satisfaction. The usual solution is to distribute bicycles so that system users can still park for locations that are usually full of bicycles or pick up bicycles at locations that normally lack bicycles. The purpose of solving this problem is to get a vehicle route with the total operating costs of the vehicle. The full vehicle operating costs are associated with the full time taken by the vehicle to distribute the bicycle. Besides, there are also penalty fees related to the lack of bikes or parking slots at the time of operation of the public bike-sharing facility. In this study, two variations of the simulated annealing (SA) algorithm were developed to solve the SBRP problem called SA_BF and SA_CF. The data used comes from a Velib bike-sharing system case study in Paris, France. The results of the experiment show that both the SA_BF and SA_CF algorithms succeeded in solving SBRP. This algorithm has an average difference of 2.21% and 0.36% of the Arc-Indexed algorithm (AI) from previous studies in the first dataset. As for the second dataset, Tabu Search algorithm, SA_BF and SA_CF obtained an average difference of 0.65%, 1.08% and 0.38% of the optimal results.
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