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The Biobjective Bike-Sharing Rebalancing Problem with Balance Intervals: A Multistart Multiobjective Particle Swarm Optimization Algorithm
Complexity ( IF 2.3 ) Pub Date : 2020-09-17 , DOI: 10.1155/2020/2845426
Yongji Jia 1 , Yuanyuan Xu 1 , Dong Yang 1 , Jia Li 1
Affiliation  

The bike-sharing system (BSS), as a sustainable way to deal with the “last mile” problem of mass transit systems, is increasingly popular in recent years. Despite its success, the BSS tends to suffer from the mismatch of bike supply and user demand. BSS operators have to transfer bikes from surplus stations to deficit stations to redistribute them among stations by means of trucks. In this paper, we deal with the bike-sharing rebalancing problem with balance intervals (BRP-BIs), which is a variant of the static bike-sharing rebalancing problem. In this problem, the equilibrium of station is characterized by a balance interval instead of a balance point in the literature. We formulate the BRP-BI as a biobjective mixed-integer programming model with the aim of determining both the minimum cost route for a single capacitated vehicle and the maximum average rebalance utility, an index for the balanced degree of station. Then, a multistart multiobjective particle swarm optimization (MS-MOPSO) algorithm is proposed to solve the model such that the Pareto optimal solutions can be derived. The proposed algorithm is extended with crossover operator and variable neighbourhood search to enhance its exploratory capability. Compared with Hybrid NSGA-II and MOPSO, the computational experimental results demonstrate that our MS-MOPSO can obtain Pareto optimal solutions with higher quality.

中文翻译:

具有平衡间隔的双目标自行车共享再平衡问题:一种多起点多目标粒子群优化算法

自行车共享系统(BSS)作为一种解决公共交通系统“最后一英里”问题的可持续方式,近年来越来越受欢迎。尽管取得了成功,但BSS往往会遭受自行车供应和用户需求不匹配的困扰。BSS操作员必须将自行车从盈余站点转移到赤字站点,以通过卡车在站点之间重新分配。在本文中,我们用平衡间隔(BRP-BI)处理自行车共享再平衡问题,它是静态自行车共享再平衡问题的一种变体。在这个问题中,车站的平衡以平衡间隔而不是文献中的平衡点为特征。我们将BRP-BI公式化为双目标混合整数规划模型,其目的是确定单一容量车辆的最小成本路线和最大平均再平衡效用(站台平衡程度的指标)。然后,提出了一种多起点多目标粒子群算法(MS-MOPSO)对模型进行求解,从而可以推导帕累托最优解。该算法通过交叉算子和变量邻域搜索进行扩展,以增强其探索能力。与混合NSGA-II和MOPSO相比,计算实验结果表明,我们的MS-MOPSO能够获得更高质量的Pareto最优解。提出了一种多起点多目标粒子群算法(MS-MOPSO)对模型进行求解,从而可以推导帕累托最优解。该算法通过交叉算子和变量邻域搜索进行扩展,以增强其探索能力。与混合NSGA-II和MOPSO相比,计算实验结果表明,我们的MS-MOPSO能够获得更高质量的Pareto最优解。提出了一种多起点多目标粒子群算法(MS-MOPSO)对模型进行求解,从而可以推导帕累托最优解。该算法通过交叉算子和变量邻域搜索进行扩展,以增强其探索能力。与混合NSGA-II和MOPSO相比,计算实验结果表明,我们的MS-MOPSO能够获得更高质量的Pareto最优解。
更新日期:2020-09-18
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