考虑多仓库的共享单车重新配置问题研究

共享单车是我国大力提倡的低碳交通出行模式,加快共享单车发展是解决最后一公里、城市拥堵和环境污染等问题的重要途径。由于人们停放共享单车的无规律性,使得共享单车系统中各车桩的单车库存量存在不平衡。如何合理的对车桩中的单车进行重新调配,来满足用户的需求,是相关企业亟待解决的问题。共享单车的调配路线优化是优化车桩库存量的重要手段之一。本文研究多仓库条件下的货车调配路线优化问题,建立了一个混合整数非线性规划模型。不同于传统的路径优化问题的研究大多是以成本或时间为目标,本文采用基于车桩库存量的非线性惩罚函数来表示用户需求,从而使得所研究的问题是一个凸函数优化问题。为了简化本文的问题,将目标函数分段线性化。基于车桩网络的特点,设计了变邻域搜索算法,以及构建初始解的贪婪算法。最后,以某共享单车公司为例,进行算例分析,来说明模型和算法的合理性和有效性。     

基于K-means聚类算法与重心法的故障共享单车回收中心选址优化

共享单车已日渐成为人们短途出行的重要交通工具，但共享单车市场一贯采用“重投放，轻维护”的发展模式，而共享单车使用中的正常损耗及人为破坏等造成的故障车数量却十分庞大，对其回收修复或报废的任务繁重，这也成了共享单车市场逆向物流亟待解决的难题。该文立足武汉共享单车市场，通过对故障共享单车报废点的聚类分析，基于运输成本导向，使用重心法探寻回收中心最佳选址点，以解决故障共享单车回收成本和效率问题。对模型的模拟验证表明，优化后的回收中心选址点不仅能降低故障共享单车回收成本，而且相比武汉市现有的三个分布较远的回收中心，其总体运营成本更低，故障共享单车回收效率更高，便于共享单车的分区域运营管理。事实证明，基于K-means聚类算法与重心法确定回收中心选址问题不仅操作简单，可行性高，而且方便快捷，相较于现实中单一考虑成本等的选址方式，此模型更能兼顾多方面因素，优势明显。基于K-means聚类算法与重心法来确定回收中心选址，适用于城市的各个区域，选点精确又方便高效，模型具有较强的实用性。     

共享单车经济的量化分析——以宁波为例北大核心

以宁波为例,对共享单车经济进行量化分析.首先,通过网络爬虫抓取宁波市共享单车位置数据,并对数据进行预处理.之后,建立了空间分布特征和品牌分布特征模型,描述共享单车的地区分布情况.并且,我们建立了供需现状,市场竞争状况,发展趋势三个指标,综合分析共享单车品牌的发展状态.此外,考虑到ofo与mobike占有绝大部分市场,引入了改进吉诺模型,预测ofo,mobike两家共享单车公司的动态博弈过程.最终,以获得的宁波市数据为例,给出模型的算例分析.研究成果可为共享经济的分析提供理论借鉴.     

“第三方代管”参与下的共享单车回收路线优化问题

以共享单车回收为背景,研究了“第三方代管”参与下的回收路线优化问题。针对代管员和调度卡车的特征,提出激励代管员将零散分布的损坏单车运送至附近的中转点,然后派遣卡车将这些集中起来的损坏单车从中转点运送至维修中心。以总成本最小为目标建立混合整数规划模型,针对问题特性设计改进遗传算法。数值实验论证了问题特性,并论证得出在所提回收策略下及时回收损坏单车,不仅可以减轻公共空间被损坏单车挤占的问题,还可以有效减少回收成本。实验结果还表明所设计算法在短时间内能获得高质量解。     

基于细粒度时区的泊位共享动态定价研究

随着共享经济的蓬勃发展,作为缓解城市拥堵有效途径的泊位共享应运而生,它能够在很大程度上提高泊位闲置资源的利用率,从而解决停车难问题.但现有的定价策略难以合理调度泊位资源,基于粗粒度的分区泊位定价模式也存在着较为明显的缺陷,因而造成了大量泊位资源的浪费.为此,提出了满足细粒度时区的动态定价策略,通过建立泊位价格和泊位空闲率之间的关系模型,将某一区域内的泊位使用率控制在一定阈值.以武汉某大学校区内的所有停车场为例,结合离散型选择模型和优化后的RAF算法,旨在通过价格调度使得单一时间段内每个停车场的使用率达到理想值,从而更好地实现泊位资源的最优化配置.     

单向航道散货港口船舶调度优化

为提高单向航道散货港口的泊位利用率,研究多港池的散货港口船舶调度优化问题。考虑船舶间需保持安全航行距离、进出港时段交替条件和成簇进出港规则等现实约束,以进港船舶总等待时间最小为目标,构建了混合整数线性规划模型。基于问题的特点,设计了启发式规则与模拟退火算法相结合的混合算法进行求解。在数值实验中分别将该算法的结果同下界值和两种现实调度方案对比。结果表明,运用混合算法求解的结果与下界值的平均相对偏差为5.28%,较两种现实调度方案的目标值优化率提升显著,且平均泊位优化率分别为6.74%和4.71%,验证了方案及算法的有效性。     

基于降低失效停放区比率的共享单车系统稳定性研究

共享单车失效停放区使得用户无法取车或还车,影响共享单车系统运营稳定性。本文基于排队论与马尔科夫链构建了共享单车系统稳定状态平均场方程组并求解稳态解,发现停放区交互作用使得失效停放区对相邻停放区产生需求溢出效应,增加系统失效停放区比率,影响系统运营稳定性。本文以降低系统失效停放区比率目标,引入(M,S)交互策略,调节失效停放区对相邻停放区的交互作用程度。进一步,以算例分析比较相同类型与不同类型停放区交互作用、(M,S)交互策略下共享单车系统的失效停放区比率。结果表明(M,S)交互策略能够有效降低失效停放区比率,提高系统运营稳定性。本研究为共享单车企业运营稳定性决策提供了有价值的参考。     

共享单车平台定价策略研究

共享单车市场规模的高速增长,形成了以摩拜单车和OFO共享单车为代表的双寡头竞争局面,共享单车平台的定价策略在平台竞争过程中起了重要作用.通过构建两阶段Hotelling双寡头竞争模型,探讨双寡头在市场均衡状态下稳定的定价机制.研究表明,在阶段一以用户扩张速度最大化为目标的平台1和以利润最大化为目标的平台2为争取市场份额展开竞争,均衡情况下平台1占优的定价策略为低接入费用、高租赁费用,其条件为用户感知的平台横向差异化程度较高;平台2占优的定价策略为高接入费用、低租赁费用,其条件为用户感知的平台横向差异化程度较低.在阶段二两平台均以利润最大化为目标时,网络外部性大小、用户感知的平台横向差异化程度以及单车与用户匹配概率对两平台竞争的均衡结果均有一定的影响;平台为获取更多的市场份额和平台利润,最优的定价策略为采用高接入费用、低租赁费用,其条件为该平台单车与用户的匹配概率较高并且交易成本较低.     

考虑感知成本的共享单车用户借还车行为意向研究

为分析奖励措施下共享单车用户的借还车行为意向,弥补现有用户行为意向无法定量描述的不足,文章基于计划行为理论,提出了考虑不同感知成本的用户借还车行为意向的定量估计方法.首先从用户行为意向的影响因素出发,添加成本感知变量建立用户行为意向模型,利用预调查获取共享单车用户的个体特征和出行特征,通过正交实验法设计了关于出行目的、奖励金额与额外步行时间的出行场景问卷;然后利用收集的用户行为意向数据,基于出行效用理论建立用户行为选择概率模型,通过SPSS软件进行二项logistic回归求解模型参数,实现用户借还车行为意向的定量估计.研究结果表明:无论是借车还是还车,用户早高峰出行均具有更高的时间敏感性,当额外步行时间大于8 min时,奖励金额以及规范停车区的设置对用户借还车行为的影响不明显;而当额外步行时间小于5 min时,奖励金额和规范停车区的设置能较好的影响用户的借还车行为.     

大数据驱动的大型自行车共享系统及其站点车辆再平衡研究

近年来,大数据、云计算与物联网为复杂系统的组织与管理提供了有力的新型信息化技术,并引起了企业的组织架构与运营机制的多方面变化.基于此,本文首先针对大数据驱动的大型自行车共享系统构建了一个新的随机模型,既表达了大数据的重要作用,又描述了大型自行车共享系统的运营过程,特别是使用卡车对各个站点自行车的再平衡.其次,本文提出了一种研究大数据驱动的大型自行车共享系统的平均场极限理论,包括利用平均场理论建立非时齐的排队系统,由非时齐的排队系统建立系统的平均场方程组;给出了经验测度过程(empirical measure process)的非线性生灭过程,提出了分段结构下生灭过程的固定点的有效算法,由此能够计算每个站点稳态平均自行车数;用数值算例分析了每个站点稳态平均自行车数是如何依赖于自行车共享系统中的一些关键参数的.基于此,本文对大数据在大型自行车共享系统中所引起的物理效应进行了建模分析,从而为大型自行车共享系统的随机分析提供了一个极有研究潜力的重要发展方向.     

Adapting the ng-path relaxation for bike balancing problems

This paper deals with a static bike relocation problem that deploys a fleet of vehicles to redistribute shared bicycles. To solve the problem to optimality, we present a branch-price-and-cut algorithm. In particular, a new path relaxation for the pricing problem is introduced that relaxes the constraints on the maximum number of bikes to move at each station in a similar fashion as elementary can be relaxed in vehicle routing problems. Computational results show that our algorithm outperforms the former state-of-the-art.     

A multi-periodic optimization formulation for bike planning and bike utilization

The number of policy initiatives to promote the use of bike, or the combined use of bicycle and public transport for one trip, has grown considerably over the past decade as part of the search for more sustainable transport solutions. This paper presents an optimization formulation to design a bike-sharing system for travel inside small communities, or as a means to extend public transport for access and egress trips. The mathematical model attempts to optimize a bike-sharing system by determining the minimum required bike fleet size that minimizes simultaneously unmet demand, unutilized bikes, and the need to transport empty bikes between rental stations to meet demand. The proposed approach is applied to an example problem and is shown to be successful, ultimately providing a new managerial tool for planning and analyzing bike utilization more effectively.     

Dynamic repositioning strategy in a bike-sharing system; how to prioritize and how to rebalance a bike station

Bike-sharing systems are becoming increasingly popular in large cities. The natural imbalance and the stochasticity of bike’s arrivals and departures lead operators to develop redistribution strategies in order to ensure a sufficiently high quality of service for users. Using a Markov decision process approach, we develop an implementable decision-support tool which may help the operator to decide at any point of time (i) which station should be prioritized, and (ii) which number of bikes should be added or removed at each station. Our objective is to minimize the rate of arrival of unsatisfied users who find their station empty or full. The existence of an optimal inventory level at each station is proven. It may vary over time but does not depend on the capacity of the truck which operates the repositioning. Next, we compute the relative value function of the system, together with the average cost and the optimal state. These results are used to derive a policy for station’s prioritization using a one-step policy improvement method. We evaluate our policy in comparison with the optimal one and with other intuitive ones in an extended version of our model. From our numerical experiments, we show that only a little intervention of the operator can significantly enhance the quality of service, and that the rule of thumb for bike repositioning is to prioritize the closer, the more active, the closer to be full or empty, and the more imbalanced stations if no reversing in the imbalance is anticipated.     

A two-phase heuristic approach to the bike repositioning problem

An approach to overcome the bike imbalance problem is to transfer excess bikes to branches with bike shortages. This study develops a constrained mathematical model to deal with a multi-vehicle bike-repositioning problem, and aims to minimize the sum of transportation and unmet demand costs over a planning horizon through bike-transfer strategies under a minimum service requirement. A two-phase heuristic based on linear programming was proposed to solve the problem and produce compromising solutions. In the first phase, the paper developed a linear programming model to quickly develop decisions related to bike inventory, unloading, and loading for all stations for each time slot. In the second phase, this paper proposed an iterative approach through two parameter sensitive mathematical models to sequentially reduce the problem scale to develop decisions related to bike transfers. Computational results show that the proposed approach is superior to a CPLEX optimizer and a hybrid heuristic based on a genetic algorithm. The proposed approach was used to analyze the bicycle system in Taiwan. The impacts of various system parameters on the system were also investigated.     

A robust counterpart approach to the bi-objective emergency medical service design problem

The paper presents a bi-objective robust program to design a cost-responsiveness efficient emergency medical services (EMS) system under uncertainty. The proposed model simultaneously determines the location of EMS stations, the assignment of demand areas to EMS stations, and the number of EMS vehicles at each station to balance cost and responsiveness. We develop a robust counterpart approach to cope with the uncertain parameters in the EMS system. Extensive numerical studies are performed to demonstrate the benefits of our robust optimization approach.     

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??Recently, big data, could computing and internet of things provide some new information technologies for organization and management of complex systems, and they have caused multifaceted changes on organization framework and operations mechanism of enterprises. Based on this, we first construct a new stochastic model for a big data driven large-scale bike-sharing system, which expresses the important role played by big data, and describes the operations mechanism of the large-scale bike-sharing system, and specifically, the rebalancing of bikes in various stations in terms of trucks. Then, we present a mean-field limit theory, which is applied to analyzing the big data driven large-scale bike-sharing system, including establishing a time-inhomogeneous queueing system by means of the mean field theory, and setting up the mean-field equations through the time-inhomogeneous queueing system; providing an empirical measure process by means of a nonlinear birth-death process, giving algorithms for computing the fixed point in terms of a segmented structural birth-death processes, and computing the average number of bikes in each station; and providing numerical examples to analyze how the steady average number of bikes in each station depends on some key parameters of the bike-sharing system. Using these results, this paper analyzes physical effect of big data on performance of the large-scale bike-sharing. Therefore, this paper gives a promising research direction of stochastic model in the study of large-scale bike-sharing systems.     

基于多阶段模糊规模效应量化的TMC模式下供应链调度优化研究

旅游大规模定制(Tourism Mass Customization, TMC)模式实施的关键是通过对旅游供应链的调度优化处理旅游活动的“规模效应”与游客“个性化需求”之间的矛盾问题。运用经济学及模糊数学的理论方法分析并实现了TMC模式下存在的多阶段模糊规模效应量化处理。构建了引入规模效应量化的服务成本最小化、引入模糊时间窗的顾客满意度最大化及供应链协同度最大化为优化目标的TMC模式下多目标供应链调度优化模型。最后,通过蚁群算法实现TMC模式下多调度优化目标的求解并对优化效果进行对比研究。研究结果表明,TMC模式下供应链调度中旅游活动存在多阶段模糊规模效应并且可以量化处理;TMC模式中的规模效应具有合理的区间范围,旅游企业应注重规模效应与其他目标的均衡;蚂蚁算法在求解TMC模式下多目标优化问题方面不仅收敛速度快,而且通过对多调度目标优化效果的对比检验表明,性能稳健优良。     

多类型公共自行车调运问题

公共自行车是我国正大力发展的低碳交通出行模式,加强公共自行车调运优化是提升自行车出行吸引力的关键要素。通过对公共自行车调运背景分析,提出了一类多类型公共自行车的调运优化问题。针对现实生活中租赁站点内公共自行车不均衡的情况,建立了以总成本最小为目标的混合整数线性规划模型,并提出一种改进的混合禁忌搜索对问题进行求解。通过数值实验分析了问题特性并验证了算法性能。实验结果表明非均衡惩罚系数决定了租赁站点各类自行车的装卸载数量,并影响了调配车辆的运行路线,是实现多类型公共自行车均衡优化的关键因素。不同类型自行车的替代策略使得调运决策更加灵活。混合禁忌搜索可以求解更大规模的问题,并能在短时间内求得较好质量的解。