数据驱动环境下考虑多重干扰情境的共享单车重置优化研究

无桩特性给共享单车重置调度带来了全新的问题和挑战，决策者面对的是随机分布在整个区域面上的单车如何调度，这与传统有桩公共自行车的重置有着显著区别。基于此，本文以某品牌共享单车为研究对象，通过分析其运行轨迹数据，界定出共享单车的活跃点（放车点）与非活跃点（收车点）。由于收车点的重置需求会因用户行为影响而不断发生变化，本文结合干扰管理的思想，综合考虑收车点的单车数变化、收车点消失以及新增收车点等干扰情境，建立考虑多重干扰情境的共享单车重置调度多目标优化模型，并设计带精英策略的非支配排序遗传算法对其求解。测试结果表明，本文所构建的模型和求解算法能够快速寻找到问题的帕累托最优解集，可为共享单车的重置调度提供有效的策略建议和决策支持。     

基于时空聚类预测的共享单车调度优化研究

无桩共享单车网络中存在着供需时空失衡现象,造成了共享资源的浪费及运营商管理成本的增加。为此,针对共享单车需求时间空间的分布特点,本文提出通过聚类分析的方法将具有相似时空属性的单位区域聚合为调度集群,使单车调度问题转化为有时间窗与载重量限制的车辆路径问题,并提出加入衡量集群划分是否合理的惩罚成本,构建共享单车调度路径优化模型。最后,本文改进了贪心算法、遗传算法对模型进行求解,并对算法的调度效果进行比较,为共享单车调度问题提出了从网络分析到调度优化的完整框架和改进算法,具有一定借鉴意义。     

考虑服务水平与动态转移规律的共享单车投放策略研究

为实现共享单车行业的精细化、科学化的运营管理，解决共享单车的过度投放问题。首先，对单车运营系统进行分析和描述，明确投放量优化涉及的核心研究问题。将研究范围界定在单车投放环节，利用马尔可夫链与状态转移矩阵来分析和描述单车的流转过程；从需求被满足概率的角度描述共享单车系统的服务水平并据此分析各投放点的单车需求量。在分析和描述的基础上，提出单车投放量优化的核心问题。其次，基于需求量的约束与单车流转规律，构建不同调度方案下的投放量优化模型，结合可行解空间变化对目标函数最优值的影响，分析最优单车投放量的性质。得出结论认为：（1）对时空的细分，有助于更准备地把握需求，最优单车投放量也会增加。（2）可以通过调度频率的增加，减少单车的投放量，但投放量有上、下限。最后，结合案例数据，对状态转移概率矩阵和服务水平约束下节点的需求量进行了计算，并据此对优化模型进行数值求解，展示了所构建模型的应用过程，说明了模型对于解决具体问题的可操作性和有效性。文章的模型及相应的求解过程，可为解决资源的优化配置问题提供参考和借鉴。     

准公共产品:基于政府监管机制的共享单车投放管理研究

随着信息技术的快速发展，以"互联网+"模式为基础的共享单车成为解决城市"最后一公里"问题的最佳选择。本文从公共管理的视角，运用演化博弈的方法，基于共享单车的投放管理，研究了政府部门和共享单车运营商的长期动态演化过程。研究发现，运营商进行共享单车投放量管理时应将社会收益作为衡量指标之一，政府部门积极发挥市场监管职能，引导运营商建立有效的市场规则，同时顺应当前互联网发展的趋势，利用社会化媒体合理有效发挥政府部门的社会监督职能，加快实现政府发挥监管作用、企业单车投放量管理高效化的稳定市场格局。     

论共享经济下个人数据的法律保护

<正>本文立足于共享经济兴起的社会背景,在分析共享经济背景下个人数据安全风险的基础上,总结并借鉴了域外个人数据的相关立法经验,对完善我国个人数据的法律保护提出些许构思。2017年5月,在国际安全极客大赛GeekPwn上,一名浙江大学毕业生演示了她所发现的多款共享单车系统的漏洞,通过这些漏洞,可以掌握共享单车用户的历史骑行路径、骑行时间、GPS定位、账户余额和注册账户信息等,并将这些信息复制到其他智能手机上使用。用户数据信息是共享平台能够整合社会资源的重要     

电子商务下基于改进两阶段算法的有时间窗车辆调度优化

为满足电子商务下的物流配送需求,将传统车辆调度模型进行修改,将目标函数改为基于费用最小,在约束条件中增加时间约束、货物容积约束、车辆最大工作时间、多种车型、载重量限制和最大行驶距离等,以提高模型的适用性和通用性。由于有时间窗的车辆调度问题是NP难问题,采用改进两阶段算法进行求解。即第一阶段用模糊分层聚类法将客户群分成若干区域,在每个区域又用扫描算法分解成若干符合约束条件的小规模子集;第二个阶段对各个分组内客户点,就是一个个单独TSPTW模型的线路优化问题,因此,采用改进混合遗传算法进行优化求解,最后的算例仿真表明了算法的有效性和可行性。     

供应链中具有产能限制的竞争企业中的信息共享研究

本文研究由一个供货商和两个制造商组成的二级供应链中，制造商之间的横向信息共享策略和供应商的定价问题。在模型中，上游供应商同时为下游两家制造商提供价格相同的原材料，下游制造商生产具有替代性的商品进行数量竞争，并受到相同的产能限制。以Cournot博弈为研究手段，求解了制造商的均衡订货决策和信息共享策略，分析比较了在不同的信息共享策略下制造商的利润、供应商的利润和批发价格。本文在研究制造商的信息共享策略时考虑了上游供应商的批发价格的影响。研究表明，当均衡解受到产能约束时，制造商的信息共享策略会反向。在某些情况下，完全信息共享和完全信息不共享都可能成为博弈的占优策略。上游供应商通过调整批发价格可以影响制造商的信息共享决策。该模型为上游供应商提供了一种最优定价策略，也为下游制造商提供了求解自身最优订货量和信息共享决策的方法。     

基于势函数的多车自动驾驶交互

多车辆的分布式控制是近年来的一个热门话题。与传统的单体系统相比,多车系统需要更复杂的控制逻辑和算法来完成给定的任务。本文对多车辆系统建立势函数模型,通过周围车辆工况建立障碍物虚拟力场和目标点的引力力场,确保连通性和避障的前提下,进行路径规划并到达指定位置。利用matlab软件搭建仿真平台,并对模型进行了验证。优化后的结果表明,该模型有效地实现了多车辆的控制。     

有数量限制的双覆盖标准救护车选址模型

目前已经有大量的文章研究救护车选址问题,这些研究主要关注覆盖率而忽略了救护车停放点的规模问题;虽然有些文章也限制了每个救护车存放点上停放车辆的上限,但是忽略了下限,只是自然的令其为0.从规模效应来看,如果停车点停放车辆,将数量限定字一定范围内会便于管理、降低成本.所以,本文建立了有数量限制的双覆盖标准救护车选址模型,在该模型中,如果在某个救护车存放点停放救护车,则该点存放的救护车数量介于上限和下限之间,从而保证规模效应;同时给出了基于拉格朗日松弛的禁忌搜索算法和算例.模拟计算表明可以通过调整参数来达到覆盖率和停放数量限制之间的均衡.     

服务质量、需求强度与共享出行平台定价——基于平台封闭与开放策略的视角

共享出行平台逐步由增加用户流量的扩张期进入提升出行多元化体验的稳定期,通过采取开放策略,平台在原有共享车基础上引入出租车来提供差异化服务。本文基于双边市场理论,构建共享出行平台定价策略模型,并利用计算实验方法,在Repast中模拟共享出行平台多智能体运行场景。研究发现：①无论平台选择封闭策略还是开放策略,平台对共享车收取的交易费应随着乘客出行需求强度增大而升高,且应随着共享车服务质量的增大而降低;平台对乘客收取的交易费应随着共享车服务质量增大而升高,且应随着乘客的出行强度的增大而降低。②平台开放程度越高,平台对共享车收取的交易费越低,对乘客收取的交易费越高。③为了利润最大化,平台应选择开放策略,并需注重提高共享车的服务质量。本文为共享出行平台优化运营策略提供了理论指导。     

The bike sharing rebalancing problem: Mathematical formulations and benchmark instances

Bike sharing systems offer a mobility service whereby public bicycles, located at different stations across an urban area, are available for shared use. These systems contribute towards obtaining a more sustainable mobility and decreasing traffic and pollution caused by car transportation. Since the first bike sharing system was installed in Amsterdam in 1965, the number of such applications has increased remarkably so that hundreds of systems are now operating all over the world.In a bike sharing system, users can take a bicycle from a station, use it to perform a journey and then leave it at a station, not necessarily the same one of departure. This behavior typically leads to a situation in which some stations become full and others are empty. Hence, a balanced system requires the redistribution of bicycles among stations.In this paper, we address the Bike sharing Rebalancing Problem (BRP), in which a fleet of capacitated vehicles is employed in order to re-distribute the bikes with the objective of minimizing total cost. This can be viewed as a special one-commodity pickup-and-delivery capacitated vehicle routing problem. We present four mixed integer linear programming formulations of this problem. It is worth noting that the proposed formulations include an exponential number of constraints, hence, tailor-made branch-and-cut algorithms are developed in order to solve them.The mathematical formulations of the BRP were first computationally tested using data obtained for the city of Reggio Emilia, Italy. Our computational study was then extended to include bike sharing systems from other parts of the world. The information derived from the study was used to build a set of benchmark instances for the BRP which we made publicly available on the web. Extensive experimentation of the branch-and-cut algorithms presented in this paper was carried out and an interesting computational comparison of the proposed mathematical formulations is reported. Finally, several insights on the computational difficulty of the problem are highlighted.     

Detection of unusable bicycles in bike-sharing systems

In bike-sharing systems, a small percentage of the bicycles become unusable every day. Currently, there is no reliable on-line information that indicates the usability of bicycles. We present a model that estimates the probability that a specific bicycle is unusable as well as the number of unusable bicycles in a station, based on available trip transaction data. Further on, we present some information based enhancements of the model and discuss an equivalent model for detecting locker failures.     

自动化立体仓库中出入库任务顺序与出库位置选择集成优化研究

具有多个出口的自动化立体仓库系统是一种将存储和分拣相结合的新型仓储技术,其最典型的特征是在货架底层有很多个出库位置以供取货人员分拣。研究此系统中出入库任务排序与出口选择的集成优化问题,以最小化堆垛机完成所有任务的移动距离为目标,将此问题转化为一个混合整数规划模型。根据问题的特点设计了两阶段启发式算法求解此问题,数值结果表明设计的算法能在较短时间内给出近似最优解,同时与企业常用的先到先服务方法相比,该算法可以缩短超过20%的移动距离。     

考虑流动性和组合再平衡风险的股票质押率优化模型

股票质押率的合理与灵活设定对于降低质权人风险，从而避免市场恐慌情绪蔓延具有重要意义。考虑到现有研究和实务操作往往只是简单地基于波动率来计算股票质押率，而忽视了流动性、杠杆和交叉持股等因素对股票质押率的影响，即在股票质押率定价过程中未能考虑质押标的的流动性风险和组合再平衡风险。鉴于此，本文分析了杠杆对投资者资产组合价值的影响机理，以及交叉持股对质押标的的正反馈效应，构建了一个兼顾波动率、流动性和组合再平衡风险的股票质押率定价模型。另外，通过模拟分析发现，杠杆及流动性冲击均显著影响股票质押率；且与仅基于波动率预测的传统模型相比，本文提出的定价模型所计算的股票质押率要显著低于前者，结果证明本模型确能有效解决杠杆过高、交叉持股情况下，股票质押率的合理调整问题。     

Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events

Evacuating residents out of affected areas is an important strategy for mitigating the impact of natural disasters. However, the resulting abrupt increase in the travel demand during evacuation causes severe congestions across the transportation system, which thereby interrupts other commuters' regular activities. In this article, a bilevel mathematical optimization model is formulated to address this issue, and our research objective is to maximize the transportation system resilience and restore its performance through two network reconfiguration schemes: contraflow (also referred to as lane reversal) and crossing elimination at intersections. Mathematical models are developed to represent the two reconfiguration schemes and characterize the interactions between traffic operators and passengers. Specifically, traffic operators act as leaders to determine the optimal system reconfiguration to minimize the total travel time for all the users (both evacuees and regular commuters), while passengers act as followers by freely choosing the path with the minimum travel time, which eventually converges to a user equilibrium state. For each given network reconfiguration, the lower‐level problem is formulated as a traffic assignment problem (TAP) where each user tries to minimize his/her own travel time. To tackle the lower‐level optimization problem, a gradient projection method is leveraged to shift the flow from other nonshortest paths to the shortest path between each origin–destination pair, eventually converging to the user equilibrium traffic assignment. The upper‐level problem is formulated as a constrained discrete optimization problem, and a probabilistic solution discovery algorithm is used to obtain the near‐optimal solution. Two numerical examples are used to demonstrate the effectiveness of the proposed method in restoring the traffic system performance.     

利用和声分散搜索算法解决动态共乘的乘客选择研究

动态共乘作为一种配合解决城市交通出行难题的新模式近年来引起了人们越来越多的关注，然而在较大范围内选择合适的乘客，以便获得最佳的综合服务效果却具有相当大的挑战性。本文正是针对这一问题，建立了以乘客效用最大化和司机总行程最短为目标函数，以满足司机与乘客的时间要求和司机参与约束为限制条件的多目标0-1规划共乘模型，用于帮助司机选择最合适的乘客。根据该模型的特点，构造了加入了分散搜索机制的新的和声搜索算法。在仿真实验时，针对司机和乘客效用的两种产生方式，在较大规模的路网环境下利用该算法分别对模型进行了求解，得到了Pareto最优解集。仿真结果不仅表明了模型的合理性和算法的可行性，而且还指出基于效用函数可以发现更多合适的潜在乘客。最后，通过与文献中其它算法的对比进一步展示了本文算法的有效性。     

Collaborative optimization for metro train scheduling and train connections combined with passenger flow control strategy

For high-frequency metro lines, the excessive travel demand during the peak hours brings a high risk to metro system and a low comfort to passengers, so it is important to consider passenger flow control when designing the metro train scheduling strategy. This paper presents a collaborative optimization method for metro train scheduling and train connections combined with passenger control strategy on a bi-directional metro line. Specifically, the dynamic equations for the train headway and train passenger loads along the metro line, the turnaround operations and the entering/exiting depot operations are considered simultaneously. The proposed collaborative optimization problem is formulated as a mixed integer nonlinear programming model to realise the trade-off among the utilization of trains, passenger flow control strategy and the number of awaiting passengers at platforms, which is further reformulated into mixed integer linear programming (MILP) model. To handle the complexity of this MILP model, a Lagrangian relaxation-based approach is designed to decompose the original problem into two small subproblems, which reduces the computational burden of the original problem and can efficiently find a good solution of the train schedule and train connections problem combined with passenger flow control strategy. The numerical experiments are implemented to investigate the effectiveness of the proposed model and approach, which shows that the proposed model is not sensitive to uncertain passenger demand. Under the proposed collaborative optimization approach, the number of train service connections and the crowding inside stations and carriages with the proper passenger flow control strategy can be evidently balanced, and thereby the operation efficiency and safety of the metro lines are effectively improved.     

基于混合流水作业组织的集装箱码头装卸作业集成调度优化

集装箱码头集疏运资源调度的对象是由岸桥、集卡、场桥所构成的多阶段一体化的集装箱装、卸、运操作系统,将该系统的调度优化基于多阶段混合流水线调度问题建立混合整数规划模型,同时考虑集装箱码头现实作业中预定义顺序、避免岸桥交叉作业、以及取决于作业顺序的切换时间等现实约束,针对问题自身的特点设计了两阶段启发式算法,得出各阶段设备的指派结果及作业顺序。通过与基于现行调度规则的调度方案以及与目标函数理论下界值的对比实验,显示了所提出的集成调度模型及求解算法能够有效降低船舶在港时间并实现集卡资源的共享,为集装箱码头集疏运资源的集成调度提供了新的思路。     

Multiple criteria approach for solving feeder assignment and assembly sequence problem in PCB assembly

The feeder assignment and assembly sequence problem in printed circuit board (PCB) assembly with the twin objectives of minimizing magazine travel time and minimizing board travel time is presented in this study. The problem uses Dynamic Pick-and-Place (DPP) model where robot arm, board and magazine move together with different speeds based on relative coordinates between consecutive assembled points. The difficulty of the problem is that the feeder assignment depends on assembly sequence and vice versa. A new approach is proposed to improve the existing approaches. The trade-off between two strategies, assembly by area and assembly by component types, can give better results. The numerical experiments proved the efficiency of the new algorithm.