基于SEM的城市公交服务质量-满意度-忠诚度研究

将城市公交服务质量简化为乘客便利感知质量、乘车环境质量、运营服务质量三个维度,运用结构方程模型(SEM)建立了常规公交乘客服务质量-满意度-忠诚度模型,并结合南昌市实际调查数据,通过服务质量指标因子定量分析各变量对乘客满意度-忠诚度的影响。研究结果表明,乘客便利感知质量、乘车环境质量、运营服务质量对乘客满意度均有直接正向相关性,而对乘客忠诚度则无直接正向相关性;影响城市公交服务质量的重要因素依次是公交路线的换乘便利情况、公交车况、公交卫生状况和公交运行安全性。     

大型公交网络线路查询模型与算法

分析了大型城市公交网络的特点,为满足乘客出行时各种不同的需求,综合考虑换乘次数、出行时间与乘车费用等多种不同因素,通过构造线路与站点、站点与站点的连接矩阵,结合矩阵算法与搜索算法的优点,提出了一种分类多目标优化搜索算法.该算法搜索时间较短,能够生成多条备选路径供出行者选择,能基本满足自主查询计算机系统的需要.     

犹豫模糊环境下的变权综合决策方法及其在群决策中的应用

对因素权重为实数而因素状态值为犹豫模糊元的多因素不确定性决策问题,先由实数型状态变权向量导出犹豫模糊状态变权向量,由此得出犹豫模糊变权公式,进而建立犹豫模糊环境下的变权综合决策模型.然后将犹豫模糊变权综合决策模型应用于群决策,给出了相应的群决策算法.最后通过一个应用实例说明了本文所构建变权综合决策模型及相应群决策算法的有效性.     

基于蓄水池抽样的智能医保动态风险决策及应用

医保风险决策在实际应用中的难点是投保人的患病具有不确定性,为此尝试利用机器学习耦合蓄水池抽样建立动态预测模型,辅助医保公司开展智能风险决策.具体做了三个方面的工作:首先,构建了医保风险决策模型,并从理论上得到了最优决策规则;然后,基于样本量固定的历史数据,搭建了智能医保静态风险决策的框架;最后,为改进静态智能预测对于风险决策指导的滞后性,提出将机器学习耦合蓄水池算法开展智能动态风险决策的思路,在不断更新的数据集上进行动态抽样,建立随时间动态更新的预测模型.以投保病种为糖尿病为例,选用2017年天池精准医疗大赛——人工智能辅助糖尿病风险预测相关数据.鉴于数据特征变量维度高且类型复杂,因此选用的机器学习算法为随机森林.对样本量相同的训练集以及同一测试集进行实验和对比,结果表明:基于动态预测的决策模型效果优于静态决策模型.     

直觉模糊信息多属性优化决策模型及其应用

针对直觉模糊信息多属性决策中存在的问题,提出一种基于优化理论的多属性决策方法.该方法首先按照方案的属性类别确定直觉模糊"理想方案"和"负理想方案",然后计算各方案与"理想方案"和"负理想方案"的距离,运用扩展的最小二乘方准则构造目标函数求解,建立了直觉模糊信息多属性优化决策模型.最后,通过黄河下游公路大桥超深大直径钻孔...     

基于熵权的混凝土大坝浇筑方案多目标决策研究

系统分析大坝施工的机械配置、混凝土坝面分仓等控制性因素,通过计算机施工仿真,分析施工方案的施工强度、工期等决策因素,结合机械设备投资费用,运用基于熵权的多目标决策理论,得到决策熵权,结合决策者的主观权重,建立浇筑施工方案多目标决策模型.工程应用说明,应用熵权进行多目标决策,能够较好地反映各决策指标的差异程度,具有概念清晰、实用的特点.     

考虑预算约束的旅游服务质量提升决策方法

通过对Kano模型的定量化分析,从最大化顾客满意视角提出考虑预算约束的旅游服务质量提升决策方法。采用问卷调查方式获取顾客旅游服务质量因素的评价信息;依据Kano模型,对评价信息进行处理并建立顾客满意度与旅游服务质量满足水平的关系函数(S-CR);基于S-CR函数构建考虑预算约束条件下、顾客满意最大化的旅游服务质量提升决策模型,通过求解确定最优的预算分配方案。最后,通过实证验证模型的可行性与有效性。     

基于灰色系统理论的工程项目评标方法研究

工程项目评标实质是多目标决策问题,为优先出合适的投标单位,建立灰色Euclid理论工程项目评标决策模型.首先,运用层次分析法(AHP)与信息熵法分别确定主客观权重.然后利用博弈集结模型对指标体系进行组合赋权,得到综合权重.最后,结合工程项目评标实例运用灰色Euclid理论评标模型进行评标决策.结果表明,运用博弈集结组合赋权和灰色Euclid理论模型选出投标单位D为最优方案,与实际评标一致,验证了模型的可操作性和适用性.     

基于区间二型梯形模糊集的应急物资储备动态协同决策模型

重大灾害下应急物资储备决策是阻止突发灾难蔓延的有效手段之一。针对救灾信息具备不确定性与复杂性特点,构建基于区间二型梯形模糊集的应急物资储备动态协同决策模型,并给出应急物资储备策略。利用区间二型模糊集理论的决策方案并结合比例分析法(COPRAS),构建常态环境下应急物资供应商选择的群决策模型,解决不相容群决策属性之间的冲突问题;进而,充分考虑“救灾阶段性动态时间因素”对储备决策的影响,构建动态救灾环境下应急物资储备结构模糊优化模型,实现常态决策与非常态应急决策之间的动态协同;最后,以2012年云南丽江“6.14”突发特大山洪灾害为实例进行数值分析,验证该动态协同决策模型的合理性与可行性,能有效解决动态救灾环境下应急物资储备结构优化问题。     

“限时服务规则”下的复线船闸多目标调度优化

针对现有船舶过闸排队规则的欠缺,基于“限时服务规则”,构建复线船闸多目标双层优化调度模型:上层模型用于获得两个闸室安全区域的船舶排布可行方案;下层模型用于获得不同船舶排布可行方案的优化闸次数。下层模型分两个阶段完成:对符合“限时服务规则”的船舶,构建以闸次最少为目标的0-1规划模型,获得此类船舶安排的闸次;对其余船舶按照“先到先服务规则”,构建以闸次最少、闸室利用率最大为目标的多目标决策模型,获得不同船舶排布可行方案应该安排的频次。以位于江苏省干线航道上的某复线船闸某日24小时内过闸船舶的数据为例,计算结果表明:采用本文优化模型获得的优化方案与“经验编排方式”相比,两座船闸各节约2个闸次,两个船闸的平均闸室利用率分别提高了3.66和4.72个百分点。     

Performance analysis of an elevator system during up-peak

We model and analyze an elevator system during up-peak. We study the round-trip time, whose distribution depends strongly on the number of passengers waiting at the lobby, as well as the number of stops and the highest reversal floor. The distribution functions of the passenger queue length in the lobby, the round-trip time, the waiting time, the ride time and the journey time are derived.     

乘客到达时间不确定的机场动态拼车策略与算法研究

网约车拼车服务作为共享经济领域重要应用,已成为国内外研究热点。针对机场在线拼车平台运营中乘客等待时间过长和车辆行驶成本较高的突出问题,本文提出前瞻式动态拼车匹配策略。该策略将未来随机到达乘客信息纳入当前已到达乘客的拼车匹配决策中,建立了乘客匹配与车辆路径联合优化两阶段随机规划模型。为了在动态环境中实时产生高质量的匹配与路径规划方案,首先基于贝叶斯估计压缩乘客随机到达情景空间,建立了问题的确定性近似最优模型。为了快速求解模型,提出基于订单目的地和乘客期望到达时间相似度的匹配规则,并以此开发改进的差分进化算法。最后,基于某拼车平台真实订单数据,通过对比测试验证了前瞻式匹配策略和改进差分进化算法的有效性与计算效率。     

A model for setting services on auxiliary bus lines under congestion

In this paper, a mathematical programming model and a heuristically derived solution is described to assist with the efficient planning of services for a set of auxiliary bus lines (a bus-bridging system) during disruptions of metro and rapid transit lines. The model can be considered static and takes into account the average flows of passengers over a given period of time (i.e., the peak morning traffic hour). Auxiliary bus services must accommodate very high demand levels, and the model presented is able to take into account the operation of a bus-bridging system under congested conditions. A general analysis of the congestion in public transportation lines is presented, and the results are applied to the design of a bus-bridging system. A nonlinear integer mathematical programming model and a suitable approximation of this model are then formulated. This approximated model can be solved by a heuristic procedure that has been shown to be computationally viable. The output of the model is as follows: (a) the number of bus units to assign to each of the candidate lines of the bus-bridging system; (b) the routes to be followed by users passengers of each of the origin–destination pairs; (c) the operational conditions of the components of the bus-bridging system, including the passenger load of each of the line segments, the degree of saturation of the bus stops relative to their bus input flows, the bus service times at bus stops and the passenger waiting times at bus stops. The model is able to take into account bounds with regard to the maximum number of passengers waiting at bus stops and the space available at bus stops for the queueing of bus units. This paper demonstrates the applicability of the model with two realistic test cases: a railway corridor in Madrid and a metro line in Barcelona.     

Passenger grouping under constant threat probability in an airport security system

In a typical security system at a US airport, there are a series of inspections done on passengers as well as on baggage to check whether any item of threat is entering into the system. Considering the large number of passengers and items using our airports, one hundred percent check on all the items is not practical. This paper investigates the benefit of classifying passengers into different groups, with the idea that the number of checks and the degree of inspection may vary for different groups. The threat probability is assumed to be known and identical for all passengers. We develop a model to determine the number of groups, the fractions of passengers and the assignment of check stations for each group. The constraint is that the false clear probability is within Federal of Aviation Administration (FAA) specifications, and the objective is that the number of false alarms (a surrogate measure of passenger inconvenience) in the system is minimized. The model studies the effect of the system parameters on the number of items checked at various check stations and on the false alarm rate. The major conclusion of this paper is that passenger grouping is beneficial even when the threat probability is assumed constant across all passengers. A further conclusion is that the optimal grouping and check station assignments can depend on the overall threat probability.     

Steady state results for the M/M(a,b)/c batch-service system

The transportation system considered in this paper has a number of vehicles with capacity constraint, which take passengers from a source terminal to various destinations and return to the terminal. The trip times are considered to be independent and identically distributed random variables with a common exponential distribution. Passengers arrive at the terminal in accordance with a Poisson process. The system is operated under the following policy: when a vehicle is available and there are at least ‘a’ passengers waiting for service, then a vehicle is dispatched immediately. A recursive algorithm is derived to obtain the steady-state probability P(m, j) that there are m idle vehicles and j waiting passengers in the queue. Analytical expressions have been derived for passenger queue length distribution, average passenger queue length, the r-th moment of passenger waiting time in the queue, service batch size distribution and the average service batch size, all in terms of P(0,0).     

Simulation-based Selectee Lane queueing design for passenger checkpoint screening

There are two kinds of passenger checkpoint screening lanes in a typical US airport: a Normal Lane and a Selectee Lane that has enhanced scrutiny. The Selectee Lane is not effectively utilized in some airports due to the small amount of passengers selected to go through it. In this paper, we propose a simulation-based Selectee Lane queueing design framework to study how to effectively utilize the Selectee Lane resource. We assume that passengers are classified into several risk classes via some passenger prescreening system. We consider how to assign passengers from different risk classes to the Selectee Lane based on how many passengers are already in the Selectee Lane. The main objective is to maximize the screening system’s probability of true alarm. We first discuss a steady-state model, formulate it as a nonlinear binary integer program, and propose a rule-based heuristic. Then, a simulation framework is constructed and a neighborhood search procedure is proposed to generate possible solutions based on the heuristic solution of the steady-state model. Using the passenger arrival patterns from a medium-size airport, we conduct a detailed case study. We observe that the heuristic solution from the steady-state model results in more than 4% relative increase in probability of true alarm with respect to the current practice. Moreover, starting from the heuristic solution, we obtain even better solutions in terms of both probability of true alarm and expected time in system via a neighborhood search procedure.     

Passenger flow control strategies for urban rail transit networks

Urban rail traffic congestion is becoming increasingly serious due to the large traffic demands in modern cities. In order to ensure the safety and quality of station services in peak hours, it's necessary to adopt some reasonable and effective passenger flow control strategies. In this study, through considering the time-dependent passenger demands, a passenger flow control model based on the network-level system is explicitly developed. The passenger successive motion process is discretized by the modeling method. Systematically considering the coordinated relationship between traffic demands and strict capacity constraints (including station passing capacity, platform load capacity and train transport capacity), we establish a mixed integer linear programming model to minimize the total passenger waiting time (including passengers outside stations and on the platforms). The optimization software Cplex is adopted to solve the developed model, and a real network of Beijing urban railway is calibrated to verify the effectiveness of the suggested model. As a result, the proposed flow control strategies can provide detailed information about control stations, control durations and control intensities, and can effectively reduce the total waiting time and relieve the number of stranded passengers in the urban rail transit network.     

Determination of the number and locations of time points in transit schedule design — Case of a single run

An analytical model for the determination of the number and locations of time points as well as the amount of slack times in transit schedule design is developed. The model considers a bus route with a special passenger demand pattern in which all boarding passengers coordinate their arrivals at each stop in such a way that they never miss their intended bus, and therefore designing the schedule separately a single run at a time, becomes possible. The model employs the dynamic programming method to deal with the trade-offs among various cost components associated with the schedule quantitatively, and yet is flexible enough to incorporate the existing rules of thumb as well as transit operators' policies. Numerical examples that illustrate the applications of the model are given. The model, although not quite applicable to bus routes with general passenger demand patterns, is useful in the analysis of the contributing factors to the design of an economical, reliable, and operational transit schedule, and is likely to be adaptable for more realistic cases.     

Railway delay management with passenger rerouting considering train capacity constraints

Delay management for railways is concerned with the question of whether a train should wait for a delayed feeder train or depart on time. The answer should not only depend on the length of the delay but also consider other factors, such as capacity restrictions. We present an optimization model for delay management in railway networks that accounts for capacity constraints on the number of passengers that a train can effectively carry. While limited capacities of tracks and stations have been considered in delay management models, passenger train capacity has been neglected in the literature so far, implicitly assuming an infinite train capacity. However, even in open systems where no seat reservation is required and passengers may stand during the journey if all seats are occupied, physical space is naturally limited, and the number of standing seats is constrained for passenger safety reasons. We present a mixed-integer nonlinear programming formulation for the delay management problem with passenger rerouting and capacities of trains. Our model allows the rerouting of passengers missing their connection due to delays or capacity constraints. We linearize the model in exact and approximate ways and experimentally compare the different approaches with the solution of a reference model from the literature that neglects capacity constraints. The results demonstrate that there is a significant impact of considering train capacity restrictions in decisions to manage delays.     

高速铁路旅客满意度研究:服务接触理论视角

作为旅客重要的出行方式,高速铁路既面临着市场竞争,又面临着旅客不断增长的服务质量要求。基于服务接触理论形成高速铁路客运服务接触链,从理论上构建高速铁路旅客满意度模型,研究影响旅客满意度的关键接触点。运用SEM定量分析潜变量间的路径系数,对该理论模型的路径假设进行检验。结果表明:四个阶段的服务接触直接影响旅客满意度,间接影响旅客忠诚度;四个服务接触阶段对旅客满意度的影响效应从高到低依次是列车、候车、出站、进站;各服务接触阶段对旅客忠诚度的影响存在明显差异。