多方式弹性需求下公交定价双层规划模型

为描述多方式城市交通网络下公交定价与出行选择行为的相互作用与影响,将出行方式选择与路径选择涵盖于同一网络,建立了上层模型分别以企业利润最大化、乘客出行成本最小化和社会福利最大化为目标函数,下层模型为多方式弹性需求随机用户配流模型的公交定价双层规划模型。运用改进遗传算法对模型整体进行求解,下层模型采用综合对角化算法和MSA算法的组合求解算法。最后,设计了一个算例以说明模型应用。结果表明:运用双层规划模型所确定的公交票价较传统静态票价可使政府、企业及出行者三方都获得更高收益,且上层模型以社会福利最大化为目标函数能代表社会群体中多数人利益,优化效果最为理想。     

基于CVaR的次优拥挤收费随机多目标模型

次优拥挤收费问题一般要考虑不同决策者的不同利益,因此,有必要考虑多个收费策略建立多目标模型来均衡不同决策者的利益.由于决策者常在信息不确定的情况下做决策,在出行需求不确定的条件下,为了确定次优拥挤收费的方案,建立了基于条件风险价值的随机多目标双层规划模型,上层规划的目标函数考虑了系统总阻抗和社会公平性,下层规划是UE用户均衡配流问题.利用基于随机模拟的遗传算法对模型进行求解,并通过数值算例对模型和算法进行分析,验证了模型的有效性.     

基于CVaR的次优拥挤收费随机多目标模型北大核心

次优拥挤收费问题一般要考虑不同决策者的不同利益,因此,有必要考虑多个收费策略建立多目标模型来均衡不同决策者的利益.由于决策者常在信息不确定的情况下做决策,在出行需求不确定的条件下,为了确定次优拥挤收费的方案,建立了基于条件风险价值的随机多目标双层规划模型,上层规划的目标函数考虑了系统总阻抗和社会公平性,下层规划是UE用户均衡配流问题.利用基于随机模拟的遗传算法对模型进行求解,并通过数值算例对模型和算法进行分析,验证了模型的有效性.     

考虑资源约束和数量折扣的联合补货-选址库存协同优化研究

本文构建了一种基于联合补货策略的配送中心选址-库存协同优化新模型,该模型允许缺货,有资源约束且考虑数量折扣;同时设计了一种融合模拟退火思想的双种群独立进化的自适应差分算法(Adaptive Simulated Annealing Differential Algorithm,ASADE)对该模型进行求解,并通过算例与自适应差分算法、改进的蛙跳算法进行对比,证实了ASADE算法的有效性。最后进行了敏感性分析,讨论相关参数变动对总成本的影响,可为管理者更好决策提供有益的依据。     

多模式城市交通网络随机用户平衡配流模型

本文考虑了影响出行者选择出行方式的多种因素(如出行时间、花费、舒适性等),分析了多种交通方式并存的城市交通网络,基于随机用户平衡理论构造了多模式的交通配流模型,并证明了模型解的等价性和惟一性,给出了求解算法.     

基于游客年龄特征的旅游巴士定价研究

为旅游巴士设计合理的定价,对旅游公共交通的发展有着积极影响。通过对游客出行偏好的分析,考虑不同年龄阶段的游客在选择行为上有较大的差异,建立了上层以旅游巴士企业利润最大为目标,下层为多方式多人群弹性需求随机用户平衡的旅游巴士定价模型,并设计了改进粒子群算法求解问题。数值实验结果表明:1)年龄特征会影响最优定价策略,考虑游客年龄在选择行为上的差异得出的票价更优;2)舒适度敏感系数对定价有影响,且旅游巴士较常规公交,舒适度更好,一定程度上提高了旅游巴士企业的竞争力;3)改进粒子群算法较标准粒子群算法,有更好的求解性能和质量。     

考虑路网可靠性和空间公平性的次优拥挤收费模型

根据随机路网环境下出行者规避风险的路径选择行为,提出了一种考虑路网可靠性和空间公平性的次优拥挤收费双层规划模型。其中,上层模型以具有空间公平性约束条件下最大化路网的社会福利为目标,下层模型是实施拥挤收费条件下考虑行程时间可靠性的弹性需求用户平衡模型。鉴于双层规划模型的复杂性,设计了基于遗传算法和FrankWolfe算法的组合式算法来求解提出的模型。算例结果表明:考虑行程时间可靠性的次优拥挤收费会产生不同于传统次优拥挤收费的平衡流量分布模式,表明出行者的路径选择行为对拥挤收费结果会产生直接影响;此外,算例结果还说明遗传算法对参数设置具有很强的鲁棒性。     

基于物流AGV的“货到人”订单拣选系统任务调度研究

基于物流AGV的“货到人”订单拣选模式由于其高效率和灵活性,逐渐成为电商物流配送中心订单拣选系统发展趋势。本文通过对基于物流AGV的电商物流配送中心订单拣选作业流程分析,提出多拣选台同步拣选和多拣选台异步拣选两种作业模式。然后对基于物流AGV的订单拣选任务调度问题进行描述,以物流AGV完成所有任务的时间最短为目标,分别建立同步和异步两种拣选模式下物流AGV任务调度模型;针对物流AGV任务调度问题特性,对共同进化遗传算法粗粒度模型进行改进用于模型求解。最后,通过改进前后算法的对比,验证了改进共同进化遗传算法在求解物流AGV任务调度问题中的有效性;通过在求解速度和优化结果上对多拣选台同步拣选和异步拣选两种作业模式进行比较,得出同步拣选优于异步拣选的结果。     

一种改进的公交网络最优路径算法

通过对公交网络模型进行分析,考虑公交线路票价变化,按照出行时间最短同时保证换乘次数较少的原则,对现有解决公交网络最短路问题的算法进行改进.应用了将公交线路抽象为顶点,建立邻接矩阵的方法处理换乘问题.通过实际问题计算验证了算法的有效性.     

考虑禁左条件的用户均衡交通配流模型与算法

交叉口处左转车流是对车流量影响较大的一个流向,也是造成城市交通拥挤和交通事故的关键因素之一,所以越来越多的城市路网选择在交叉口禁止左转来保障主干路车流通畅.在禁左的情况下,出行者究竟如何选择出行路径,究竟应该在哪些交叉口采取禁左管理可以使网络总旅行时间最小?针对以上两个问题,将根据用户均衡(UE)原理,建立考虑交叉口禁左条件的交通配流双层规划模型.利用Frank-Wolfe算法,对下层模型中的出行车辆进行配流,并返回到上层模型计算网络总旅行时间,通过遗传算法确定设置禁左交叉口位置,使得网络总旅行时间达到最小.根据数值算例,展现合理设置禁左交叉口的位置,可以使得交通网络总费用减小.     

Transit passenger origin-destination estimation in congested transit networks with elastic line frequencies

This paper deals with the transit passenger origin-destination (O-D) estimation problem by using updated passenger counts in congested transit networks and outdated prior O-D matrix. A bilevel programming approach is extended for the transit passenger O-D updating problem where the upper-level problem seeks to minimize the sum of error measurements in passenger counts and O-D matrices, while the lower level is the stochastic user equilibrium assignment problem for congested transit networks. The transit assignment framework is based on a frequency-adaptive transit network model in this paper, which can help determine transit line frequencies and the network flow pattern simultaneously in congested transit networks. A heuristic solution algorithm is adapted for solving the transit passenger O-D estimation problem. Finally, a numerical example is used to illustrate the applications of the proposed model and solution algorithm. The work described in this paper was mainly supported by two research grants from the Research Grants Council of the Hong Kong Special Administrative Region (Project No. PolyU 5143/03E and PolyU 5040/02E).     

Optimisation of timetable-based, stochastic transit assignment models based on MSA

Public transport assignment models have increased in complexity in order to describe passengers' route choices as detailed and correctly as possible. Important trends in the development are (1) timetable-based assignment, (2) inclusion of feeder modes, (3) use of stochastic components to describe differences in passengers' preferences within and between purposes and classes (random coefficients), as well as to describe non-explained variation within a utility theory framework, and (4) consideration of capacity problems at coach level, system level and terminal level. In the Copenhagen-Ringsted Model (CRM), such a large-scale transit assignment model was developed and estimated. The Stochastic User Equilibrium problem was solved by the Method of Successive Averages (MSA). However, the model suffered from very large calculation times. The paper focuses on how to optimise transit assignment models based on MSA combined with a generalised utility function. Comparable tests are carried out on a large-scale network. The conclusion is that there is potential of optimising MSA-based methods. Examples of different approaches for this is presented, tested and discussed in the paper.     

Developing work schedules for an inter-city transit system with multiple driver types and fleet types

In developing work schedules, the job assignment flexibility exploits the variety of available skills, thus enabling the assignment of workers to perform different jobs. In this study, we investigate the problem of finding the mix of primary and secondary jobs in short term work schedules to meet, at minimum cost, the daily service requirements of an inter-city bus transit firm in Andra Pradesh India operating multiple fleet types. We formulate the problem as a set covering model with resource allocation constraints. We develop a branch-and-price procedure to solve the model. Computational results are provided.     

Estimation of Transit Origin–Destination Matrices from Passenger Counts Using a Frequency-Based Approach

This paper investigates the transit passenger origin–destination (O–D) estimation problem in congested transit networks where updated passenger counts and outdated O–D matrices are available. The bi-level programming approach is used for the transit passenger O–D estimation problem. The upper level minimizes the sum of error measurements in passenger counts and O–D matrices, and the lower level is a new frequency-based stochastic user equilibrium (SUE) assignment model that can determine simultaneously the passenger overload delays and passenger route choices in congested transit network together with the resultant transit line frequencies. The lower-level problem can be formulated as either a logit-type or probit-type SUE transit assignment problem. A heuristic solution algorithm is developed for solving the proposed bi-level programming model which is applicable to congested transit networks. Finally, a case study on a simplified transit network connecting Kowloon urban area and the Hong Kong International Airport is provided to illustrate the applications of the proposed bi-level programming model and solution algorithm. This revised version was published online in July 2006 with corrections to the Cover Date.     

机场新增卫星厅对登机口影响的评估方法

为了解决航站楼客流量饱和的问题,采用一种增加卫星厅的方法,实现了旅客分流.基于单目标整数线性规划和多目标优化的方法,分别构建了登机口优化分配网络模型和多目标优化模型.利用登机口优化分配网络算法筛选出所使用的共同登机口,建立了目标函数并列出约束条件,采用目标约束法对建立的模型进行求解.在此基础上,根据目标建模的思想,建立了可供中转旅客总体流程时间最短且使用登机口数量最小的航班-登机口分配模型.利用MATLAB计算可知,利用42个登机口即可实现303架航班的正常运转.     

弹性需求下城市公交网络服务的优化

对具有弹性需求的城市公交网络系统进行了票价结构与发车频率组合的优化。考虑到公交定价和发车频率会影响乘客需求以及乘客对路径的选择行为,将这一问题描述为一个双层规划问题,上层是寻求社会福利最大的优化问题;下层考虑了乘客的出行选择行为,为弹性需求下乘客在城市公交网络上流量分布的随机用户平衡分配模型。鉴于双层规划问题的非凸性,运用模拟退火算法对模型进行求解,并给出一个仿真算例说明提出的模型和算法的合理性。     

非平衡指派模型及其在航空机务维修中的应用

非平衡指派问题是最优平衡指派问题的推广与深化,在航空机务维修工作中,维修任务的合理配置对及时完成维修任务,保障训练作战计划非常重要.本文从装备完好率和人力资源的优化配置角度出发,按照不考虑维修任务等待时间和考虑维修任务等待时间两种情况分别建立了非平衡指派优化模型,并给出了这两种情况下效益矩阵的构造方法,进而将优化模型转化为最优平衡指派模型进行求解,从而为航空机务维修工作中维修人员的优化配置提供了一种科学、合理的决策方法.     

Solving the forward-reserve allocation problem in warehouse order picking systems

Many warehouses store at least some goods in two areas, a reserve areathat is efficient for storage and a forward area that is efficient fororder picking. The forward-reserve allocation problem determines the set ofStock-Keeping Units and their space allocations in the forward area to maximizethe forward area's benefit by trading off the relevant costs of orderpicking and internal replenishment. The mathematical model of this decisionresembles the classical knapsack problem with the additional complexity that ithas a discontinuous nonlinear cost function. A simple greedy heuristic has beenproposed in the literature to solve this problem. This paper proposes analternative branch-and-bound algorithm that can quickly solve the problem tooptimality. Heuristic and optimal solutions are numerically compared usingproblem instances based on real warehouse data. Results suggest that theheuristic solutions are very close to the optimal ones in terms of both theobjective value and the forward assignment.     

New MILP models for the permutation flowshop problem

Two new mixed-integer linear programming (MILP) models for the regular permutation flowshop problem, called TBA and TS3, are derived using a combination of JAML (job-adjacency, machine-linkage) diagrams and variable substitution techniques. These new models are then compared to the incumbent best MILP models (Wilson, WST2, and TS2) for this problem found in the flowshop sequencing literature. We define the term best to mean that a particular model or set of models can solve a common set of test flowshop problems in significantly less time than other competing models. In other words, the two new MILP models (TBA and TS3) become the challengers to the current incumbent best models (Wilson, WST2, TS2.). Both new models are shown to require less time, on average, than the current best models for solving this set of problems; and the TS3 model is shown to solve these problems in statistically significantly less time than the other four models combined.