城市轨道交通车站序列瓶颈系统优化分析

针对城市轨道交通车站进站设施瓶颈疏解缺乏系统的定量分析、成本模糊的问题,提出车站瓶颈定量分析模型,并在此基础上提出一种新优化策略。首先,建立乘客进站流程图,基于串联和并联混合的排队网络构建系统优化模型;其次,在现有优化策略基础上提出一种新的控制优化策略——变更设施序列,即交换安检设备和自动检票闸机的物理顺序;最后选取上海莘庄地铁站并根据两种优化策略给出具体优化方案,进行仿真实验。3种优化方案均有效减少了乘客排队时间,但总成本差异很大。到达率一定时,与无优化方案相比,优化方案1增加1台安检设备,减少了92.5%的等待时间,总成本增加了3.2%;优化方案2交换安检设备和闸机顺序,减少了80.3%的等待时间,总成本下降了50.4%;而优化方案3即方案1和2的叠加,几乎完全消除了乘客排队等待时间,但总成本却增加29.6%。结果分析表明,该模型能够很好地模拟瓶颈疏解成本,新策略在降低总成本上明显优于传统策略。     

铁路快捷货物货源组织优化模型及算法

针对铁路快捷货物货主在选择货运产品时具有越来越多的主动权,研究货主选择车次条件下的快捷货物货源组织优化问题。构建了以货源组织成本最小为目标的0-1规划模型,并根据问题的特点,设计了整数编码的遗传算法进行求解。通过一个数值算例对模型和算法的可行性及有效性进行了验证。研究结果表明,所提出的方法能够综合反映铁路和货主双方的收益,可为铁路货运组织改革条件下的快捷货物货源组织提供参考。     

基于NLAPSO．RBF的铁路货运量预测研究

铁路货运量需求预测在国家和区域经济发展规划、运输经营决策中具有重要作用。针对提高预测准确性与收敛速度问题,建立了基于RBF神经网络的预测模型。该模型具有最佳函数逼近性能和全局最优特性,适于预测计算,但有参数确定与优化的难题。提出一种基于非线性学习因子调节的粒子群优化（NLA-PSO）算法应用于RBF神经网络的参数优化,进而提高铁路货运量预测的精度与效率。通过1992--2011年铁路货运量预测的实例验证,将仿真结果与其他算法进行了比对,证明了方法的预测精度与收敛速度均优于其他算法,在铁路货运量预测计算上有效可行。     

基于双论域粗糙集的快捷货物运输方案选择

针对多种不确定因素下的快捷货物运输方案决策问题,提出一种基于双论域直觉模糊粗糙集的快捷货物运输方案决策模型与决策规则。依据双论域直觉模糊粗糙集理论来确定快捷货物运输方案决策的双论域模糊近似空间。将固定成本、运输成本、转运成本、碳排放、转运时间等评价指标的消耗程度视为直觉模糊数,利用评价指标与运输方案之间的直觉模糊关系计算求得下近似集与上近似集,并引入最大直觉性指标及海明贴近度得出运输方案决策规则。以兰州至北京的一条快捷货物运输线路为例,依据决策规则从公路、普铁、航空组合出的9种运输方式中选择出最优运输方案。对运输成本、转运成本进行灵敏度分析以验证结果的准确性。最终选择出的两种最优运输方案表明了双论域直觉模糊粗糙集在此类问题上的适用性。     

Multi-route railroad blocking problem by improved model and ant colony algorithm in real world

In rail freight transportation, general merchandise freight cars may pass through many classification stations on their route from origin to destination. The Railroad Blocking Problem (RBP) is to reclassify inbound traffic from various origins in the classification stations and put them on outbound trains with the same or close destinations, the objective of the RBP is to minimize the total operating costs of delivering all traffic on the railway network while satisfying the resource and capacity constraints at the stations and the priority constraints for shipments. In this paper, we introduce a new mathematic model which can comprehensively describe the blocking strategy and various combinations of multi-route O–D pairs in large scale railway network. Furthermore, we propose an improved Ant Colony (AC) algorithm for RPB, and a computational experiment derived from the real life instances of coal heavy haul rail network in north China is given. Experimental results verified the validation of the model and effectiveness of the algorithm.     

Application of a combined fuzzy multiple criteria decision making and optimization programming model to the container transportation demand split

The container transportation demand split is one of the most important decision issues for government transportation departments and port organizations. In previous studies, many researchers assumed that the shipping carrier would aim to minimize the total operation cost by selecting an appropriate port as the most favorable one to call, and the shipper would aim to minimize the inland freight cost by selecting the nearest port as the most favorable one to import and export international trade containers. Thus, a number of mathematical programming models have been developed. But in practice, the shipping carrier not only aims to minimize the total operation cost but also takes into account other criteria such as the volume of containers and port facility conditions when choosing an appropriate port as the most favorable one to call. The shipper not only aims to minimize the inland freight cost but also takes into account the frequency of ship callings when choosing an appropriate port as the most favorable one to import and export international trade containers.Thus, the purpose of this paper is to formulate a combined fuzzy multiple criteria decision making and optimization programming model for solving the container transportation demand split problem. There are two stages in this combined model: in stage one, we first compute the container transportation demand split rate by using fuzzy multiple criteria decision making (MCDM) method; whereas in stage two, an optimization mathematical programming network model is proposed for determining the inland origin destination (O-D) of import/export containers. The utilization of the proposed model is demonstrated with a case of Taiwanese ports. The results show that the proposed combined fuzzy MCDM and optimization programming model can be used to explain the container transportation demand split practice.     

Cooperation and profit allocation in two-echelon logistics joint distribution network optimization

Collaborative two-echelon logistics joint distribution network can be organized through a negotiation process via logistics service providers or participants existing in the logistics system, which can effectively reduce the crisscross transportation phenomenon and improve the efficiency of the urban freight transportation system. This study establishes a linear optimization model to minimize the total cost of two-echelon logistics joint distribution network. An improved ant colony optimization algorithm integrated with genetic algorithm is presented to serve customer clustering units and resolve the model formulation by assigning logistics facilities. A two-dimensional colony encoding method is adopted to generate the initial ant colonies. Improved ant colony optimization combines the merits of ant colony optimization algorithm and genetic algorithm with both global and local search capabilities. Finally, an improved Shapley value model based on cooperative game theory and a cooperative mechanism strategy are presented to obtain the optimal profit allocation scheme and sequential coalitions respectively in two-echelon logistics joint distribution network. An empirical study in Guiyang City, China, reveals that the improved ant colony optimization algorithm is superior to the other three methods in terms of the total cost. The improved Shapley value model and monotonic path selection strategy are applied to calculate the best sequential coalition selection strategy. The proposed cooperation and profit allocation approaches provide an effective paradigm for logistics companies to share benefit, achieve win–win situations through the horizontal cooperation, and improve the negotiation power for logistics network optimization.     

集成智能自适应货运量预测算法研究

在深入分析各种货运量预测模型的基础上,首先提出了基于云理论的新的不确定性推理模型——T-S-C-Y模型;然后基于灰色关联理论对货运量及其相关因素进行了灰关联分析,并确定影响货运量诸多因素的灰关联序;在此基础上结合一维T-S-C-Y模型和径向基函数神经网络建立了货运量预测模型对河南省1997~2002年的货运量进行预测。该预测模型既能很好地集成概念的模糊性和随机性,同时又具有自学习、自组织、自适应和强的容错能力,而且预测输出结果为置信区间值,能够很好地适应货运量的高度不确定性。结果表明模型的预测值基本上与实际值吻合,是一更加科学、合理的预测模型。     

云计算中基于排队系统的任务调度模型研究

为缩短云计算中任务调度过程任务等待时间及提高虚拟机任务调度系统的执行效率,提出一种云环境下基于 排队系统的任务调度模型。对该模型中系统稳态分布和条件随机分解结果进行了分析,给出该模型的稳态队长的随机分解和稳态等待时间,结合数值例子,准确的找到服务率与期望队长、期望等待时间及其它性能指标之间的关系。通过云任务调度系统的仿真,实验结果验证了该模型能够快速地完成云任务的调度,提高了虚拟机资源的平均利用率。     

绿色农产品供应链网络智能优化

近年来我国绿色农产品供应链网络快速发展,但效率低下、物流成本高,亟需建立智能化的绿色农产品供应链网络。粒子群优化PSO算法是基于群体智能的随机优化技术,被广泛应用于各类问题求解。为减少总运输成本．实现高效管理,提出一种绿色农产品供应链网络优化模型,并采用PSO算法进行优化求解。实验结果表明该模型切实可行．算法实现绿色农产品供应链网络智能调度。     

考虑供应商选择的选址一库存一路径的联合优化

针对装配型制造企业供应链集成优化问题,建立了随机需求情形下整合供应商选择和各层级之间运输方式选择的多层级选址—库存模型。该模型通过对供应商的选择,装配厂和分销中心的选址,相邻两层级之间的分配服务关系及运输方式的确定,实现整体供应链网络成本最小化。为求解此混合整数非线性规划模型,设计了一种矩阵编码的改进自适应遗传算法。仿真实验表明,该算法的解的寻优能力明显优于标准遗传算法,得出了供应链总成本与装配厂的最大提前期存在一定规律性的结论。     

多级多商品流物流网络设计的优化模型与组合模拟退火算法*

以最小化系统内的建设费用、库存费用和运输费用为优化目标,建立可描述一般性带能力约束的多级多商品流物流网络设计问题的优化模型,并根据物流网络设计问题的特点,基于模拟退火算法的思想,设计组合模拟退火算法对模型进行求解。算例的计算结果表明,该优化方法运算快捷,结果正确合理,能为此类物流网络设计问题提供科学的决策依据;通过分析发现,存储费用和运输费用都会影响物流网络系统的总费用,但运输费用对系统总费用的影响程度相对要大得多。     

Simulation-based multi-objective model for supply chains with disruptions in transportation

Unpredictable disruptions (e.g., accidents, traffic conditions, among others) in supply chains (SCs) motivate the development of decision tools that allow designing resilient routing strategies. The transportation problem, for which a model is proposed in this paper, consists of minimizing the stochastic transportation time and the deterministic freight rate. This paper extends a stochastic multi-objective minimum cost flow (SMMCF) model by proposing a novel simulation-based multi-objective optimization (SimMOpt) solution procedure. A real case study, consisting of the road transportation of perishable agricultural products from Mexico to the United States, is presented and solved using the proposed SMMCF-Continuous/SimMOpt solution framework. In this case study, time variability is caused by the inspection of products at the U.S.-Mexico border ports of entry. The results demonstrate that this framework is effective and overcomes the limitations of the multi-objective stochastic minimum cost flow problem (which becomes intractable for large-scale instances).     

Automatic Control of the Freight Train Makeup

Construction of the stochastic models of routing the railway freight cars descending from the yard hump was considered. The models enable automatic control of railway train makeup.     

考虑商业信誉影响的海运集装箱超订模型

对于运输能力具有随机性的海运集装箱舱位超订问题,将运输能力假定为离散型的随机变量,考虑超订水平对商业信誉的影响,在具有空箱调运需求的情况下建立了以期望总成本最小化为目标的超订模型。基于混沌优化算法给出了求解最优超订水平的方法。当预订舱位的重箱出现比率为服从均匀分布的随机变量时,证明了超订模型最优解的存在性和唯一性。最后的算例表明了该方法的有效性。     

A Train Dispatching Model Under a Stochastic Environment: Stable Train Routing Constraints and Reformulation

After major capacity breakdown(s) on a railway network, train dispatchers need to generate appropriate dispatching plans to recover the impacted train schedule from perturbations and minimize the expected total train delay time under stochastic scenarios. In this paper, we propose a cumulative flow variables-based integer programming model for dispatching trains under a stochastic environment on a general railway network. Stable Train Routing (STR) constraints are introduced to ensure that trains traverse on the same route across different capacity breakdown scenarios, which are further reformulated to equivalent linear inequality constraints. Track occupancy and safety headways are modelled as side constraints which are dualized through a proposed Lagrangian relaxation solution framework. The original complex train dispatching problem is then decomposed to a set of single-train and single-scenario optimization subproblems. For each subproblem, a standard label correcting algorithm is embedded for finding the time dependent least cost path on a space-time network. The resulting dual solutions can be transformed to feasible solutions through priority rules. Numerical experiments are conducted to demonstrate the efficiency and effectiveness of the proposed solution approach.     

基于GS-LSTM模型的铁路货运量预测

铁路货运量预测的准确性对铁路运输企业制定营销计划和营销决策来说是必要的,尤其是短期铁路货运量的影响至关重要。为了提高铁路货运量的预测精度,提出一种优化长短期记忆网络（Long-Short Term Memory, LSTM）参数的预测模型——GS-LSTM模型,通过利用网格搜索算法（Grid Search）对LSTM模型训练网络中最主要的参数（批量大小、隐含层神经单元个数和学习率）进行优化。基于2005年1月—2021年7月的铁路货运量月度数据,首先建立BP和LSTM模型对预测结果进行比较,LSTM模型比BP模型的MAPE降低1.55个百分点,然后分别对BP和LSTM模型的网络参数进行优化后再进行比较,优化后的2种模型比基础模型的预测效果均有提高,而且优化后的LSTM模型比BP模型的MAPE又进一步降低0.18个百分点。实验结果显示,优化后的LSTM模型预测效果更佳,泛化能力更好,具有很好的研究和使用价值。     

铁路集装箱中心站最佳通道数设计问题研究

铁路集装箱中心站大门通道数量优化设计问题,直接关系到整个系统的运作效率,传统的研究多采用数学建模方法,而基于运作成本角度的系统细节忽略较多,造成系统不全面。根据排队系统理论(M/M/1和M/M/S),分析了铁路集装箱中心站大门集装箱卡车服务规则与到达时间分布,考虑排队等待时间,建立基于随机服务系统(排队论)的铁路集装箱中心站大门最佳通道数计算仿真模型,并利用eM-Plant仿真软件系统对模型的实体、事件、活动和进程四个要素进行仿真。通过一个算例仿真了系统运行情况,分析了不同数量通道数对中心站运作成本的影响。仿真结果表明,可为铁路集装箱中心站大门通道数优化设计提供理论依据。     

考虑客户聚类与产品回收的两级闭环物流网络选址-路径优化

针对目前不合理的废旧产品回收以及物流活动产生的碳排放污染，提出了一种考虑客户聚类与产品回收的两级闭环物流网络选址-路径优化模型。首先，结合实际物流网络的动态性假设客户需求量和回收率的不确定性特征，以最小运营成本和最小环境影响为目标建立选址-路径优化模型；其次，对多目标进化算法进行改进，提出了考虑客户聚类结果的两级物流设施选址-路径问题求解算法；最后，对该优化算法进行算法性能分析，并以重庆市某企业为例进行了模型和算法验证。结果表明，所建立的模型和算法能有效降低决策难度并提高物流系统的运作效率，所求出的优化方案能减少物流运作成本和降低物流运输过程对环境的影响。     

Optimal facility location with random throughput costs

In this paper we consider the optimal location and size of facilities where the throughput costs for each facility are random. Given a set of origins and a set of destinations, we want to determine the optimal location and size of a set of intermediate facilities in order to minimize the expected total generalized transportation cost. The generalized transportation cost of a freight unit from an origin to a destination passing through a facility is the sum of two terms: the transportation cost from the origin to the destination through the facility and the throughput cost of the facility. While the first term is deterministic, the second one is stochastic with a Gumbel probability distribution. Looking for the expected value of the optimal solution, a mixed deterministic nonlinear problem for the optimal location of the facilities is derived. Two heuristics, which give very good approximations to the optimum, are proposed.