允许订单延后的易腐品库存——路径问题研究

在一个供应商供应单一产品给多个零售商的一体化决策的供应链中,假设各个零售商的需求率已知,产品在零售阶段存在腐败且允许缺货,优化的目标是确定为每个零售商送货的配送路径、配送周期以及不缺货时间。文章通过建立数学模型,证明了目标函数的性质,给出了不考虑约束时最优解满足的条件;以改进的节约算法生成路径,结合各极值点的特定约束,设计求解步骤对该问题进行了求解;通过算例说明了允许缺货对最优配送策略的影响。     

基于ERP平台的物流配送路径优化

物流配送具有终端多、分布广、批量小、批次多、货物种类繁多、时间紧、车辆路径问题复杂等特点.以往企业对供应链整合缺乏系统性认识,对ERP平台下配送的特殊性了解不够.文章通过建立适合ERP平台下的路径优化模型,解决在保证商品准时到达客户指定点的前提下,尽可能的减少运输的车次和运输的总路程.     

基于模糊多分配的辽宁煤炭枢纽站选址研究

提出一种模糊多分配p枢纽站中位问题,其中运输成本定义为模糊变量,问题的目标函数是在给定的可信性水平下,最小化总的运输成本。对于梯形和正态运输成本,问题等价于确定的混合整数线性规划问题。在实证分析中,选取了辽宁省煤炭产业的相关面板数据,分析计算在不同可信度水平下煤炭运输枢纽站设立的数量和位置,再利用传统的优化方法(如分枝定界法)求解。经计算,这一模型和求解方法可以用来解决辽宁省煤炭运输的选址问题。     

物流网络系统配送路线的优化方法

文章通过研究物流系统配送车的最优行驶路线的动态规划模型、静态规划模型及其算法,给出了物流网络系统配送路线的优化方法.     

基于化学反应算法的鲜活农产品配送路径优化

文章通过引入时间价格因子考虑在鲜活农产品配送过程的损耗并结合运输成本和惩罚成本构建数学模型.从实际的运用环境出发,考虑到未在期望获得时间内达到运输地点的成本消耗,并且随着时间的推移,鲜活农产品本身也有成本损耗构建目标函数.根据该模型的特点设计化学反应的最小单元分子结构和算法步骤.运用化学反应算法可以很好地解决模型算法过早收敛于局部最优问题.通过实例验证了模型和算法的有效性和科学性.     

碳排放强制上限下的供应链生产与配送协同运作模型

文章分析了碳排放强制上限对制造商的生产、库存以及物流服务商的配送、库存和供货延迟等方面的影响.将碳排放参数融入到生产运作优化建模中,为实现供应链整体最优,构建了基于整体优化的生产与配送协同运作模型.通过对参数赋值,利用ILOG CPLEX进行模型求解,验证了可以通过供应链协同运作优化实现碳减排.研究结果表明:碳排放强制上限下调,不会对供应链总成本有明显的影响;在节能减排方面,协同运作能够比技术投资的性价比更高;生产与配送的协同运作能够以较低的成本满足碳排放强制上限限制.     

基于产业结构升级的产业竞争力定位模型

竞争力的形成与微观层次的生产率及中观、宏观层次的市场运行效率有关。根据产业结构中生产要素技术含量和生产要素的专业化程度,提出了一个产业竞争力定位及其升级路径的经验性模型,以确定区域产业结构升级方向和产业结构追赶目标。通过对国际上不同国家的产业竞争力定位及中国不同地区产业竞争力定位的实证分析,认为模型与经验基本吻合。     

基于逆优化方法的供应链网络结构优化配置

供应链网络结构优化能够实现设施合理布局和产能合理分配,文章以供应链运营成本整体最优为原则,以线性规划思路建立供应链网络结构优化模型,并基于逆优化的思路,即通过对网络结构优化模型中的参数进行调整和优化,为供应链在满足客户需求前提下建立动态应对策略提供路径。     

融合客户满意度的供应链动态优化

文章在综合考虑商品在供应链中不同环节的动态生产成本、运输成本、库存成本、销售价格等,同时将客户满意度作为决策变量融入到供应链的物流成本结构体系内,建立基于客户满意度的供应链动态优化模型,并进行优化计算与求解.同时,以实例仿真来检验:在一定的客户满意度下,以供应链整体利润最大化为供应链动态优化的优化目标,不仅可使供应链获取较高的利润值,而且客户满意度也能克服设定约束而达到更高值.     

有害物品运输风险度量过程中的计算误差分析

有害物品运输与其它物品运输的区别在于运输过程中事故发生的相关风险。从运输路径选择角度看,有害物品运输问题通常归结为以运输风险和运输成本为目标的双目标问题。显然,运输风险度量便成为运输路径选择的基础,其精确与否直接影响到运输路径的选择结果。文章分析了传统风险模型在事故影响后果估计、事故概率的近似计算以及离散化处理等方面存在的计算误差,并分别对其影响路径运输风险的程度进行了评价。结果表明,在传统风险模型中,影响后果估计的误差有可能比较明显,且纠正成本较高;而后两类误差实际上很小,不会影响计算结果的应用。对于其它几种常用的风险度量模型,其计算误差的情形与传统风险模型的相关结论基本一致。     

Two Applications of Statistics to Traffic Models

Two statistical applications for estimation and prediction of flows in traffic networks are presented. In the first, the number of route users are assumed to be independent α-shifted gamma Γ(θ, λ₀) random variables denoted H(α, θ, λ₀), with common λ₀. As a consequence, the link, OD (origin-destination) and node flows are also H(α, θ, λ₀) variables. We assume that the main source of information is plate scanning, which permits us to identify, totally or partially, the vehicle route, OD and link flows by scanning their corresponding plate numbers at an adequately selected subset of links. A Bayesian approach using conjugate families is proposed that allows us to estimate different traffic flows. In the second application, a stochastic demand dynamic traffic model to predict some traffic variables and their time evolution in real networks is presented. The Bayesian network model considers that the variables are generalized Beta variables such that when marginally transformed to standard normal become multivariate normal. The model is able to provide a point estimate, a confidence interval or the density of the variable being predicted. Finally, the models are illustrated by their application to the Nguyen Dupuis network and the Vermont-State example. The resulting traffic predictions seem to be promising for real traffic networks and can be done in real time.     

Forecasting Multivariate Road Traffic Flows Using Bayesian Dynamic Graphical Models,Splines and Other Traffic Variables

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simple model to use. While statistical flow forecasting models usually base their forecasts on flow data alone, data for other traffic variables are also routinely collected. This paper shows how cubic splines can be used to incorporate extra variables into the LMDM in order to enhance flow forecasts. Cubic splines are also introduced into the LMDM to parsimoniously accommodate the daily cycle exhibited by traffic flows. The proposed methodology allows the LMDM to provide more accurate forecasts when forecasting flows in a real high‐dimensional traffic data set. The resulting extended LMDM can deal with some important traffic modelling issues not usually considered in flow forecasting models. Additionally, the model can be implemented in a real‐time environment, a crucial requirement for traffic management systems designed to support decisions and actions to alleviate congestion and keep traffic flowing.     

Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis

Model-based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model-based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM); (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony-based approach and an evaluation graph-based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.     

On weak convergence of long-range-dependent traffic processes

A possible model for communication traffic is that the amount of work arriving in successive time intervals is jointly Gaussian. This model seems to fly in the face of certain obvious and characteristic features of real traffic, such as the fact that it arrives in discrete bundles and that there is often a non-zero probability of zero traffic in a time interval of significant length. Also, the Gaussian model allows the possibility of negative traffic, which is clearly unrealistic. As the number of sources of traffic increases and the quantity of traffic in communication networks increases, however, under suitable conditions, the deviation between the distribution of real traffic and the Gaussian model will become less. The appropriate concept of topology/convergence must be used or the result will be meaningless. To identify an appropriate convergence framework, the performance statistics associated with a network, namely cell loss, delay, and, in general, statistics which can be expressed in terms of the network buffers which accumulate in the network may be used as a guide. Weak convergence of probability measures has the property that when the probability measures of traffic processes converge to that of a certain traffic process, the distribution of their performance characteristics, such as buffer occupancy, also converges in the same sense to the performance of the system to which they were converging. Real traffic appears, unambiguously, to be long-range dependent. There is an interesting example where aggregation of traffic does not seem to produce convergence to the queueing behaviour expected of Gaussian traffic, at any rate the tail characteristics do not converge to those of the Gaussian result. However, in Section 4, it is shown that if the variance of one traffic stream is finite and as a proportion of the variance of the whole traffic volume tends to zero, then the traffic in networks can be expected to converge to Gaussian in the sense of weak convergence of probability measures. It is then shown that, as a consequence, the traffic in the paradoxical example does converge in this sense also. The paradox is explained by noticing that asymptotic tail behaviour may become increasingly irrelevant as traffic is aggregated. This fact should sound a warning concerning the cavalier use of tail-behaviour as an indication of performance. Long-range dependence apparently places no inhibition on convergence to Gaussian behaviour. Convergence to a Gaussian distribution of increasing aggregates of traffic is only shown to occur for discrete time models. In fact it appears that continuous time Gaussian models do not share this property and their use for modelling real traffic may be problematic.     

Modelling the travel time of transit vehicles in real‐time through a GTFS‐based road network using GPS vehicle locations

Predicting the arrival time of a transit vehicle involves not only knowledge of its current position and schedule adherence, but also traffic conditions along the remainder of the route. Road networks are dynamic and can quickly change from free‐flowing to highly congested, which impacts the arrival time of transit vehicles, particularly buses which often share the road with other vehicles, so reliable predictions need to account for real‐time and future traffic conditions. The first step in this process is to construct a framework with which road state (traffic conditions) can be estimated using real‐time transit vehicle position data. Our proposed framework implements a vehicle model using a particle filter to estimate road travel times, which are used in a second model to estimate real‐time traffic conditions. Although development and testing took place in Auckland, New Zealand, we generalised each component to make the framework compatible with other public transport systems around the world. We demonstrate the real‐time feasibility and performance of our approach in real‐time, where a combination of R and C++ was used to obtain the necessary performance results. Future work will use these estimated traffic conditions in combination with historical data to obtain reliable arrival time predictions of transit vehicles.     

Building and Fitting Non‐Gaussian Latent Variable Models via the Moment‐Generating Function

Abstract. For certain classes of hierarchical models, it is easy to derive an expression for the joint moment‐generating function (MGF) of data, whereas the joint probability density has an intractable form which typically involves an integral. The most important example is the class of linear models with non‐Gaussian latent variables. Parameters in the model can be estimated by approximate maximum likelihood, using a saddlepoint‐type approximation to invert the MGF. We focus on modelling heavy‐tailed latent variables, and suggest a family of mixture distributions that behaves well under the saddlepoint approximation (SPA). It is shown that the well‐known normalization issue renders the ordinary SPA useless in the present context. As a solution we extend the non‐Gaussian leading term SPA to a multivariate setting, and introduce a general rule for choosing the leading term density. The approach is applied to mixed‐effects regression, time‐series models and stochastic networks and it is shown that the modified SPA is very accurate.     

ELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORK

The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models. The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process. A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM.     

Bayesian hierarchical models for linear networks

The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two methods have been developed to achieve this aim. First, the motorway-specific intensity is estimated by using a homogeneous Poisson process. The heterogeneity across motorways is incorporated using two-level hierarchical models. The data structure is multilevel since each motorway consists of junctions that are joined by grouped segments. In the second method, the segment-specific intensity is estimated. The homogeneous Poisson process is used to model accident data within grouped segments but heterogeneity across grouped segments is incorporated using three-level hierarchical models. A Bayesian method via Markov Chain Monte Carlo is used to estimate the unknown parameters in the models and the sensitivity to the choice of priors is assessed. The performance of the proposed models is evaluated by a simulation study and an application to traffic accidents in 2016 on the UK motorway network. The deviance information criterion (DIC) and the widely applicable information criterion (WAIC) are employed to choose between models.     

南非物流成本统计方法研究

宏观物流成本是评价一个国家经济发展质量的重要指标,客观、科学地统计宏观物流成本具有非常重要的意义。在系统地介绍了南非物流成本统计模型中的分解与合计统计方法的基础上,着重介绍了合计方法。合计方法是按每一种产品的生产量分别进行统计,比较符合物流运作的实际情况,可以为中国物流成本统计方法的改进提供有益的参考和借鉴。     

基于大数据规则挖掘的交通拥堵治理研究

随着中国城市机动车保有量的急剧增多,交通拥堵已经成为现代城市病。交通拥堵在道路网络中呈现向四周放射的传导特性,拥堵路段倾向于将拥堵扩散传导到其他相邻路段,该特性此前未被系统研究过,综合比较各种方法的适用性,从时间和大数据规则挖掘角度对拥堵建模;使用时间序列规则挖掘算法建立交通拥堵传导规律模型,并基于传导规则预测未来交通流状况;更重要的是,挖掘出来的拥堵传导规则直观可用,能够用于建立拥堵预警防治机制,完善道路路网建设规划中不合理的部分,从而达到提升交通效率的目的。研究结果证明本模型能够较好达到研究目的,挖掘出的拥堵传导规则可以精确分析交通拥堵状况并预测未来交通流状况,因此可以为交通拥堵治理决策提供重要参考。