结构构件疲劳损伤累积的可靠性分析

在目前在船舶与海洋工程结构疲劳可靠性分析中所采用的Ｗｉｒｓｃｈｉｎｇ模型进行了较系统的分析，指出了该模型待改进的主要问题。然后，基于二维概率ＭＩＮＥＲ准则，建立了在变幅载荷谱或随机时间历程载荷谱给定的条件下，对结构构件的疲劳损伤累积进行可靠性分析的新模型。     

城市交叉口的闯红灯违规分析研究

闯红灯冲突比起交叉路口所有冲突占有比较高的伤害百比率。通过对西安部分城市交叉路口机动车辆违规行为的调查分析，对设置有全红灯间隔、没有设置全红灯间隔以及是否安装闯红灯自动检测系统的交叉口进行了比较研究，分析了闯红灯违规的特点，研究分析表明在信号灯相位及其间隔设置合理的前提下，实施安装自动闯红灯监测系统可以明显减少闯红灯行为的发生，闯红灯自动检测系统机动车驾驶员的行为规范得到改善。还给出了黄灯信号期通过交叉口车辆的微观仿真建模及其实验结果，初步探讨了减少闯红灯违规的有效措施。     

Short-term shift setting and manpower supplying under stochastic demands for air cargo terminals

A good air cargo terminal manpower supply plan helps terminals deal efficiently with their cargos and reduces their operating costs. To design a good air cargo terminal manpower supply plan, a terminal has to consider not only its operating costs, but also the uncertainty of the manpower demand in actual operations. However, most air cargo terminals in Taiwan currently depend on staff experience with a fixed demand when establishing the manpower supply plan, which is neither effective nor efficient. We have developed two stochastic-demand manpower supply plan models for air cargo terminals that can resolve stochastic demands occurring in practice. The objectives of both models are to minimize the total man-hour cost, subject to the related operating constraints. The models are formulated as integer/mixed integer linear programs. To evaluate the two stochastic-demand models under stochastic demands, we have also developed two deterministic-demand manpower supply plan models, by suitably modifying two stochastic-demand models, respectively, and an evaluation method. Here, we perform a case study using real operating data from a Taiwan air cargo terminal. The preliminary results are good, showing that the models could be useful for planning air cargo terminal manpower supply.

高速铁路隧道洞口地形对微压波的影响分析

高速列车进入隧道后将产生一系列的空气动力学效应,其中隧道出口的微压波效应对人类环境的危害性较大。影响微压波的因素主要有:列车进入隧道的速度、隧道的阻塞比、隧道长度、隧道内部条件和隧道出口地形等。采用数值模拟方法,深入研究了隧道出口地形对微压波的影响,得到了隧道出口地形对微压波的影响特性。     

基于随机Petri网的GSM-R越区切换成功率的形式化分析

首先建立了基于随机Petri网的GSM-R越区切换模型,在模型中综合考虑了信道故障、信道占用和越区切换参数配置不合理等影响越区切换成功率的因素.其次,说明了随机Petri网与马尔可夫链的关系,以及使用马尔可夫链分析“GSM-R越区切换的随机Petri网模型”的方法.最后,利用SPNP工具分析了影响GSM-R越区切换成功率的因素,发现GSM-R小区预留信道越多,越区切换成功率越高;列车运行速度越高,越区切换成功率越低;在一定范围内,列车追踪间隔越大,越区切换成功率就越高     

Modeling and Analysis of Logistics System Based on Stochastic Petri Net under Supply Chain Circumstances

In order to describe the impacts of adjustment ability of logistics system on the profit and cost of logistics system, we constructed a model of logistics system based on stochastic Petri net (SPN), and proposed a theorem for the adjustment ability of logistics system under supply chain circumstances on condition that the payoff of supply chain is a steadily increasing process. Then we put forward that a sequence of MGF (moment generating function) of out-of-goods risk process for logistics system is a martingale. The model we proposed can overcome shortcomings of the previous ones that can not describe the characteristics of the adjustment ability of logistics system. The results of simulation experiments are consistent with theoretical analysis.     

Dynamic stochastic transit assignment with explicit seat allocation model

This paper proposes a stochastic dynamic transit assignment model with an explicit seat allocation process. The model is applicable to a general transit network. A seat allocation model is proposed to estimate the probability of a passenger waiting at a station or on-board to get a seat. The explicit seating model allows a better differentiation of in-vehicle discomfort experienced by sitting and standing passengers. The paper proposes simulation procedures for calculating the sitting probability of each type of passengers. A heuristic solution algorithm for finding an equilibrium solution of the proposed model is developed and tested. The numerical tests show significant influences of the seat allocation model on equilibrium departure time and route choices of passengers. The proposed model is also applied to evaluate the effects of an advanced public transport information system (APTIS) on travellers’ decision-making.     

Norm approximation method for handling traffic count inconsistencies in path flow estimator

Path flow estimator (PFE) is a one-stage network observer proposed to estimate path flows and hence origin–destination (O–D) flows from traffic counts in a transportation network. Although PFE does not require traffic counts to be collected on all network links when inferring unmeasured traffic conditions, it does require all available counts to be reasonably consistent. This requirement is difficult to fulfill in practice due to errors inherited in data collection and processing. The original PFE model handles this issue by relaxing the requirement of perfect replication of traffic counts through the specification of error bounds. This method enhances the flexibility of PFE by allowing the incorporation of local knowledge, regarding the traffic conditions and the nature of traffic data, into the estimation process. However, specifying appropriate error bounds for all observed links in real networks turns out to be a difficult and time-consuming task. In addition, improper specification of the error bounds could lead to a biased estimation of total travel demand in the network. This paper therefore proposes the norm approximation method capable of internally handling inconsistent traffic counts in PFE. Specifically, three norm approximation criteria are adopted to formulate three L_p-PFE models for estimating consistent path flows and O–D flows that simultaneously minimize the deviation between the estimated and observed link volumes. A partial linearization algorithm embedded with an iterative balancing scheme and a column generation procedure is developed to solve the three L_p-PFE models. In addition, the proposed L_p-PFE models are illustrated with numerical examples and the characteristics of solutions obtained by these models are discussed.     

Joint leisure travel optimization with user-generated data via perceived utility maximization

The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users’ willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users’ preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals’ satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.     

Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.