Efficiency of routing and scheduling system for small and medium size enterprises utilizing vehicle location data

The routing and scheduling for trucks and vans in an urban road network depends critically on the state of the road network. Trucks and vans impose significant costs on other road users and the environment, so improved routing and scheduling benefits more than just the logistics industry. However, small and medium size enterprises (SMEs) in the logistics business cannot justify investment in planning systems. In this paper, an autonomous routing and scheduling system which is available to SMEs is proposed and the efficiency of the system is investigated. The proposed system accumulates vehicle location data in a central server and uses it to generate traffic information. Test simulations using a grid network demonstrate the effects of utilizing and sharing vehicle location data on delivery efficiency. The simulation results show that the improvement of delivery efficiency is mainly due to the reduction of penalty cost for early and late arrival at the customer location. It is also shown that the system leads to the buffer effect from variations in traffic conditions on delivery cost and this effect is enhanced by taking travel time uncertainty into consideration. It is further shown that the presence of measurement periods with insufficient data results in unreliable routing and scheduling. For a reliable system, data collection over a wider area is required rather than dense data in a subset of links.     

Nonlinear programming methods based on closed-form expressions for optimal train control

This paper proposes a novel approach to solve the complex optimal train control problems that so far cannot be perfectly tackled by the existing methods, including the optimal control of a fleet of interacting trains, and the optimal train control involving scheduling. By dividing the track into subsections with constant speed limit and constant gradient, and assuming the train’s running resistance to be a quadratic function of speed, two different methods are proposed to solve the problems of interest. The first method assumes an operation sequence of maximum traction – speedholding – coasting – maximum braking on each subsection of the track. To maintain the mathematical tractability, the maximum tractive and maximum braking functions are restricted to be decreasing and piecewise-quadratic, based on which the terminal speed, travel distance and energy consumption of each operation can be calculated in a closed-form, given the initial speed and time duration of that operation. With these closed-form expressions, the optimal train control problem is formulated and solved as a nonlinear programming problem. To allow more flexible forms of maximum tractive and maximum braking forces, the second method applies a constant force on each subsection. Performance of these two methods is compared through a case study of the classic single-train control on a single journey. The proposed methods are further utilised to formulate more complex optimal train control problems, including scheduling a subway line while taking train control into account, and simultaneously optimising the control of a leader-follower train pair under fixed- and moving-block signalling systems.     

改进型EDF算法在计算机联锁全电子执行单元中的应用研究

针对计算机联锁全电子执行单元的安全性、可靠性、大数据量传输的要求,结合CAN总线的特点,提出一种基于指数分区的EDF动态调度算法,以此来提高CAN总线的实时性以及全电子执行单元发送消息的成功率。对改进后的EDF算法的编码进行详细的描述,通过使用量化误差的概念,推导任务的可调度性公式,以此来衡量算法的优化程度。使用Matlab中的truetime工具箱和全电子执行单元的通信数据,对该算法进行仿真实验,并与平均分区的EDF算法进行比较。仿真结果表明:本文算法能够平衡高低优先级的关系;发送消息的成功率更高。改进后的调度算法能够实现消息的动态调度,良好地解决了计算机联锁全电子执行单元中CAN总线网络资源不足时任务不可调度的问题。     

Incorporating space–time constraints and activity-travel time profiles in a multi-state supernetwork approach to individual activity-travel scheduling

Activity-travel scheduling is at the core of many activity-based models that predict short-term effects of travel information systems and travel demand management. Multi-state supernetworks have been advanced to represent in an integral fashion the multi-dimensional nature of activity-travel scheduling processes. To date, however, the treatment of time in the supernetworks has been rather limited. This paper attempts to (i) dramatically improve the temporal dimension in multi-state supernetworks by embedding space–time constraints into location selection models, not only operating between consecutive pairs of locations, but also at the overall schedule at large, and (ii) systematically incorporate time in the disutility profiles of activity participation and parking. These two improvements make the multi-state supernetworks fully time-dependent, allowing modeling choice of mode, route, parking and activity locations in a unified and time-dependent manner and more accurately capturing interdependences of the activity-travel trip chaining. To account for this generalized representation, refined behavioral assumptions and dominance relationships are proposed based on an earlier proposed bicriteria label-correcting algorithm to find the optimal activity-travel pattern. Examples are shown to demonstrate the feasibility of this new approach and its potential applicability to large scale agent-based simulation systems.     

Unraveling the relationship between trip chaining and mode choice: evidence from a multi-week travel diary

Abstract

Trip chaining (or tours) and mode choice are two critical factors influencing a variety of patterns of urban travel demand. This paper investigates the hierarchical relationship between these two sets of decisions including the influences of socio-demographic characteristics on them. It uses a 6-week travel diary collected in Thurgau, Switzerland, in 2003. The structural equation modeling technique is applied to identify the hierarchical relationship. Hierarchy and temporal consistency of the relationship is investigated separately for work versus non-work tours. It becomes clear that for work tours in weekdays, trip-chaining and mode choice decisions are simultaneous and remain consistent across the weeks. For non-work tours in weekdays, mode choice decisions precede trip-chaining decisions. However, for non-work tours in weekends, trip-chaining decisions precede mode choice decisions. A number of socioeconomic characteristics also play major roles in influencing the relationships. Results of the investigation challenge the traditional approach of modeling mode choice separately from activity-scheduling decisions.     

基于ITS的公共交通换乘等待时间最短调度问题研究

为使公共交通的换乘时间实现最佳衔接,先进的公共交通系统的高效率和优势得以体现,在对各国研究现状分析的基础上,研究了ITS实时提供公共交通信息的条件下,公共交通换乘等待时间最短调度问题。为了对公共交通换乘总等待时间最短目标下的公共汽车发车时刻表确定方法进行分析,应用运筹学理论,分别针对一个换乘点和多个换乘点的情况建立了线性规划模型,并用一个换乘点的算例验证了模型的可行性和有效性。结果表明：该方法能最大限度地减少乘客换乘总等待时间,尤其是对于发车间隔较大的公共交通方式间换乘的时间节约效果更为明显。     

The role of anticipated time pressure in activity scheduling

In the present article we focus on the cost or disutility of engaging in activities arising from the time pressure people frequently experience when they have committed themselves to perform too many activities in a limited amount of time. Specifically, we propose that anticipated time pressure increases the likelihood of two types of planning, one short-term and the other long-term encompassing different strategies for eliminating or deferring activities. In addition, we discuss several behaviorally realistic such strategies. It is assumed that strategies differ depending on whether an activity satisfies physiological needs, is performed because of institutional requirements or social obligations, or is performed because of psychological or social motives. Strategies are also assumed to differ depending on the degree to which planning is feasible. Computer simulations of available activity data are presented to illustrate consequences of the different strategies on time pressure and activity agendas.     

生产管理中一类排序问题的扩展模型及通用算法

讨论了生产管理中将n个零件安排给m台机器加工的一类排序问题。取消了m≤n的限制，允许机制空闲并引入负荷（最大加工零件数）的思想，得出了此类排序问题的扩展模型以及通用求解算法。算便结果表明该扩展模型和求解算法的有效性。     

区段站阶段计划的优化模型和算法

以区段站阶段内准点满轴发车为目标,建立混合０－１规划模型,并根据大系统理论将系统分解为４个有序的子系统,同时给出分解算法,为阶段计划的计算机自动编制提供了一个优化决策基础。