利用可编程控制器对立式车床的改造

用可编程控制器对立式车床(C5116A)进行改造,在机床原有的操作方式和机床的主电路不变的情况下,使机床的控制线路大为简化,从而使机床故障率降低.文中提出了总体构思,设计出了PLC的梯形图及接线图,列出了改造后各电器元件的代号与老图纸的代号的对照表.并介绍了部分电路的调试过程.     

平行流交叉口行人过街控制策略研究

为解决平行流交叉口行人与机动车冲突问题,考虑行人与机动车在冲突点为寻找间隙穿越的交替穿插的抢行博弈过程,重新设计行人过街信号相位方案,提出人车搭接相位行人过街模式;考虑行人专用相位过街模式,给出两种行人过街模式的信号控制策略;构建优化模型,并对模型等价变换,降低计算复杂度.结果显示：常规交叉口处于高饱和状态时,两种行人过街模式下平行流交叉口分别降低了73.8%、50.3%的车均延误,故人车搭接相位过街模式效益更优;人车搭接相位过街模式与不考虑行人过街模式相比,交叉口车均延误仅增加 0.6~3.8 s⋅ pcu-1 .研究成果可为平行流交叉口行人信号配时提供理论依据.     

横向型企业集团利润最大化的订单分配模型

当由集团公司统一承接市场订单时，订单在各成员企业问的分配是关键问题．以集团利润最大化为目标，运用优化理论建立了单目标0-1规划的订单分配模型．模型中考虑了股权、资金成本、固定运营成本等因素．对模型求解可以确定集团对市场订单的分配，并由此编制集团各成员企业的生产及运输计划．用算例说明了模型的求解方法．     

Workspace analysis to generate a collision-free torch path for a ship welding robot

We propose a method to determine the optimal initial location and to generate torch paths for a ship welding robot with 6 degrees of freedom (DOF). The optimal initial location is determined using an objective function, which is set up by combining constraints on the torch posture, manipulability, and the range of each joint angle to avoid collisions. A genetic algorithm (GA) is used to optimize the objective function because it does not require additional derivatives. After the initial location is determined, torch paths are generated by interpolating the starting point, endpoints and torch postures using inverse kinematics. Our method can be applied to automate the welding job for each block during ship building, irrespective of the shape of the robots, by changing the objective function.     

船体分段制造最低成本的二次规划模型及其求解

以分段建造费用与工时成线性关系为前提建立的船体分段制造最低成本数学模型其约束条件呈线性关系,目标函数边际收益递减,将最低成本模型改进为二次规划,利用EXCEL工具进行求解,实例计算逼近全局最优,方法简单实用.     

浙江省公路网车辆OD数据库管理系统开发研究

基于公路网OD调查数据现状管理与深度利用的需要,结合浙江省公路网车辆OD数据库管理系统的开发．对系统设计原则与开发平台、功能与框架、技术流程以及关键技术进行详细阐述,可为今后建立更科学和完善的数据库系统提供参考。     

Large-scale evacuation network optimization: a bi-level control method with uncertain arterial demand

To improve the efficiency of large-scale evacuations, a network aggregation method and a bi-level optimization control method are proposed in this paper. The network aggregation method indicates the uncertain evacuation demand on the arterial sub-network and balances accuracy and efficiency by refining local road sub-networks. The bi-level optimization control method is developed to reconfigure the aggregated network from both supply and demand sides with contraflow and conflict elimination. The main purpose of this control method is to make the arterial sub-network to be served without congestion and interruption. Then, a corresponding bi-objective network flow model is presented in a static manner for an oversaturated network, and a Genetic Algorithm-based solution method is used to solve the evacuation problem. The numerical results from optimizing a city-scale evacuation network for a super typhoon justify the validity and usefulness of the network aggregation and optimization control methods.     

An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.     

Transit network design with allocation of green vehicles: A genetic algorithm approach

The use of fossil fuels in transportation generates harmful emissions that accounts for nearly half of the total pollutants in urban areas. Dealing with this issue, local authorities are dedicating specific efforts to seize the opportunity offered by new fuels and technological innovations in achieving a cleaner urban mobility. In fact, authorities are improving environmental performances of their public transport fleet by procuring cleaner vehicles, usually called low and zero emission vehicles (LEV and ZEV, respectively). Nevertheless there seems to be a lack of methodologies for supporting stakeholders in decisions related to the introduction of green vehicles, whose allocation should be performed since the network design process in order to optimize their available green capacity.In this paper, the problem of clean vehicle allocation in an existing public fleet is faced by introducing a method for solving the transit network design problem in a multimodal, demand elastic urban context dealing with the impacts deriving from transportation emissions.The solving procedure consists of a set of heuristics which includes a routine for route generation and a genetic algorithm for finding a sub-optimal set of routes with the associated frequencies.     

Multiple criteria optimization method for the vehicle assignment problem in a bus transportation company

A vehicle assignment problem (VAP) in a road, long‐haul, passenger transportation company with heterogeneous fleet of buses is considered in the paper. The mathematical model of the VAP is formulated in terms of multiobjective, combinatorial optimization. It has a strategic, long‐term character and takes into account four criteria that represent interests of both passengers and the company's management. The decision consists in the definition of weekly operating frequency (number of rides per week) of buses on international routes between Polish and Western European cities. The VAP is solved in a step‐wise procedure. In the first step a sample of efficient (Pareto‐optimal) solutions is generated using an original metaheuristic method called Pareto Memetic Algorithm (PMA). In the second step this sample is reviewed and evaluated by the Decision Maker (DM). In this phase an interactive, multiple criteria analysis method with graphical facilities, called Light Beam Search (LBS), is applied. The method helps the DM to define his/her preferences, direct the search process and select the most satisfactory solution.