导电密封胶对不锈钢车体电阻点焊焊接性能影响研究

分析了电阻点焊原理,对相同的点焊搭接方式,分别采用3种工况进行焊接试验。3种工况分别采用相同焊接参数对搭接接触面涂导电密封胶和不涂导电密封胶的试件进行焊接;优化焊接参数后,对搭接接触面涂导电密封胶的试验件进行焊接。对3种工况下完成的焊接试件进行拉伸力检测试验、金相熔核尺寸检测和缺陷检测试验,对熔核直径、拉伸力、熔核内部缺陷的试验结果进行分析。试验结果表明:导电密封胶对电阻点焊的焊接性能存在影响;通过适当优化焊接参数,可以提高涂导电密封胶试件的焊接性能,焊接质量能够满足产品焊接要求。     

共享自动驾驶汽车经营策略优化分析

研究共享自动驾驶汽车(SAV)与普通汽车共存背景下，SAV公司如何根据运营目标优化经营策略并影响通勤者出行方式选择. 假设高速公路上存在一定数量的独驾通勤者，无车通勤者在SAV和地铁之间进行权衡. 分析固定需求和弹性需求下，SAV公司追求系统总成本最小或系统净收益最大和利润最大时的最优经营策略，即SAV票价和容量，进而得到均衡时模式划分情况，SAV最优发车数，系统总成本或系统净收益，以及公司利润等指标. 算例对均衡结果进一步验证发现，SAV公司垄断经营会收取较高的票价，提供容量较小的车型. 在系统最优情形下，SAV公司无法获得正利润，需要政府补贴运营.     

A real-time adaptive signal control in a connected vehicle environment

The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.     

Modeling the impacts of mandatory and discretionary lane-changing maneuvers

In this paper, a novel mesoscopic multilane model is proposed to enable simultaneous simulation of mandatory and discretionary lane-changing behaviors to realistically capture multilane traffic dynamics. The model considers lane specific fundamental diagrams to simulate dynamic heterogeneous lane flow distributions on expressways. Moreover, different priority levels are identified according to different lane-changing motivations and the corresponding levels of urgency. Then, an algorithm is proposed to estimate the dynamic mandatory and discretionary lane-changing demands. Finally, the lane flow propagation is defined by the reaction law of the demand–supply functions, which can be regarded as an extension of the Incremental-Transfer and/or Priority Incremental-Transfer principles. The proposed mesoscopic multilane cell transmission model is calibrated and validated on a complex weaving section of the State Route 241 freeway in Orange County, California, showing both the positive and negative impact of lane changing maneuvers, e.g., balancing effect and capacity drop, respectively. Moreover, the empirical study verifies that the model requires no additional data other than the cell transmission model does. Thus, the proposed model can be deployed as a simple simulation tool for accessing dynamic mesoscopic multilane traffic state from data available to most management centers, and also the potential application in predicting the impact of traffic incident or lane control strategy.     

Generalized Maximum Benefit Multiple Chinese Postman Problem

This research is focused on a generalization on the Max Benefit Chinese Postman Problem and the multiple vehicle variant of the Chinese Postman Problem. We call this generalization, the Generalized Maximum Benefit k-Chinese Postman Problem (GB k-CPP). We present a novel Mixed Integer Programming (MIP) formulation for the GB k-CPP. Four different cases of the model are discussed. The first case, performs arc-routing with profits and assumes that the origin and destination for each vehicle is the same for each cycle and is given by the user. The next case relaxes the assumption that the origin and destination for each vehicle should be the same and allows the users to select possible origins/destinations for vehicles. Case three gets the origin for each vehicle as input and produces a solution based on finding the best destination for each vehicle. The last case, that is very general, allows the optimization model to select possibly different locations for vehicle origin and destination, during each cycle. The different cases are applied to a security patrolling case conducted on the network of University of Maryland at College Park campus and the results are compared.     

Heterogeneous hazard model of PEV users charging intervals: Analysis of four year charging transactions data

Public charging infrastructure represents a key success factor in the promotion of plug-in electric vehicles (PEV). Given that a large initial investment is required for the widespread adoption of PEV, many studies have addressed the location choice problem for charging infrastructure using a priori simple assumptions. Ideally, however, identifying optimal locations of charging stations necessitates an understanding of charging behavior. Limited market penetration of PEV makes it difficult to grasp any regularities in charging behavior. Using a Dutch data set about four-years of charging transactions, this study presents a detailed analysis of inter-charging times. Recognizing that PEV users may exhibit different charging behavior, this study estimates a latent class hazard duration model, which accommodates duration dependence, unobserved heterogeneity and the effects of time-varying covariates. PEV users are endogenously classified into regular and random users by treating charging regularity as a latent variable. The paper provides valuable insights into the dynamics of charging behavior at public charging stations, and which strategies can be successfully used to improve the performance of public charging infrastructure.     

Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data

Assessing sustainability of supply chains is a critical and increasingly complex problem. In recent years sustainability has received more attention in supply chain management (SCM) literature with triple bottom lines including social, environmental, and economic factors. Conventional data envelopment analysis (DEA) models consider decision making units (DMUs) as black boxes that consume a set of inputs to produce a set of outputs and do not take into consideration internal interactions of DMUs. Two-stage DEA models deal with such DMUs. However, existing two-stage DEA models are applicable only in technologies characterized by positive inputs/outputs. This paper aims to build and present a new two-stage DEA model considering negative input-intermediate-output data. Some numerical examples along with some theorems and properties are given to show capability of proposed method. The proposed ideas are used in a case study where 29 Iranian supply chains producing equipment of expendable medical devices are evaluated in terms of sustainability.     

A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: A case study

The management of products’ end-of-life and the recovery of used products has gained significant importance in recent years. In this paper, we address the carbon footprint-based problem that arises in a closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed of or be remanufactured to be resold as new ones. Given this environment, an optimization model for a closed-loop supply chain (CLSC) in which carbon emission is expressed in terms of environmental constraints, i.e., carbon emission constraints, is developed. These constraints aim to limit the carbon emission per unit of product supplied with different transportation modes. Here, we design a closed-loop network where capacity limits, single-item management and uncertainty on product demands and returns are considered. First, fuzzy mathematical programming is introduced for uncertain modeling. Then, the statistical approach to the possibility to synthesize fuzzy information is utilized. Therefore, using a defined possibilistic mean and variance, we transform the proposed fuzzy mathematical model into a crisp form to facilitate efficient computation and analysis. Finally, the risk caused by violating the estimated resource constraints is analyzed so that decision makers (DMs) can trade off between the expected cost savings and the expected risk. We utilize data from a company located in Iran.     

Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis

A number of approaches have been developed to evaluate the impact of land development on transportation infrastructure. While traditional approaches are either limited to static modeling of traffic performance or lack a strong travel behavior foundation, today’s advanced computational technology makes it feasible to model an individual traveler’s response to land development. This study integrates dynamic traffic assignment (DTA) with a positive agent-based microsimulation travel behavior model for cumulative land development impact studies. The integrated model not only enhances the behavioral implementation of DTA, but also captures traffic dynamics. It provides an advanced yet practical approach to understanding the impact of a single or series of land development projects on an individual driver’s behavior, as well as the aggregated impacts on the demand pattern and time-dependent traffic conditions. A simulation-based optimization (SBO) approach is proposed for the calibration of the modeling system. The SBO calibration approach enhances the transferability of this integrated model to other study areas. Using a case study that focuses on the cumulative land development impact along a congested corridor in Maryland, various regional and local travel behavior changes are discussed to show the capability of this tool for behavior side estimations and the corresponding traffic impacts.