2021 9th International Conference on Traffic and Logistic Engineering (ICTLE)最新文献_第2页

Continuous Improvement Integrating Technological Tools to Assertively Accelerate Decision-making of Logistics. Case Implemented in a Construction Materials Supplier Company 整合技术工具，持续改进，果断加快物流决策。案例在某建筑材料供应商公司实施

2021 9th International Conference on Traffic and Logistic Engineering (ICTLE) Pub Date : 2021-08-09 DOI: 10.1109/ICTLE53360.2021.9525785

Armando Segura-Henríquez, A. Troncoso-Palacio, Javier Vera-Ruíz, Felipe Muñoz-La-Rivera

{"title":"Continuous Improvement Integrating Technological Tools to Assertively Accelerate Decision-making of Logistics. Case Implemented in a Construction Materials Supplier Company","authors":"Armando Segura-Henríquez, A. Troncoso-Palacio, Javier Vera-Ruíz, Felipe Muñoz-La-Rivera","doi":"10.1109/ICTLE53360.2021.9525785","DOIUrl":"https://doi.org/10.1109/ICTLE53360.2021.9525785","url":null,"abstract":"Considering that many of the logistics infrastructure designs around the world are often sup-ported by studies with various computational tools, but most of these solutions are using in isolation and little understandably. Therefore, it is proposed, to develop this research based on a Logistics Reference Model, which will allow, visualize, manage and analyze the different processes and logistical scenarios of the system, with in aim to execute the best cost-benefit strategy in a company dedicated to the distribution of construction materials. By implementing this methodology, the management of the company studied was able to make the best decision for the structuring of its processes in the area of picking and dispatch. The results showed a 50% re-duction in inventory review time, equal an increase in reliability 7% that leaves the company in around location close to 85.68%; a decrease in cycle time in each order between 20% and 40% which positively impacted the customer service level. In addition, a decrease in lead times for the receipt of materials to suppliers was achieved between 15% and 30%, and a decrease in the number of warehouses, went having from 5 independent to maintain one single distribution center.","PeriodicalId":199084,"journal":{"name":"2021 9th International Conference on Traffic and Logistic Engineering (ICTLE)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130495444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

The Calculation Method and the Design of the Warning System for the Dangerous Area of the Inner Wheel difference of the Vehicle 车辆内轮差危险区域预警系统的计算方法与设计

2021 9th International Conference on Traffic and Logistic Engineering (ICTLE) Pub Date : 2021-08-09 DOI: 10.1109/ICTLE53360.2021.9525692

Wang Tianchen, Zhang Xiaoyu, Xin Yahang, Lou Jiaming, Yang Wendi

引用次数: 1

Research and Optimization of Urban Rail Transit Handover Based on WLAN 基于WLAN的城市轨道交通切换研究与优化

2021 9th International Conference on Traffic and Logistic Engineering (ICTLE) Pub Date : 2021-08-09 DOI: 10.1109/ICTLE53360.2021.9525657

Wang Chenglong, Wang Yeli, Wang Xinji

{"title":"Research and Optimization of Urban Rail Transit Handover Based on WLAN","authors":"Wang Chenglong, Wang Yeli, Wang Xinji","doi":"10.1109/ICTLE53360.2021.9525657","DOIUrl":"https://doi.org/10.1109/ICTLE53360.2021.9525657","url":null,"abstract":"In order to adapt to the development trend of urban rail transit systems, the traditional IEEE 802.11 standard handover method is aimed at the problems of high handover delay and ping-pong handover. The train control system CBTC (Communication Based Train Control) handover is studied. This paper studies the WLAN (Wireless Local Area Network) handover strategy combined with DQN (Deep Q-Network) under the WLAN urban rail transit model. This strategy extracts and inputs train driving characteristic status information, and performs switching actions based on the signal strength of the serving AP and the target AP. Finally, after deep neural network training, the best switching points at different speeds are obtained. The optimal value obtained by the DQN algorithm is substituted into the model for simulation verification. The results show that compared with the traditional method, the throughput of this strategy is increased by 36%, and the packet delay is reduced by 55%. Prove the effectiveness of DQN algorithm in WLAN urban rail transit handover. At the same time, this strategy can obtain the optimal switching position after training at different speeds and different AP distances. This makes the strategy widely applicable and makes the WLAN urban rail transit switching system safer and more reliable.","PeriodicalId":199084,"journal":{"name":"2021 9th International Conference on Traffic and Logistic Engineering (ICTLE)","volume":"391 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124503495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on The Key Influencing Factors of Road Wear for Battery Electric Vehicles tyres 纯电动汽车轮胎道路磨损关键影响因素研究

2021 9th International Conference on Traffic and Logistic Engineering (ICTLE) Pub Date : 2021-08-09 DOI: 10.1109/ICTLE53360.2021.9525633

Rongliang Liang, Wenjun Wang, Guozhuo Wang

引用次数: 0

Research on Automatic Loading and Unloading System Compatible with Multiple Materials 多物料自动上下料系统研究

2021 9th International Conference on Traffic and Logistic Engineering (ICTLE) Pub Date : 2021-08-09 DOI: 10.1109/ICTLE53360.2021.9525663

Yueming Xu, Lei Wang, S. Ouyang, Shichang Han, Jun Zhang, S. Luo, Jiao Zhou, Guilong Liang

引用次数: 0

Sales Forecasting in the Electrical Industry - An Illustrative Comparison of Time Series and Machine Learning Approaches 电气行业的销售预测——时间序列和机器学习方法的说明性比较

2021 9th International Conference on Traffic and Logistic Engineering (ICTLE) Pub Date : 2021-08-09 DOI: 10.1109/ICTLE53360.2021.9525747

Daniel Büttner, M. Rabe

{"title":"Sales Forecasting in the Electrical Industry - An Illustrative Comparison of Time Series and Machine Learning Approaches","authors":"Daniel Büttner, M. Rabe","doi":"10.1109/ICTLE53360.2021.9525747","DOIUrl":"https://doi.org/10.1109/ICTLE53360.2021.9525747","url":null,"abstract":"Sales forecasts are required for planning resources and defining stock levels through the supply chain (SC) because demand is becoming diversified due to higher customer expectations regarding service and higher competitive pressure through products' substitution possibilities. The theoretical foundations of Time Series Analyses (TSA) started in 1927 with the work of Yule and TSA, but it still seems to be only partially established in industry. Meanwhile, the use of machine learning (ML) approaches for forecasting sales volumes has come to the fore in the current big data era due to higher data availability and computing power. In recent years, much experience and new methods have been obtained in this scientific field. Both TSA and ML will continue to play a role. The variety of methods should be tested and compared continuously in a quantitative and qualitative manner to support practical knowledge and advance forecasting. Using data from a company in the electrical industry, this paper compares sales forecasts built from TSA and ML. In addition to this illustrative analysis, general strengths and weaknesses of these sales forecasting methods are elaborated and recommendations to make this topic more assessable in practical - use are given.","PeriodicalId":199084,"journal":{"name":"2021 9th International Conference on Traffic and Logistic Engineering (ICTLE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131572297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conceptual Design of Mechatronic Systems Based on Function/Means Tree 基于功能/均值树的机电一体化系统概念设计

2021 9th International Conference on Traffic and Logistic Engineering (ICTLE) Pub Date : 2021-08-09 DOI: 10.1109/ICTLE53360.2021.9525733

Vinícius Vigolo, Karol Muñoz, V. J. De Negri

引用次数: 0

Blockchain-Based Solution for Supply Chain Traceability: The Case of SmartLog Project 基于区块链的供应链可追溯性解决方案:以SmartLog项目为例

2021 9th International Conference on Traffic and Logistic Engineering (ICTLE) Pub Date : 2021-08-09 DOI: 10.1109/ICTLE53360.2021.9525749

Riivo Pilvik, K. Kaare, O. Koppel

{"title":"Blockchain-Based Solution for Supply Chain Traceability: The Case of SmartLog Project","authors":"Riivo Pilvik, K. Kaare, O. Koppel","doi":"10.1109/ICTLE53360.2021.9525749","DOIUrl":"https://doi.org/10.1109/ICTLE53360.2021.9525749","url":null,"abstract":"The digital transformation in the logistics sector is inevitable. New blockchain technology is changing how logistics sector industries and enterprise's function having a profound impact in future. The significance of blockchain technology is recognized but the number of functioning solutions is low, they are in early stages and not ready to be widely exploited. European Territorial Cooperation Program INTERREG project SmartLog (Smart Logistics and Freight Villages Initiative) with the goal to develop Hyperledger Fabric based blockchain solution to the logistics sector. The aim of the project was to test the developed technology in two TEN-T corridors measuring the potential of saving lead times in transporting goods. To achieve and measure the above-mentioned goal the following methodology was used: an IT-platform, online questionnaire, expert interviews, data and process analyses and modelling. The results showed the possibility to shorten lead times 3-8%. This paper describes the course of the research project and presents results and conclusions achieved, also suggestions for further development.","PeriodicalId":199084,"journal":{"name":"2021 9th International Conference on Traffic and Logistic Engineering (ICTLE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120846025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Unsupervised Deep Learning of Depth, Ego-Motion, and Optical Flow from Stereo Images 无监督深度学习的深度，自我运动，光流从立体图像

2021 9th International Conference on Traffic and Logistic Engineering (ICTLE) Pub Date : 2021-08-09 DOI: 10.1109/ICTLE53360.2021.9525746

Delong Yang, Zhaohui Luo, Peng Shang, Zhigang Hu

{"title":"Unsupervised Deep Learning of Depth, Ego-Motion, and Optical Flow from Stereo Images","authors":"Delong Yang, Zhaohui Luo, Peng Shang, Zhigang Hu","doi":"10.1109/ICTLE53360.2021.9525746","DOIUrl":"https://doi.org/10.1109/ICTLE53360.2021.9525746","url":null,"abstract":"Unsupervised deep learning methods have demonstrated an impressive performance for understanding the structure of 3D scene from videos. These data-based learning methods are able to learn the tasks, such as depth, ego-motion, and optical flow estimation. In this paper, we propose a novel unsupervised deep learning method to jointly estimate scene depth, camera ego-motion, and optical flow from stereo images. Consecutive stereo images are used to train the system. After training stage, the system is able to estimate dense depth map, camera 6D pose, and optical flow by using a sequence of monocular images. No labelled data set is required for training. The supervision signals for training three deep neural networks of the system come from various forms of image warping. Due to the use of optical flow, the impact caused by occlusions and moving objects on the estimation results is alleviated. Experiments on the KITTI and Cityscapes datasets show that the proposed system demonstrates a better performance in terms of accuracy in depth, ego-motion, and optical flow estimation.","PeriodicalId":199084,"journal":{"name":"2021 9th International Conference on Traffic and Logistic Engineering (ICTLE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129124143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Research on Agricultural Products Supply Chain Traceability System: Blockchain consensus algorithm optimization 农产品供应链溯源系统研究:区块链共识算法优化

2021 9th International Conference on Traffic and Logistic Engineering (ICTLE) Pub Date : 2021-08-09 DOI: 10.1109/ICTLE53360.2021.9525744

Chunrong Song, Chen Li

引用次数: 0