scholarly journals How to Use Advanced Fleet Management System to Promote Energy Saving in Transportation: A Survey of Drivers’ Awareness of Fuel-Saving Factors

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Changjian Zhang ◽  
Jie He ◽  
Chunguang Bai ◽  
Xintong Yan ◽  
Jian Gong ◽  
...  
Keyword(s):  
Engine Speed ◽  
Learning Algorithm ◽  
Reliability And Validity ◽  
Third Party ◽  
Fleet Management ◽  
Gradient Boosting ◽  
Third Party Logistics ◽  
Fuel Saving ◽  
Extreme Gradient Boosting

Despite the broad application of advanced fleet management systems (FMSs) in third-party logistics (3PL) companies, there is a marginally limited understanding of how to employ them to enhance transport energy efficiency. In a case study of a Chinese 3PL company, this paper analyzed data obtained from the online FMS to assess drivers’ awareness of fuel-saving factors. A questionnaire was primarily designed to investigate the drivers’ awareness of fuel-saving factors based on the reliability and validity test. Then, Extreme Gradient Boosting (XGBoost), a machine learning algorithm, was utilized to explore the intrinsic impacts of various factors on fuel consumption with the outputs providing the evaluation basis for individual awareness of the drivers. The results show a significant deviation in the driver’s awareness of fuel-saving factors, among which the three indicators of engine speed, idling condition, and rolling without engine load are seriously underestimated, while the indicators related to the environment are seriously overestimated due to social expectations. In addition, the average speed was found to be the most important fuel-saving indicator besides the load. Based on these findings, this paper recommends that the 3PL companies choose a route with more freeways when planning, and the mileage should be controlled within 800 km as far as possible.

scholarly journals Comparison of Approaches for Urban Functional Zones Classification Based on Multi-Source Geospatial Data: A Case Study in Yuzhong District, Chongqing, China

2019 ◽  
Vol 11 (3) ◽  
pp. 660 ◽  
Author(s):  
Kai Cao ◽  
Hui Guo ◽  
Ye Zhang
Keyword(s):  
Rapid Development ◽  
Geospatial Data ◽  
Classification Model ◽  
Gradient Boosting ◽  
Support Vector ◽  
Night Time ◽  
Heat Map ◽  
Extreme Gradient Boosting ◽  
Functional Zones

Accurate and timely classification and monitoring of urban functional zones prove to be significant in rapidly developing cities, to better understand the real and varying urban functions of cities to support urban planning and management. Many efforts have been undertaken to identify urban functional zones using various classification approaches and multi-source geospatial datasets. The complexity of this category of classification poses tremendous challenges to these studies especially in terms of classification accuracy, but on the opposite, the rapid development of machine learning technologies provides us with new opportunities. In this study, a set of commonly used urban functional zones classification approaches, including Multinomial Logistic Regression, K-Nearest Neighbors, Decision Tree, Support Vector Machine (SVM), and Random Forest, are examined and compared with the newly developed eXtreme Gradient Boosting (XGBoost) model, using the case study of Yuzhong District, Chongqing, China. The investigation is based on multi-variate geospatial data, including night-time imagery, geotagged Weibo data, points of interest (POI) from Gaode, and Baidu Heat Map. This study is the first endeavor of implementing the XGBoost model in the field of urban functional zones classification. The results suggest that the XGBoost classification model performed the best and was able to achieve an accuracy of 88.05%, which is significantly higher than the other commonly used approaches. In addition, the integration of night-time imagery, geotagged Weibo data, POI from Gaode, and Baidu Heat Map has also demonstrated their values for the classification of urban functional zones in this case study.

Holistic performance management of virtual teams in third-party logistics environments

2018 ◽  
Vol 24 (3/4) ◽  
pp. 186-202 ◽  
Author(s):  
Youness Eaidgah ◽  
Amir Abdekhodaee ◽  
Manoochehr Najmi ◽  
Alireza Arab Maki
Keyword(s):  
Performance Management ◽  
Virtual Teams ◽  
Integrated Approach ◽  
Third Party ◽  
Coherent System ◽  
Third Party Logistics ◽  
Skill Levels ◽  
Content Type ◽  
High Staff Turnover

Purpose The purpose of this paper is to investigate the use of an integrated approach for performance improvement of virtual teams (VTs) in third-party logistics (3PL) through the integration of performance management (PM), visual management (VM) and continuous improvement (CI) initiatives into one coherent system. The paper will also propose a methodological framework to establish such a system. The intended integrated system is called as integrated visual management (IVM) throughout this paper. Design/methodology/approach This research is based on a case study that took place in a 3PL context with 19 VTs of different sizes spread across Australia. Many major 3PL companies provide their services either internationally or nationwide and therefore use VTs on a regular basis. The selected company does the same. This case was picked as representative of the many complexities which VTs face in 3PL settings, e.g. geographical and temporal separations; different skill levels within the team and between different team bases; multi-teaming system; high staff turnover; recurring performance problems and firefighting approach to problem-solving; and highly demanding performance requirements from clients. Further, this case study, being of a newly established contract and team, enabled the observation of the team dynamic and complexities from the earliest stages. In addition, as the main author of the paper was part of the managerial layer of the studied VT, this provided it a unique opportunity to escape the usual bureaucracy and rather focus on the research. This study also includes a literature review on VTs along with PM, VM and CI, which comprises IVM. Findings It was found that an integrated approach to PM, VM and CI was effective in systematically improving the VT performance. The framework for implementing IVM was productive and enabled to successfully plan and deploy the improvement intentions. Even though the team was highly virtual and encompassed a range of situational challenges, including different skill levels, a multi-teaming system and a high staff turnover, nevertheless, through IVM, the results met and exceeded performance targets on a sustainable base. Inventory record accuracy, dispatch on time, delivery in full on time and dock to stock were improved by 45, 62, 22 and 25 per cent on average, respectively. Originality/value The originality of the paper comes from its methodological approach to performance improvement for VTs in 3PL contexts through integrating PM, VM and CI systems into one coherent system, IVM.

scholarly journals THIRD PARTY LOGISTICS REDUCES COST, MYTH OR REALITY? CASE STUDY OF THE RETAIL INDUSTRY OF MAURITIUS

2018 ◽  
Vol 3 (3) ◽  
pp. 1284-1298
Author(s):  
Luvkishan Persand ◽  
◽  
Sachi Pertaub ◽  
Ashvin K Seenarain ◽  
◽  
...  
Keyword(s):  
Third Party ◽  
Retail Industry ◽  
Third Party Logistics

scholarly journals Detection and Identification of Organic Pollutants in Drinking Water from Fluorescence Spectra Based on Deep Learning Using Convolutional Autoencoder

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2633
Author(s):  
Jie Yu ◽  
Yitong Cao ◽  
Fei Shi ◽  
Jiegen Shi ◽  
Dibo Hou ◽  
...  
Keyword(s):  
Drinking Water ◽  
Deep Learning ◽  
Fluorescence Spectroscopy ◽  
Organic Pollutants ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Gradient Boosting ◽  
Spectral Processing ◽  
Extreme Gradient Boosting ◽  
Convolutional Autoencoder

Three dimensional fluorescence spectroscopy has become increasingly useful in the detection of organic pollutants. However, this approach is limited by decreased accuracy in identifying low concentration pollutants. In this research, a new identification method for organic pollutants in drinking water is accordingly proposed using three-dimensional fluorescence spectroscopy data and a deep learning algorithm. A novel application of a convolutional autoencoder was designed to process high-dimensional fluorescence data and extract multi-scale features from the spectrum of drinking water samples containing organic pollutants. Extreme Gradient Boosting (XGBoost), an implementation of gradient-boosted decision trees, was used to identify the organic pollutants based on the obtained features. Method identification performance was validated on three typical organic pollutants in different concentrations for the scenario of accidental pollution. Results showed that the proposed method achieved increasing accuracy, in the case of both high-(>10 μg/L) and low-(≤10 μg/L) concentration pollutant samples. Compared to traditional spectrum processing techniques, the convolutional autoencoder-based approach enabled obtaining features of enhanced detail from fluorescence spectral data. Moreover, evidence indicated that the proposed method maintained the detection ability in conditions whereby the background water changes. It can effectively reduce the rate of misjudgments associated with the fluctuation of drinking water quality. This study demonstrates the possibility of using deep learning algorithms for spectral processing and contamination detection in drinking water.

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