Train Dwell Time Efficiency Evaluation with Data Envelopment Analysis: Case Study of London Underground Victoria Line

2021 ◽  
pp. 036119812110566
Author(s):  
Natchaya Tortainchai ◽  
Howard Wong ◽  
David Winslett ◽  
Taku Fujiyama
Keyword(s):  
Data Envelopment Analysis ◽  
Dwell Time ◽  
Efficiency Score ◽  
Movement Direction ◽  
Data Envelopment ◽  
Time Efficiency ◽  
Movement Data ◽  
London Underground ◽  
Key Factor ◽  
Passenger Volume

Train dwell time is a complicated component and depends on many factors. One of the dominant factors is passenger volume. This study used actual train movement data and passenger demand data from London Underground, UK, to estimate the number of passengers and train dwell times at each station, and then evaluated train dwell times from a different perspective. Considering the various characteristics of stations, it is complicated to evaluate dwell time. Therefore, data envelopment analysis (DEA) was introduced to evaluate the dwell time at each station in relation to passenger volume at that station. The study investigated whether the dwell time spent at stations is efficient when considering the number of passengers that the stations can serve. The results showed that, in low-passenger-volume stations, the dwell time efficiency score is low and increases relative to the increase in passenger volume. For high-passenger-volume stations, interactions between passengers are more relevant and have a strong influence on dwell time. Passenger movement direction is a key factor to classify stations. This research proposes that stations should be classified according to their characteristics, and points out the challenge at any station with the same characteristics as Victoria station which has high passenger volume with bi-directional flow, and where trains arriving are crowded. This characteristic would result in high interactions between passengers, thus making a long dwell time. The station has to handle high passenger volume and also has to keep the dwell time within the threshold.

scholarly journals Selection and Analysis of Input-Output Variables using Data Envelopment Analysis of Decision Making Units - Indian Private Sector Banks

2021 ◽  
Vol 10 (5) ◽  
pp. 119-127
Author(s):  
B. Vittal ◽  
Raju Nellutla ◽  
M. Krishna Reddy
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Banking System ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Input Output ◽  
Key Factor ◽  
And Performance ◽  
Using Data ◽  
Decision Making Units

In banking system the evaluation of productivity and performance is the key factor among the fundamental concepts in management. For identify the potential performance of a bank efficiency is the parameter to evaluate effective banking system. To measure the efficiency of a bank selection of appropriate input-output variables is one of the most vital issues. The suitable identification of input-output variables helps to create and identify model in order to evaluate the efficiency and analysis. The Data Envelopment Analysis (DEA) is a mathematical approach used to measure the efficiency of identified Decision Making Units (DMUs). The DEA is a methodology for evaluating the relative efficiency of peer decision making units of identified input/output variables for the financial year 2018-19. In this study the basic DEA CCR, BCC models used for measure the efficiency of DMUs. In addition to these models for minimize the input excess and output shortfall Slack Based Measure (SBM) efficiency used. The SBM is a scalar measure which directly deals with slacks of input, output variables which help in obtain improved efficiency score compare with previous model. The result from the analysis is

scholarly journals Benchmarking the Hotel Industry in Oman Through a Three-Stage DEA-Based Procedure

2018 ◽  
Vol 9 (2) ◽  
pp. 5 ◽  
Author(s):  
Amar Oukil ◽  
Asma Al-Zidi
Keyword(s):  
Data Envelopment Analysis ◽  
Hotel Industry ◽  
Contextual Effects ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Sultanate Of Oman ◽  
Policy Makers ◽  
Average Efficiency ◽  
Key Factor ◽  
Positive Effect

This study is concerned with benchmarking the hotel industry in the Sultanate of Oman besides identifying the environmental factors that influence the operational efficiency of hotels. The benchmarking analysis is carried out through data envelopment analysis (DEA), used essentially to evaluate the efficiency ratios of a selected sample of 58 hotels. Although less than 23% of the hotels are found efficient, the average efficiency score of 83% indicates a reasonable efficiency in resource management for most of the hotels. Regarding the contextual effects, hotel Size, Star rating and cultural attractions are found to have the most significant effect on hotel efficiency in Oman. The positive effect of cultural attractions can inform policy makers on the necessity to preserve and promote cultural heritage as an important key factor of attraction.

Effects of Data Envelopment Analysis on Performance Assessment

2018 ◽  
pp. 557-581
Author(s):  
Heinz Ahn ◽  
Nadia Vazquez Novoa
Keyword(s):  
Data Envelopment Analysis ◽  
Financial Performance ◽  
Performance Assessment ◽  
Performance Appraisal ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Financial Assessment ◽  
Dea Efficiency ◽  
Controlling Processes

This paper examines the Data Envelopment Analysis (DEA) methodology from a cognitive perspective. Specifically, it analyzes (a) the role of DEA scores as an overall efficiency measure and (b) to what extent the presence of DEA scores for a non-financial performance appraisal influences a posterior financial performance assessment. The study confirms that the efficiency score acts as a strong performance marker when deciding on which decision making units (DMUs) should be awarded for their non-financial performance. Furthermore, it shows that the results of the non-financial performance evaluation may act as an anchor which significantly influences a posterior financial assessment. These insights have practical consequences for planning, reporting, and controlling processes that incorporate DEA efficiency scores.

scholarly journals Supporting Better Decision-Making: A Combined Grey Model and Data Envelopment Analysis for Efficiency Evaluation in E-Commerce Marketplaces

2020 ◽  
Vol 12 (24) ◽  
pp. 10385
Author(s):  
Chia-Nan Wang ◽  
Thanh-Tuan Dang ◽  
Ngoc-Ai-Thy Nguyen ◽  
Thi-Thu-Hong Le
Keyword(s):  
Data Envelopment Analysis ◽  
Business Performance ◽  
Technology Development ◽  
Technological Development ◽  
Hybrid Approach ◽  
Efficiency Score ◽  
Grey Model ◽  
Modern World ◽  
Data Envelopment ◽  
Input Variables

E-commerce has become an integral part of businesses for decades in the modern world, and this has been exceptionally speeded up during the coronavirus era. To help businesses understand their current and future performance, which can help them survive and thrive in the world of e-commerce, this paper proposes a hybrid approach that conducts performance prediction and evaluation of the e-commerce industry by combining the Grey model, i.e., GM (1, 1) and data envelopment analysis, i.e., the Malmquist-I-C model. For each e-commerce company, GM (1, 1) is applied to predict future values for the period 2020–2022 and Malmquist-I-C is applied to calculate the efficiency score based on output variables such as revenue and gross profit and input variables such as assets, liabilities, and equity. The top 10 e-commerce companies in the US market are used to demonstrate model effectiveness. For the entire research period of 2016–2022, the most productive e-commerce marketplace on average was eBay, followed by Best Buy and Lowe’s; meanwhile, Groupon was the worst-performing e-commerce business during the studied period. Moreover, as most e-commerce companies have progressed in technological development, the results show that the determinants for productivity growth are the technical efficiency change indexes. That means, although focusing on technology development is the key to e-commerce success, companies should make better efforts to maximize their resources such as labor, material and equipment supplies, and capital. This paper offers decision-makers significant material for evaluating and improving their business performance.

scholarly journals Performance Assessment Based on the Relative Efficiency of Indian Opencast Coal Mines Using Data Envelopment Analysis and Malmquist Productivity Index

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4902
Author(s):  
Biswaranjita Mahapatra ◽  
Chandan Bhar ◽  
Sandeep Mondal
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Primary Source ◽  
Efficiency Score ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Significant Difference ◽  
Using Data

Coal is the primary source of energy in India. Despite being the second-largest coal-producingcountry, there exists a significant difference in demand and production in India. In this study, the relativeefficiency of twenty-eight selected opencast mines from a large public sector undertaking coal companyin India for 2018–2019 was assessed and ranked by using data envelopment analysis (DEA). This studyused input-oriented DEA with efficiency decomposition to pure technical efficiency, technical efficiency,and scale efficiency. The result showed that 25% and 36% of mines were efficient in technical efficiencyand pure technical efficiency, respectively, whereas the eight mines scale efficiency was inefficient witha decreasing return to scale. Further, in this study, theMalmquist Productivity Index (MPI)was employedto measure the efficiency of the selected mines for three consecutive years (2016–2017 to 2018–2019).The result shows that in only three mines the efficiency is continuously improving from 2016–2017 to2018–2019, whereas in more than 20% of mines the efficiency score is decreasing. Comparing theMPIefficiency and productivity assessment throughout the years, changes in innovation and technology areincreasing from 2017–2018 to 2018–2019. Finally, the study concluded with a comprehensive evaluationof each variable with mines performance. The author formulated the strategies, which in turn help coalprofessionals to improve the efficiency of the mine.

scholarly journals Evaluating the Efficiency of Transit-Oriented Development Using Network Slacks-Based Data Envelopment Analysis

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3609 ◽  
Author(s):  
Eun Hak Lee ◽  
Hosuk Shin ◽  
Shin-Hyung Cho ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim
Keyword(s):  
Data Envelopment Analysis ◽  
Efficiency Score ◽  
Design Stage ◽  
Measure Data ◽  
Subway Station ◽  
Data Envelopment ◽  
Transit Oriented Development ◽  
Overall Efficiency ◽  
Administrative Units ◽  
Design Score

The purpose of this research is to evaluate transit-oriented development (TOD) efficiency in Seoul using the network slacks-based measure data envelopment analysis (NSBM DEA) model. The smartcard data and socio-economic data are used to evaluate the transit efficiency of 352 subway station areas in Seoul. To measure the TOD efficiency, the two-stage network is designed with the transit design stage and the transit efficiency stage. The overall efficiency score of each station area is estimated through each score of the stage. The results of the efficiency evaluation by station area indicate that the overall efficiency score average is 0.349, with the transit design score and efficiency score estimated to be 0.453 and 0.245, respectively. The results indicate that the balance of each stage is crucial to achieve an efficient station in the concept of transit efficiency. With the efficiency scores of the 352 subway station areas, the TOD efficiency is also evaluated by the administrative units in Seoul. The results of district analysis reveal that the top 10 efficient administrative units are characterized by both residential and commercial land use. The results indicate that efficiency is found to be good in areas having both residential and commercial characteristics.

scholarly journals Data envelopment analysis in measuring the efficiency of volleyball teams in Primorsko-Goranska County

2020 ◽  
Vol 8 (1) ◽  
pp. 121-134
Author(s):  
Jelena Jardas Antonić ◽  
Kristina Kregar ◽  
Nenad Vretenar
Keyword(s):  
Data Envelopment Analysis ◽  
Efficiency Analysis ◽  
Efficiency Score ◽  
Financial Resources ◽  
Data Envelopment ◽  
Financial Factors ◽  
Measuring Efficiency ◽  
Dea Models ◽  
Sport Organisation ◽  
Ccr Model

Every sport organisation strives to evaluate its performance: its weaknesses and strengths. Measuring efficiency and sports are two interrelated concepts and it is not surprising that most of the research on sports is focused on analysing the efficiency of teams according to player techniques, attack and defence efficiency. However, there are very few studies based on the analysis of financial factors such as teams’ revenue and costs. In this paper two Data Envelopment Analysis (DEA) models were used to evaluate 16 young cadet volleyball teams in Primorsko-Goranska County based on two economic inputs. The paper aims to explain the importance of teams’ financial resources in achieving sports efficiency. To analyse the relative efficiency of teams, two frequently used models are employed, the Banker Charnes Cooper (BCC) and the Charnes Cooper Rhodes (CCR) model. In the end, a super efficiency analysis was conducted to make a distinction in efficiency scores between efficient units. Analyses showed that financial factors are not crucial factors for efficiency score and gave possibility to use obtained results and improve the performance of inefficient volleyball teams. The study was conducted on a sample of 16 teams through 4 inputs and 1 output collected during 2017/2018 season.

Fuzzy C-means based data envelopment analysis for mitigating the impact of units’ heterogeneity

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 536-551 ◽  
Author(s):  
Seyed Hossein Razavi Hajiagha ◽  
Shide Sadat Hashemi ◽  
Hannan Amoozad Mahdiraji
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Negative Impact ◽  
Positive Impact ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Content Type ◽  
Fuzzy C Means ◽  
The Impact ◽  
Decision Making Units

Purpose – Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous. Design/methodology/approach – To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters. Findings – Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts. Research limitations/implications – The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained. Originality/value – The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.

Benchmarking Economical Efficiency of Renewable Energy Power Plants: A Data Envelopment Analysis Approach

2013 ◽  
Vol 772 ◽  
pp. 699-704 ◽  
Author(s):  
Corrado lo Storto ◽  
Gabriella Ferruzzi
Keyword(s):  
Renewable Energy ◽  
Data Envelopment Analysis ◽  
Power Plants ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Efficiency Measure ◽  
Super Efficiency ◽  
Economical Efficiency ◽  
Energy Plant ◽  
Measure Index

This paper implements Data Envelopment Analysis (DEA) to calculate an efficiency measure index of 21 energy power plants that use different technologies, including both renewable and conventional types. Super-efficiency measurements are used to generate a ranking of plants based on their efficiency score that can be used to select among alternatives. It is also showed how DEA can also be adopted to estimate the amount of financial subsidies necessary to make a renewable energy plant as efficient as a conventional energy plant, by calculating the extent to which inefficient power plants over-utilize specific inputs or under-produce outputs.

scholarly journals Optimization on the Efficiency of the Construction Sector Companies in Malaysia With Data Envelopment Analysis Model

2018 ◽  
pp. 893-898
Author(s):  
Lim Shun Jinn ◽  
Lam Weng Hoe ◽  
Lam Weng Siew
Keyword(s):  
Data Envelopment Analysis ◽  
Construction Industry ◽  
Efficiency Score ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Construction Sector ◽  
Analysis Model ◽  
Construction Company ◽  
The Growth Of Economy

Construction industry contributes to the growth of economy in Malaysia. Therefore, efficiency is important to measure how well the construction company is performing in utilizing the resources to generate outcomes. The aim of this research is to evaluate the efficiency of the listed construction sectors companies in Malaysia with Data Envelopment Analysis model. In this study, BREM, DKLS, ECONBHD, HSL, KERJAYA, MELATI, MLGLOBAL, PTARAS, PUNCAK, SUNCON and ZECON are ranked as efficient companies which achieve 100% efficiency score. This study is significant because it helps to identify the efficient companies that serve as benchmark to other inefficient companies for further improvement.

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