scholarly journals Model of Determining the Optimal, Green Transport Route among Alternatives: Data Envelopment Analysis Settings

2020 ◽  
Vol 8 (10) ◽  
pp. 735
Author(s):  
Luka Vukić ◽  
Tanja Poletan Jugović ◽  
Giambattista Guidi ◽  
Renato Oblak
Keyword(s):  
Data Envelopment Analysis ◽  
Optimal Transport ◽  
Transport Cost ◽  
Transport Sector ◽  
Practical Case ◽  
Data Envelopment ◽  
Quantitative Factor ◽  
Shortest Route ◽  
Starting Point ◽  
Set Up

Data envelopment analysis (DEA) is a useful method for determining relative efficiency in many types of businesses, including the transport sector. In line with the European Union’s (EU) policy of sustainable development of transport, external costs become the competitiveness factor of the transport route valorization. Presenting specific DEA settings, the paper aims to show and test a developed model for determining the optimal transport route among alternatives towards the same destination where external cost as a socio-ecological factor is included in DEA, along with transport cost (quantitative factor) and transport time (qualitative factor). In order to adhere to the principles of the least possible energy consumption, the given distance that also included in DEA settings represents the shortest route between the starting point and destination, as a unique and constant output variable. Therefore, the optimal direction selected by the DEA stands for the green route. The capabilities of the DEA, set up in this way within the broader model, are demonstrated in the practical case.

ASSESSING THE EFFICIENCY OF LITHUANIAN TRANSPORT SECTOR BY APPLYING THE METHODS OF MULTIMOORA AND DATA ENVELOPMENT ANALYSIS

Transport ◽  
2011 ◽  
Vol 26 (3) ◽  
pp. 263-270 ◽  
Author(s):  
Alvydas Baležentis ◽  
Tomas Baležentis
Keyword(s):  
Data Envelopment Analysis ◽  
Data Availability ◽  
Production Factor ◽  
Transport Sector ◽  
Scale Efficiency ◽  
Economic Downturn ◽  
Data Envelopment ◽  
Multi Objective Optimization ◽  
Limited Data ◽  
Full Capacity

This study focuses on evaluating Lithuanian transport sector throughout 1995–2009 by applying multi–criteria decision making method MULTIMOORA (Multi–Objective Optimization plus the Full Multiplicative Form) and data envelopment analysis (DEA). MULTIMOORA provided ranks that enabled to perform time series analysis, whereas DEA made possible to identify both technical and scale inefficiencies. Due to limited data availability, we analyzed the transport sector as a whole, i. e. it was not decomposed into that of land, air, railway or water. Although every production factor, including labour, capital and land is required for developing the transport sector, due to limited data availability, it is not possible to tackle them all when performing analysis. Consequently, one input, namely energy consumption in transport, was considered in the conducted analysis. On the other hand, two forms of transport – passenger and freight transport – were distinguished, and each of them was measured using composite indicators of passenger and tonne kilometres respectively. These two indices were considered as the outputs of transport sector activity. The final ranks provided by MULTIMOORA indicate that the transport sector was operating most effectively during 2004–2008, whereas it exhibited relative inefficiency throughout 1996–1998. The application of DEA suggests that the efficiency downturn of 1996–1998 might have been caused by technical inefficiency, whereas that of 2008–2009 was driven by scale inefficiency. Indeed, the technical modernization of the transport sector as well as the resolution of resource allocation problems might have lead to an increase in technical efficiency. Meanwhile, economic downturn prevents the transport system from working at full capacity; hence, scale efficiency is still observed.

scholarly journals Efficiency Evaluation of State Transport Undertakings of India using DEA-NN approach

2015 ◽  
Vol 10 (5) ◽  
pp. 2189-2198
Author(s):  
Dr. Punita Saxena ◽  
Dr. Amita Kapoor
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Envelopment Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Transport Sector ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Data Set ◽  
Policy Makers

The economy of any nation depends on the structure and functioning of its various sectors. Transport sector is one of the vital sectors for the financial system of any developing country. All other sectors are dependent on it either directly or indirectly. Thus improving the efficiency of this sector has become a major concern for the operators and the policy makers. The present paper presents an amalgamation of the two non-parametric techniques, Data Envelopment Analysis (DEA) and Neural Networks (NNs) to compute the efficiency scores of State Transport Undertakings of India. DEA is used to compute the efficiency scores of 27 DMUs. These scores are used to train a neural network model, namely the BPN model. The algorithm is developed and used for predicting the efficiency scores of other units of the data set. The results obtained are comparable and it has been shown that this approach helps in improving the discriminatory power of DEA.

scholarly journals A Benchmarking Strategy for Delhi Transport Corporation: An Application of Data Envelopment Analysis

2019 ◽  
Vol 4 (1) ◽  
pp. 232-244 ◽  
Author(s):  
Punita Saxena
Keyword(s):  
Regression Analysis ◽  
Data Envelopment Analysis ◽  
Urban Areas ◽  
Returns To Scale ◽  
Road Transport ◽  
Transport Sector ◽  
Data Envelopment ◽  
Data Set ◽  
Explanatory Variables ◽  
Input And Output

The growth of any developing economy depends largely on its transport sector. Growing economy leads to more job opportunities and movement of people from rural to urban areas. Public road transport hence plays a significant role as a support system in carrying passengers. This paper discusses the efficiency of State Transport Undertakings of India, in particular, Delhi Transport Corporation (DTC) using the technique of Data Envelopment Analysis (DEA) and regression analysis. A data set of 46 State Transport Undertakings of India have been considered for the study. DEA was applied to compute the efficiencies of units under study. Potential improvements in the input and output variables were computed for the inefficient units. Regression analysis was then performed to identify the explanatory variables that significantly affect the input and output variables. It was observed that DTC is one amongst the worst performers. It showed a technical inefficiency of 50.94% and was operating on decreasing returns to scale. Further, DTC needs to increase its output substantially in order to attain the level of efficiency. Also it is not utilizing its resources optimally as it needs to reduce all its inputs. In other words, DTC is not utilizing its resources as optimally as its efficient peers. This paper is an attempt to apply regression technique along with the non parametric technique of Data Envelopment Analysis so that the decision makers of DTC can identify the areas where improvement is required and plan a strategy to improve their performance. This would enable DTC to move from a loss incurring to a profit making unit.

scholarly journals Applicability Evaluation of China’s Retail Electricity Price Package Combining Data Envelopment Analysis and a Cloud Model

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 6
Author(s):  
Yongxiu He ◽  
Meiyan Wang ◽  
Fengtao Guang
Keyword(s):  
Data Envelopment Analysis ◽  
Electricity Market ◽  
Evaluation System ◽  
Cloud Model ◽  
Evaluation Model ◽  
Electricity Price ◽  
Evaluation Index System ◽  
Data Envelopment ◽  
Combining Data ◽  
Set Up

With the reform of the power system, the retail electricity market in China has gradually been liberalized. The mechanism of freely selling electricity have been set up. To grab market share and increase profits, electricity retail companies have introduced a series of retail electricity price packages. To evaluate the applicability of these retail electricity price packages, an adaptive evaluation index system that takes into account the interests of both the power company and the users is first established. Furthermore, an integrated evaluation model that combines data envelopment analysis (DEA) and the cloud model is proposed. In this model, DEA is used to process quantitative indicators and the cloud model is employed to quantify qualitative problems. A case study of Tianjin is conducted to verify the effectiveness of the proposed evaluation system and model. The empirical study shows that qualitative indicators can also affect the applicability of the retail electricity price packages, and the applicability of the retail electricity price package was different in different seasons. Finally, several reliable suggestions on how to design retail electricity price packages are given based on the research. This study provides useful support for customers to choose the price package to increase the competitiveness of power selling companies and ultimately promote the reform of power selling.

Human capital in public research laboratories: a method proposal for assessing actual and target scores

2020 ◽  
Vol 12 (5) ◽  
pp. 1189-1208 ◽  
Author(s):  
Driss El Kadiri Boutchich
Keyword(s):  
Human Capital ◽  
Data Envelopment Analysis ◽  
Research Policy ◽  
Public University ◽  
Data Envelopment ◽  
Content Type ◽  
Research Activities ◽  
Starting Point ◽  
Target Values ◽  
The Impact

PurposeThis work aims to propose an alternative method of human capital calculation for research laboratories of public university, taking into account some drawbacks of the methods currently applied in this field.Design/methodology/approachThis method is implemented via a linear program extracted from Data Envelopment Analysis based on slack movement. This is the formulation of Copper et al. (2000), which is used as the starting point for developing the proposed method through important transformations.FindingsThe proposed method is supported by an illustration related to a Moroccan public university. This illustration showed that 57 per cent of the laboratories and all the research activities that they perform are in deficit with respect to target scores.Research limitations/implicationsThe proposed method has technical limitations related to scores equal to 1 and to variables when those are numerous. To solve them, it is possible to use peer benchmarking system for the first limitation, and methods of regrouping the variables when those are numerous for the second limitation. Equally, the proposed method does not associate slack with important factors like governance and the impact analysis of research on innovation, competitiveness, and societal aspects. Likewise, it does not use the slack to measure individual efficiency at the same laboratory. Future research can fill these gaps.Practical implicationsThis work allows making appropriate budgetary and research policy within university, through budgeting process and management control by using raw and adjusted target values as well as actual ones. Also, the highlighting of the excessive slacks leads the university to take actions to reduce them, according to the most loss-making research activities.Originality/valueThe proposed method is original, since it fills a deficit in terms of human capital target values calculation and of the slack movement concept in relation to the efficient frontier. Additionally, it transforms the Data Envelopment Analysis program into a program that eliminates the slacks linked to the inputs, the radial movement related to the outputs and treats only the outputs and slacks related to these outputs.

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