Integrated index decomposition analysis-artificial neural network-data envelopment analysis (IDA-ANN-DEA): implementation guide

The activities of the building and construction industry have made it one of the highest energy consumers and thus one of the highest emitters of greenhouse gases. The main objective of this study was to develop a system to determine energy saving in the industry. This was achieved through an integrated model of index decomposition analysis, an artificial neural network, and data envelopment analysis. Index decomposition analysis is used to understand the contribution of the factors responsible for energy consumption. These factors are inputs to the artificial neural network to predict the baseline energy consumption. The energy saving is finally determined through data envelopment analysis. The results showed that the integrated model presents a reasonable amount of energy saving in the building and construction industry.

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Assessing the possible potential in the global energy consumption: Integrated artificial neural network and data envelopment analysis

2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) ◽

10.1109/ieem.2017.8290246 ◽

2017 ◽

Author(s):

O. A. Olanrewaju ◽

C. Mbohwa

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Energy Consumption ◽

Data Envelopment Analysis ◽

Data Envelopment ◽

Global Energy ◽

Artificial Neural ◽

Global Energy Consumption

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A hybrid approach using data envelopment analysis and artificial neural network for optimising 3PL supplier selection

International Journal of Logistics Systems and Management ◽

10.1504/ijlsm.2017.081500 ◽

2017 ◽

Vol 26 (2) ◽

pp. 203 ◽

Cited By ~ 2

Author(s):

Rakesh D. Raut ◽

Sachin S. Kamble ◽

Manoj G. Kharat ◽

Hemendu Joshi ◽

Chirag Singhal ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Envelopment Analysis ◽

Supplier Selection ◽

Hybrid Approach ◽

Data Envelopment ◽

Artificial Neural ◽

Using Data

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An integrated data envelopment analysis–artificial neural network approach for benchmarking of bank branches

Journal of Industrial Engineering International ◽

10.1007/s40092-015-0125-7 ◽

2016 ◽

Vol 12 (2) ◽

pp. 137-143 ◽

Cited By ~ 10

Author(s):

Elsa Shokrollahpour ◽

Farhad Hosseinzadeh Lotfi ◽

Mostafa Zandieh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Envelopment Analysis ◽

Data Envelopment ◽

Network Approach ◽

Neural Network Approach ◽

Bank Branches ◽

Artificial Neural ◽

Artificial Neural Network Approach

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Forecasting sustainability of supply chains in the circular economy context: a dynamic network data envelopment analysis and artificial neural network approach

Journal of Enterprise Information Management ◽

10.1108/jeim-12-2020-0494 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Hadi Shabanpour ◽

Saeed Yousefi ◽

Reza Farzipoor Saen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Envelopment Analysis ◽

Supply Chains ◽

Circular Economy ◽

Real Life ◽

Network Data ◽

Data Envelopment ◽

Content Type ◽

First Time

PurposeThe objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.Design/methodology/approachIt is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.FindingsA practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.Originality/valueWe propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.

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