A Novel Artificial Intelligent Approach: Comparison of Machine learning Tools and Algorithms Based on Optimization DEA Malmquist Productivity Index for Ecoefficiency Evaluation

Mirpouya Mirmozaffari (1) , Elham Shadkam (2) , Seyed Mohammad Khalili (3) , Kamyar Kabirifar (4) , Reza Yazdani (5) , Tayyebeh Asgari Gashteroodkhani (6)

1 University of Texas at Arlington [Arlington]
2 Khayyam University
3 FUM - Ferdowsi University of Mashhad
4 UNSW - University of New South Wales [Sydney]
5 Islamic Azad University
6 University of Guilan

Purpose: Cement as one of the major components of construction activities, releases a tremendous amount of CO 2 into atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO 2 consumption has urged construction companies and decision makers to consider ecological efficiency affected by CO 2 consumption. Therefore, this research aims at developing a method capable of analyzing and assessing the Ecoefficiency determining factor in Iran's 22 local cement companies over 2015-2019. Design/Methodology/Approach: This research utilizes two well-known artificial intelligence approaches, namely optimization Data Envelopment Analysis (DEA) and machine learning algorithms at the first and second steps respectively to fulfill the research aim. Meanwhile, to find the superior model, CCR model, BBC model, and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks and hence, is a critical limitation of radial models. Thus, additive model by considering desirable and undesirable outputs, as a well-known DEA nonproportional and non-radial model, are utilized to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of additive model. Findings and Implications: After applying the proposed model, the Malmquist Productivity Index (MPI) is computed to evaluate the productivity of companies over 2015-2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms plays an important role at this step. Association rules is used to extract hidden rules, and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs, and desirable inputs to final desirable inputs in a single-stage model to minimize inputs as well as turning desirable outputs to final desirable outputs in the single stage model to maximize outputs to have a positive effect on the efficiency of the whole process

Mots clés: Artificial Intelligent; Machine learning; Optimization; Data Envelopment Analysis; Two-stage additive models; Eco-efficiency; Association rules; Classification

Domaines: Recherche opérationnelle [math.OC]; Intelligence artificielle [cs.AI]; Autre

Mirpouya Mirmozaffari, Elham Shadkam, Seyed Mohammad Khalili, Kamyar Kabirifar, Reza Yazdani, et al.. A Novel Artificial Intelligent Approach: Comparison of Machine learning Tools and Algorithms Based on Optimization DEA Malmquist Productivity Index for Ecoefficiency Evaluation. International Journal of Energy Sector Management, 2021, 15 (3), pp.523-550. ⟨10.1108/IJESM-02-2020-0003⟩. ⟨hal-03221354⟩