Sustainability assessment of renewable energy site location using a combinatorial decision-making model under uncertainty and data reliability

ABSTRACT

Decision-making has become one of the most important aspects of various problems. Accordingly, making the right decision accurately and efficiently based on the most influential criteria is still one of the critical challenges in this area. Among various optimisation issues, sustainable development of renewable energies is one the most crucial in which one of the significant prerequisites is considering the most suitable location for establishing renewable power plants. Based on these motivations, this paper developed a combinatorial multi-criteria decision model using Fuzzy-DEMATEL (FDEMATEL), Fuzzy-ANP (FANP) and Z-number-DEA (ZDEA) methods considering uncertainty and data reliability. For validation of the proposed model, a decision problem is considered for the location selection of a landfill with the aims of managing urban waste, reducing the total cost, improving the efficiency of waste disposal and community social issues. This model considers four indicators, including environmental, economic, social and strategic aspects. In addition, the Crisp-DEA (DEA) and Fuzzy-DEA (FDEA) methods are employed to validate the results using the Non-Parametric Spearman Correlation Test and a sensitivity analysis is performed for the ZDEA model. In the next step, a managerial analysis is presented. Finally, a comparison is discussed between the proposed model and existing decision models.

KEYWORDS:

• Decision support systems
• multi criteria decision making
• location selection
• fuzzy logic
• Z-number theory
• data reliability

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in Mendeley at http://doi.org/10.17632/y3f6277465.1.

Additional information

Notes on contributors

Pedram Memari

Pedram Memari completed his M.Sc. at School of Industrial Engineering, College of Engineering, University of Tehran (2019). He obtained his B.Sc. from Urmia University of Technology (2015). His research interests are Multi-Criteria Decision Making (MCDM), Artificial Intelligence (AI), scheduling, supply chain management (SCM) and inventory optimisation problems in deterministic and stochastic cases, besides simulation, data mining and smart grids.

Seyedeh Samira Mohammadi

Seyedeh Samira Mohammadi is a software engineer. She studied B.Sc. in Computer Engineering at Urmia University of Technology (2015). She obtained her Master's degree in Software Engineering from Islamic Azad University, South Tehran Branch (2018). Her research interests include cloud computing, fog computing, data mining, artificial intelligence, smart cities, job scheduling, resource management, load balancing, high-performance computing, distributed systems, programming languages, visualisation, human–computer interaction, and networks.

Fariborz Jolai

Fariborz Jolai is the Dean of School of Industrial Engineering at the University of Tehran. He is interested in theoretical and practical optimisation research problems in service and manufacturing systems. His recent research works are on the supply chain planning and management, health care system and scheduling problems in uncertain environments.

Seyed Farid Ghaderi

Seyed Farid Ghaderi was born in Iran, on 17 July 1962. He graduated in Electrical Engineering (Power) from Ferdowsi University in 1989, studied M. Sc. in Industrial Engineering at Amir Kabir University in 1993, and received his Ph.D. in Social Engineering, Tokyo Institute of Technology (TIT) in 1999, and now he is a faculty member at University of Tehran.