Multi-criteria group decision-making method for optimal selection of sustainable industrial building options focused on petrochemical projects

Over the past three decades, sustainable development (SD) has gained wide recognition among decision makers and politicians. Today, this concept aims to provide a healthier environment, more-improved society, and developed economy in both developed and developing world communities. Thus, to achieve a sustainable community, one needs to first better understand the underlying indicators and adopt appropriate sustainable policies in different sections and sectors of the community. As a vital component, the construction industry plays a key role in SD, which has drawn the attention of decision makers to find sustainable solutions worldwide for such an active industry. Industrial buildings, as a large segment of the construction industry, consume vast amounts of energy and financial resources and have a major role in job creation and improving the quality of life in a society. Therefore, this paper focuses on social, economic, and environmental aspects of SD of industrial buildings to evaluate the sustainable indicators and to develop a combinatory multi-criteria group decision-making framework for optimal selection of sustainable options of industrial buildings. The developed framework considers uncertainty alongside various preference orders and risk attitudes of decision makers-using utility functions. The decision making process consists of two stages: the first stage, takes the advantages of integration of utility function, ELECTRE I (the elimination and choice translating reality), and Grey theory to create the new method of Grey-Utility-ELECTRE to rank the feasible alternatives. And the second stage utilizes the ordered weighted averaging (OWA) aggregation operator to aggregate the preferences of environmental, social, and economic dimension, as three decision makers, to determine the final ranking. Because this research focuses on the sustainability of industrial buildings, the performance and capabilities of the developed framework are demonstrated using five petrochemical buildings of a petrochemical company in Iran, as a case study. (C) 2016 Elsevier Ltd. All rights reserved.