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Top1. Introduction
Supplier selection related to the construction of green buildings differs from the conventional construction (Mokhlesian, 2014). This is due to the existing differences in its basic principles and utilization of environmentally friendly materials and technologies. The green construction refers to the efficiency in resource usage over the entire buildings’ lifecycle including planning, design, construction, operation, maintenance, renovation and demolition (Zhang et al., 2018). Building information modeling could be express as a process to ensure the planning, design, and construction of buildings. In this regard, the current paper deals with some aspects of planning that aims to select suppliers for construction of green buildings. The challenge for construction managers is the construction supply chain management to make the right materials quantities for construction site on time within a limited budget (Cengiz et al., 2017). The suppliers’ evaluation and selection are an important tasks in any organization and especially in the building industry. The supplier selection is a strategic decision because overall project completion depends on the performance of different suppliers (Khan et al., 2018). The managers are faced with decisions that require an evaluation of several alternatives toward some evaluation criteria with different importance that is not an easy decision task. The construction supply chain management is related to delivering of materials to specific construction site project. The selection of the most appropriate supplier for ready-mix concrete requires trade-offs among multiple stakeholders and different evaluation criteria (Chou & Ongkowijoyo, 2015). Thus, the supplier selection is transformed into a multi-expert and multi-criteria decision making (MCDM) problem. There exist different MCDM methods to rank or to select a supplier using multiple criteria (Wang et al., 2016; Huang et al., 2015). These MCDM methods can be divided into two categories: compensatory and outranking (not compensatory) (Majumder, 2015). The compensatory model is a rational decision-making model because the attractive attributes of an alternative can compensate for less attractive ones, while the outranking methods rely on pairwise comparison of alternatives performance. Due to the variety of MCDM methods and importance in the selection of the most appropriate suppliers, the group decision making should be considered as the better approach. The individual decision maker’s preferences for single attributes could be aggregated in a utility function to add up the preferences of the entire set of attributes when assessing alternatives. The methods based on multi-attribute utility theory (MAUT) such as SAW, WPM, SMARTER, AHP, TOPSIS, etc., share the compensatory strategy and use the utility function that is maximized (Huang et al., 2013; Wang, 2015; Edwards & Barron, 1994; Saaty, 1987; Hwang & Yoon, 1981).
In the building industry, the AHP model is applied for selection of steel and cement supplier in the construction industry of India (Patil & Kumthekar, 2016). It describes the selection of a supplier using ten evaluation criteria. A compensatory method based on the fundamentals of MAUT, namely SMARTER is used to structure the process of suppliers’ selection for the goal of a civil construction company in Brazil (Schramm & Morais, 2012). Due to the importance of transportation-related impacts of raw materials for concrete production a new method is proposed considering the analysis of road transportation of materials (Goswein et al., 2018). The problems of supplier selection usually involve many and conflicting criteria such as price, quality, certifications, delivery lead-time, price breaks and quantity discounts, etc. A critical aspect in supplier evaluation is the subjectivity in the estimates of the performance ratings and criteria weights due to the different experts’ points of view. That’s why each expert opinion could be taken with different importance depending on its particular knowledge and experience to the investigated problem (Korsemov et al., 2018). Such decisions require the use of appropriate MCDM tools that can be realized as web-based application (Borissova et al., 2016; Kirilov et al., 2013) or realized as MS Excel application (Borissova et al., 2018).