The decision-making trial and evaluation laboratory (DEMATEL) method has been applied to solve numerous multi-criteria decision-making (MCDM) problems where crisp numbers are utilized in defining linguistic evaluation. Previous literature suggests that the intuitionistic fuzzy DEMATEL (IF-DEMATEL) can offer a new decision-making method in solving MCDM problems where intuitionistic fuzzy sets (IFSs) are utilized in defining linguistic evaluation. This paper aims to develop a cause-effect diagram of subcontractor selection using a modified IF-DEMATEL method. In this paper, three modifications are made to the IF-DEMATEL method. Two memberships of IFSs, relative weights of experts, and a transformation equation are the elements introduced to the IF-DEMATEL. The linguistic variables that defined in IFSs are meant to capture wide arrays of uncertain and fuzzy information in solving MCDM problems. Furthermore, the modified IF-DEMATEL is applied it to a subcontractors' selection problem where groups of cause and effect criteria are segregated. A group of experts' opinions were sought to provide linguistic evaluations regarding the degree of influence between criteria in subcontractors' selection. The results show that four criteria are identified as cause criteria while six other criteria are identified as effect criteria. The results also suggest that the criteria "experience" is the main cause that influence the selection of subcontractors. The identification of cause and effect criteria would be a great significance for practical implementation of subcontractors' selection.

In the last few decades, the computational methods under Multi-Criteria Decision-Making (MCDM) have experienced significant growth in research interests from various scientific communities. Multi-Attributive Border Approximation area Comparison (MABAC) is one of the MCDM methods where its computation procedures are based on distances and areas, and able to express a complex decision systematically. Previous literature have suggested the combination of MABAC with fuzzy sets, in which this combination is used to solve problems that are characterized by uncertain and incomplete information. Differently from the fuzzy MABAC, which directly used single membership, this paper proposes bipolar neutrosophic MABAC of which the positive and negative of truth, indeterminate and false memberships of bipolar neutrosophic set are introduced to enhance decision in sustainable energy selection. Fourteen criteria and seven alternatives of sustainable energy are the main MCDM structures that need to be solved using the proposed method. A group of experts were invited to provide rating of performance values of criteria and alternatives of sustainable energy problem using a bipolar neutrosophic linguistic scale. The distances of alternatives from the Border Approximation Area of bipolar neutrosophic MABAC are the main output of the proposed method prior to making the final decision. The computational results show that ‘Biomass’ is the optimal alternative to sustainable energy selection. Comparable results are also presented to check the consistency of the proposed method.

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