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Traditional industries, seen as cultural heritages, need to be innovative to grow and survive in the era of fast technological advancement. Traditional industries are continuously encountering decline. This research aims at identifying and addressing barriers to innovation to make an informed decision for better survival and improvement. A review of literature was used to propose a hierarchical structure of criteria in which innovation barriers were categorized into organizational, environmental, and supply chain dimensions. An online questionnaire was designed based on a multi-criteria decision-making (MCDM) method called Best–Worst Method (BWM) to identify the importance of the barriers. The opinion of 41 experts working in the Iranian Carpet and Leather industries was collected. The results indicate that organizational culture and management support are the most important barriers to innovation in the Leather and Carpet industries. We also found that environmental criteria are the least important criteria in both selected traditional industries. Results illustrated that the two industries have no significant difference in the three perspectives.

In real-life applications, there generally exist Decision Makers (DMs) who have preferences over outputs and inputs. Choosing appropriate weights for different criteria by DMs often arises as a problem. The Best-Worst Method (BWM) in Multiple Criteria Decision-Making (MCDM) depends on very few pairwise comparisons and just needs DMs to identify the most desirable and the least desirable criteria. Unlike MCDM, Data Envelopment Analysis (DEA) does not generally assume a priority for an output (an input) over any other outputs (inputs). The link between DEA and MCDM can be introduced by considering Decision-Making Units (DMUs) as alternatives, outputs as criteria to be maximized, and inputs as criteria to be minimized. In this study, we propose a linear programming model to embed DEA and BWM appropriately. We first propose a modified BWM linear programming model to satisfy all conditions that DMs can assume. We then illustrate how a conventional DEA model can be developed to include the BWM conditions. From our approach, the MCDM problem to obtain the optimal weights of different criteria are measured. At the same time, the relative efficiency scores of DMUs corresponding to the MCDM criteria are also calculated. We provide the foundation of measuring the efficiency scores when most desirable and the least desirable inputs and outputs are known. To show the process of the proposed approach, a numerical example (including 17 DMUs with seven inputs and outputs) is also discussed.

Multiple criteria sorting (MCS) dedicates to assigning alternatives to one of the predefined ordered categories according to their evaluation information on multiple criteria. The utility (value) function-based sorting is a popular MCS procedure, which requires decision-makers to express their preferences through assignment examples. By taking the assignment examples as reference alternatives, the additive value function, as the preferred model of a decision maker, can be built using the preference disaggregation technique. However, the existing literature hardly considered people’s hesitancy when determining assignment examples, and ignored applying linguistic evaluation information on qualitative criteria. To fill these research gaps, this study proposes a value-driven MCS procedure with probabilistic linguistic information considering uncertain assignment examples. Specifically, the probability linguistic term set, as a flexible information representation tool, is introduced to express the hesitancy of decision-makers regarding assignment examples and the performance of alternatives on qualitative criteria. Besides, to comprehensively reflect the preference of a decision-maker, a weighted additive value function is proposed based on the preference disaggregation technique to calculate the comprehensive scores of alternatives in which the weights are determined by the best-worst method. Finally, a case study on the sorting of down coats for sale demonstrates the applicability and superiority of our proposed method.

Choosing the best schools from a group of schools is a multi-criteria decision-making (MCDM) problem. In this paper, we have represented a method that uses the fusion of two multi-criteria decision-making methods, Best–Worst Method (BWM) and Analytic Hierarchy Process (AHP), to rank some of the user preferred alternatives. The system considers the choice of the user and the quality of the alternatives to rank them. User preferences on the criteria are taken as inputs in the form of best–worst comparison vectors to measure the choice of the user. These values are applied to calculate the numeric weights of each of the criteria. These weights reflect the preference of the user. A dataset of secondary schools in Bangladesh has been compiled and used for automatic quantitative pairwise comparison on the alternatives to calculate the score of each alternative in every criterion, which reflects its quality in that criterion. These scores are calculated using AHP. The weights of the criteria as well as the scores of these alternatives in those criteria are then used to calculate the final score of the alternatives and to rank them accordingly. An extensive experimental analysis and comparative study is reported at the end of this paper.