A multi-attribute framework for the selection of high-performance work systems: the hybrid DEMATEL-MABAC model

Research in strategic human resource management indicates that high performance work systems (HPWS) have a positive impact on the overall performance of an organization as a result of better human resource (HR) outcomes. Regarding the multi-dimensional and complex nature of these factors, common statistical models are not useful for examining the performance of HPWS. Using the capabilities of multi-attribute decision-making (MADM) methods to deal with various criteria that may be contradictory, this study proposes a MADM-based framework that provides the opportunity to prioritize HR practices. Based on this framework, high-performance HR practices and their related HR outcomes were identified after studying the theoretical literature and ascertaining the views of decision-makers and HR experts. Then, after looking at the interactions among HR outcomes, the weights of the criteria were calculated using the method of the decision making trial and evaluation laboratory (DEMATEL). Then, the alternatives were ranked using the multi-attributive border approximation area comparison (MABAC) method. Finally, the designed framework was implemented in an organization active in the banking industry. This framework can be used to improve employees’ performance and, consequently, the performance of the organization. Accordingly, taking into account the resource constraints organizations face, the priorities presented can be helpful in budgeting human-resource-management (HRM) improvement projects and making an appropriate resource allocation for this

Selection of optimal variant route based on dynamic fuzzy GRA

Given the high costs of construction and maintenance, an optimum design methodology is one of the most important steps towards the development of transportation infrastructure, especially freeways. However, the effects of different variables on the decision-making process to find an optimal variant have caused the choice to become a very difficult and professional task for decision makers. So, the current paper aims to determine the optimal variant route for Isfahan-Shiraz freeway through MADM approaches. First, evaluation indices for an optimal route variant are derived through literature review and expert panel assessment. Then, a dynamic fuzzy GRA method is used for weightings and optimal route selection. Bases on the results, the road longevity, views of NGOs and route integration are identified as the highest-weighted criteria in route variant prioritization. Further, Route 3 is defined as the priority for the optimal variant for Isfahan–Shiraz freeway, which is the main basis in practice

An Extension of the Failure Mode and Effect Analysis with Hesitant Fuzzy Sets to Assess the Occupational Hazards in the Construction Industry

The construction industry is considered as one of the most dangerous industries in terms of occupational safety and has a high rate of occupational incidents and risks compared to other industries. Given the importance of identifying and assessing the occupational hazards in this industry, researchers have conducted numerous studies using statistical methods, multi-criteria decision-making methods, expert-based judgments, and so on. Although, these researchers have used linguistic variables, fuzzy sets and interval-valued intuitionistic fuzzy sets to overcome challenges such as uncertainty and ambiguity in the risk assessment conducted by experts; the previous models lack in efficiency if the experts are hesitant in their assessment. This leads to the inability to assign a specific membership degree to any risk. Therefore, in this research, it is tried to provide an improved approach to the Failure Mode and Effects Analysis (FMEA) method using an Multi-Criteria Decision-Making (MCDM) method based on the hesitant fuzzy set, which can effectively cope with the hesitance of the experts in the evaluation. Also, Stepwise Weight Assessment Ratio Analysis (SWARA) method is applied for risk factor weighing in the proposed approach. This model is applied to a construction industry case study to solve a realistic occupational risk assessment. Moreover, a comparison is made between the results of this model and those obtained by the conventional FMEA and some other aggregation operators. The results indicate that the newly developed approach is useful and flexible to address complex FMEA problems and can generate logical and reliable priority rankings for failure modes.This article belongs to the Special Issue Occupational Safety and Risks in Constructio

An evaluation model of business intelligence for enterprise systems with new extension of CODAS (CODAS-IVIF)

Due to today’s dynamic and changing environment and the organization need to decide in emergencies and accurate analysis of the internal and external environment from different aspects, creating a decision support environment is considered as a vital factor for the success of organizations that is achieved using business intelligence. Hence, it is necessary to have enterprise systems at a reasonable level of business intelligence to provide an environment suitable for supporting decision makers through aggregation and analysis of data in their database. Therefore, this study provides a novel assessment framework of BI for enterprise systems, by extending of CODAS method with interval-valued intuitive fuzzy sets. The CODAS is a new method for multiple attribute decision making (MADM) problems. In the proposed model, a number of 34 criteria from the most important BI indexes are identified and, accordingly, five enterprise systems are evaluated through expert discussions. The results reveal that the most important assessment criteria defined by expert panels include visual graph display, dashboard design, capable of data storage, meeting stakeholder needs, and the possibility for detailed realistic analysis. Then, one alternative is defined as the final selection which provides an outstanding performance on the criteria of groupware programs, group decision-making tools, training techniques, data transfer capability, knowledge inference, supporting fuzzy concepts under ambiguity and uncertainty, realtime analytical processing, managing email channels, and achieving stakeholder satisfaction. The results obtained from the extended method are compared with three different ranking techniques. And, the analysis of correlation coefficients confirms similarity between this solution and such methods as COPRAS-IVIF and MABAC-IVIF

A Hybrid Multi-Criteria-Decision-Making Aggregation Method and Geographic Information System for Selecting Optimal Solar Power Plants in Iran

Policy-makers should focus on solar energy due to the increasing energy demand and adverse consequences such as global warming. Conflicting criteria influence choosing the most desirable place to construct a Solar Power Plant (SPP). Researchers have popularized multicriteria decision-making (MCDM) methods because of the potential. Although the simultaneous use of several methods increases the robustness and accuracy of the results, existing methods to integrate MCDM methods mainly consider the same weight for all methods and utilize the alternatives ranking for the final comparison. This paper presents a hybrid decision-making framework to determine the best location for SPPs in Iran using a set of criteria extracted from the literature and expert opinions. An initial list of decision-making alternatives is prepared and evaluated using GIS software in terms of criteria. Decision-makers prioritized the identified alternatives using the MCDM methods, including SWARA and different ranking methods (TOPSIS, TODIM, WASPAS, COPRAS, ARAS, and MULTIMOORA). Finally, the CCSD method aggregates the results and identifies the best location. Results highly correlate with the results of previous methods and demonstrate the robustness of the proposed approach and its capability to overcome the limitations of previous methods