Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision-making problems. The authors attempted to create two novel hybrid MCDM systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex PRoportional ASsessment (COPRAS). To demonstrate the ability and effectiveness of these two hybrid models i.e., TOPSIS-ARAS and COPRAS-ARAS were applied to solve a real-time robot selection problem with 12 alternative robots and five selection criteria, while evaluating the parametric importance using the CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. The rankings of the robot alternatives gained from these two hybrid models were also compared to the obtained results from eight other solo MCDM tools. Although the rankings by the applied methods slightly differ from each other, the final outcomes from all of the adopted techniques are consistent enough to suggest that robot 12 is the best choice followed by robot 11, and robot 4 is the worst one among these 12 alternatives. Spearman Correlation Coefficient (SCC) also reveals that the proposed rankings derived from various methods have a strong ranking relationship with one another. Finally, sensitivity analysis was performed to investigate the effects of weight variation and to validate the robustness of the implemented MCDM approaches.

Inter-vehicle communication is a promising way to share and disseminate real-time and nearby safety information on the road. Scalability of vehicular ad hoc network is one of the decisive matters for a network designer, which can be solved by clustering. In this paper, as our first algorithm an attempt has been made to create clusters by considering direction, position and relative speed of the vehicle for managing the scalability issue. Moreover, we have proposed a new algorithm for selecting the most appropriate cluster head (CH) by considering the real time updated position and trust value of vehicles. As, evolutionary based routing is a predominant research theme thus we have used the most efficient ant colony routing technique based on trust for the simple highway scenario based VANET. Finally simulation result shows our proposed Trust dependent Ant Colony Routing (TACR) has outperformed the Mobility-aware Ant Colony Optimization Routing (MAR-DYMO) algorithm in terms of routing overhead.

Robots are one of the most commonly used automated material handling equipment (MHE) in an industry, installed to perform a variety of hazardous and repetitive tasks, e.g., loading, unloading, pick-and-place operations, etc. The selection of an appropriate industrial robot is influenced by a number of subjective and objective factors that define its characteristics and working accuracy. As a result, robot selection can be regarded as a multi-criteria decision-making problem. In this article, a new hybrid MCDM model combining COPRAS and ARAS is developed to execute an industrial robot selection process based on three alternatives and five criteria. Fuzzy analytic hierarchy process is integrated to compute the parametric weights. It is discovered that Robot 3 and Robot 1 are coming out to be the best and worst alternative robots from this hybrid model. Finally, comparative analysis among eight other MCDM tools and sensitivity analysis are also performed to assess the stability and robustness of the developed hybrid model and other applied MCDM tools.

This article presents the detailed study of integrated AHP-TOPSIS multiple-criteria decision-making (MCDM) methodology. For these purposes, a real-life example is taken where the best smartphone mobile model is proposed among 10 different available models by implementing integrated AHP-TOPSIS methodology. The 10 mobile models selected for this analysis are presently available in the market and are from different brands having different specifications and price range. The selection process is done based on four major criteria (i.e., price, internal storage, RAM, and brand). AHP is applied for the criteria weightage's calculation, whereas TOPSIS is adopted for selecting the best alternative and make a preference ranking order indicating the best model to the worst. The final result shows that Samsung J7 is the best smartphone model followed by Redmi 7A, and Redmi K20 pro occupies the last position; thus, it is the worst model among the group.

In Multi-Criteria Decision Making (MCDM) method, the criteria weighting is one of the most critical parameters that affect the stability of the ranks, sensitivity of the decision and the robustness of the model. A variety of methods have been developed to weight; these can be categorized as subjective, objective and hybrid. The weights derived from the statistical or information theoretic properties of the inherent data are called objective methods, while those based on expert judgment and cognitive evaluations are called subjective methods. Combining both paradigms is known as hybrid methods and provides a more reliable and unbiased approach. Although significant methodological progress has been made, there remain many different methods, variations in method selection, validation and contextual applicability. The paper comprehensively reviews and critically analyzes the most important criteria weighting techniques in MCDM. It categorizes and compares current solutions, analyzes their theory and limitations, and explores their use in different fields including engineering, energy systems, supply chain and financial decision-making. The research also identifies the new trends like fuzzy logic integration, machine learning based weighting and adaptive decision frameworks. Thirdly, the main research gaps and future research directions are suggested to lead next generation weighting models in MCDM under uncertainty.