Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision-making problems.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.

The main objective of this research article is to select the best mobile model among various alternatives available on the market. For this analysis 10 alternative models from different brands are selected from different online shopping website having different specifications and ranging from low budget to medium budget in terms of price. For this selection purposes two multiple criteria decision making tools (MCDM) has been adopted i.e. Complex Proportional Assessment (COP-RAS) and Additive Ratio Assessment (ARAS). The selection process is done based on four important criteria i.e. price, internal storage, RAM and brand. The weightages of the criteria are calculated by using Analytic Hierarchy Process (AHP) and these weightages are further used in COPRAS and ARAS methods. Individual COPRAS and ARAS method is applied for the selection of the best mobile and the preference ranking order of the models are also proposed by each process. The proposed ranking order by both the methods are compared and it is found that the outcome results are more or less the same using both techniques but there is a slight change in ranking of the middle-order alternatives. Both processes give model 1 and model 4 as the best and the worst models respectively among 10 alternatives.

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This research paper presents a comprehensive review of Multiple Criteria Decision-Making (MCDM) methods, encompassing their advancements, applications, and future directions. The study begins with an introduction emphasizing the significance of MCDM in complex decision-making scenarios. Through a systematic literature review, recent developments in MCDM techniques are examined, including multi-objective methods, fuzzy-based approaches, data-driven models, and hybrid methodologies. The strengths and limitations of each method are critically analyzed. Furthermore, the paper investigates the diverse applications of MCDM in domains such as business, engineering, environment, healthcare, and public policy, highlighting the practical implications through real-world case studies. The study then identifies emerging trends and challenges in MCDM research, discussing the integration of MCDM with emerging technologies, enhancing robustness and adaptability, addressing uncertainty, and identifying unexplored domains for potential application. This comprehensive review serves as a valuable resource for decision-makers and researchers, providing insights into the advancements, applications, and future directions of MCDM methods.

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