Akshay Kumar

The integration of multiple technical, economic, environmental, and social criteria establishes Multi-Criteria Decision Analysis (MCDA) as a dependable decision-making tool in the context of interdisciplinary research. This study employs a literature-based methodology to illustrate how MCDA, particularly utilizing the Analytical Hierarchy Process (AHP) and TOPSIS models, has been utilized to tackle intricate decision-making issues. It also highlights the noteworthy discoveries derived from real-world applications, drawing upon previous research and case studies. This study explores the methodologies employed in the commonly utilized AHP and TOPSIS approaches, highlighting their broad applicability across various industries from 2000 to 2023. Additionally, a comprehensive examination of the applications of MCDA has been organized into five distinct sectors, namely supply chain, healthcare, business, resource management, and engineering & manufacturing.

Nowadays, utility of the multi-criteria decision making (MCDM) technique in tackling real-world complex problems has risen tremendously. Even the United Nations is focusing on decision-making in order to accomplish Agenda 2030, as stated in its paragraph 48. The desire to promote sustainable development (SD) necessitates complex decision models, which could be achieved through the use of an efficient MCDM approach. Analytical Hierarchy Process (AHP) is one of the most efficient MCDM techniques that is incorporated in this study. The purpose of this work is to provide a contrasting of AHP's application that emerged between 2011 and 2022, rather than to reflect on its methodological improvements. Its application encompasses a wide range of disciplines including Renewable Energy, Sustainable manufacturing, Natural Hazards, Environmental Pollution, Landfill waste management and many others which lies explicitly or implicitly under the theme of SD. Previously, many reviews have been conducted that concentrated on a single decision topic; moreover, this review explore the comprehensive viewpoint of decision problems. As per statistical results, Middle Eastern countries such as Iran placed top in terms of applying AHP application in different sectors. GIS and fuzzy logic are the most often used approaches to incorporate AHP across all disciplines. Notably, the findings indicate that the most decision problem have selection and assessment as a major concern whereas, environmental, economical, LULC & DFR are more frequently used criteria.

Abstract Reliability allocation for components, redundancy allocation, and reliability redundancy allocation are of great significance for system reliability designing. Generally, standby redundancy gives higher reliability for any system than active redundancy, but standby redundancy has more complex modeling than active redundancy. Cold standby strategy is one of the most consistently applied procedures to accomplish high‐reliability necessity, where backups are performed to guarantee that a backup part can take control over the undertaking successfully when the currently working fizzles. Considering the fact that components may fail during the switching process from standby to active, the impact of an imperfect switch is also applied in the system. In this work, a new hybrid GWO‐PSO(HPSGWO) algorithm, based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), is presented to solve the cold‐standby reliability redundancy allocation problem (RRAP). The RRAP is a popular mixed integer nonlinear programming issue in a system plan that necessitates that the reliability target is set to fulfill the resource utilization requirement. Four contextual analyses are examined to feature the applicability of the proposed algorithm. The outcomes are compared with those obtained from PSO and GWO.