Browsing by Author "Ravichandran, K. S."

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An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods.
(Computer Modeling in Engineering & Sciences, 2024) Krishankumar, Raghunathan;; Sundararajan, Dhruva;; Ravichandran, K. S.
Hydrogen is the new age alternative energy source to combat energy demand and climate change. Storage of hydrogen is vital for a nation's growth. Works of literature provide different methods for storing the produced hydrogen, and the rational selection of a viable method is crucial for promoting sustainability and green practices. Typically, hydrogen storage is associated with diverse sustainable and circular economy (SCE) criteria. As a result, the authors consider the situation a multi-criteria decision-making (MCDM) problem. Studies infer that previous models for hydrogen storage method (HSM) selection (i) do not consider preferences in the natural language form; (ii) weights of experts are not methodically determined; (iii) hesitation of experts during criteria weight assessment is not effectively explored; and (iv) three-stage solution of a suitable selection of HSM is unexplored. Driven by these gaps, in this paper, authors put forward a new integrated framework, which considers double hierarchy linguistic information for rating, criteria importance through inter-criteria correlation (CRITIC) for expert weight calculation, evidence-based Bayesian method for criteria weight estimation, and combined compromise solution (CoCoSo) for ranking HSMs. The applicability of the developed framework is testified by using a case example of HSM selection in India. Sensitivity and comparative analysis reveal the merits and limitations of the developed framework
Solving Vehicle Routing Problem Using a Hybridization of Gain-Based Ant Colony Optimization and Firefly Algorithms
(Hand Book of Formal Optimization, 2023-08-13) Sangeetha, V.; Krishankumar, R.; Pamucar,Dragan; Ravichandran, K. S.; Peng,Xindong; Gandomi, Amir H.
Vehicle Routing Problem is one of the classical NP hard and combinatorial optimization problems that has been a spark of interest in the operation research domain. Though many variations of classical VRP are being developed, there is still the need for developing algorithms to improve solutions for VRP. A hybrid gain-based ant colony optimization-firefly algorithm (GACO-FA) has been proposed to deal with VRP. A global search is initially performed using the gain-based ant colony optimization, and subsequently local search for promising solution is done in the fine-tuned search space using firefly algorithm. The strengths of GACO and weakness of FA are aptly managed with a finer trade-off between them. The proposed GACO-FA is compared with best-known solutions and existing algorithms for performance analysis using the benchmark dataset. Analysis has been performed using measures like route cost, standard deviation, and percentage variation in length. The results have also been statistically verified for their significance.