Browsing by Author "Gandomi, Amir H."

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Ranking Barriers Impeding Sustainability Adoption in Clean Energy Supply Chains: A Hybrid Framework With Fermatean Fuzzy Data
(IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT,, 2024) Krishankumar,Raghunathan; Ramanujam, Niranjana; Zavadskas, Edmundas Kazimieras; Ravichandran,Kattur Soundarapandian; Gandomi, Amir H.
—In this article, we aim to prioritize barriers hindering sustainability inclusion within clean energy supply chains. Supply chain management is a crucial aspect of the clean energy sector, whereby the global supply chains can be enforced with policies to adopt sustainability/green practices. The literature infers that the adoption of sustainability is not direct, and multiple barriers impede the process, driving researchers to rank these barriers. Previous studies on prioritizing barriers cannot effectively model uncertainty; experts’ reliability is directly assigned; interrelationships/hesitation of criteria/experts are usually not considered; and there is a lack of personalized ordering based on individuals’ preferences. Motivated by these gaps, the authors put forward an integrated framework with a Fermatean fuzzy set, variance-based criteria importance through intercriteria correlation for determining experts’ and criteria weights, and ranking procedure with complex proportional assessment–Copeland for personalized ordering of barriers. The usefulness of the developed approach is testified through a case example. Results infer that wastage/pollution reduction and profit from green production are the two top criteria considered for rating sustainability barriers, while limited governmental policies, monitoring/control issues, and expertise mismatch are the top three barriers impeding sustainability adoption. Finally, sensitivity and comparative analyses are performed to understand the framework’s efficacy
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.