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Pandian Vasant

Researcher at Universiti Teknologi Petronas

Publications -  316
Citations -  4572

Pandian Vasant is an academic researcher from Universiti Teknologi Petronas. The author has contributed to research in topics: Fuzzy logic & Optimization problem. The author has an hindex of 30, co-authored 312 publications receiving 3805 citations. Previous affiliations of Pandian Vasant include Ton Duc Thang University & Curtin University.

Papers
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Journal ArticleDOI

Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures

TL;DR: In this paper, the literature bearing on the use of optimization methods for charging infrastructure is considered, and the authors present a review study of the literature related to the optimization of EV charging infrastructure.
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Industry 4.0 framework for management and operations: a review

TL;DR: The purpose of the study is to analyze categorically recent advances through qualitative and segmentation methods allowing to reveal trends and areas of opportunity in the industry sense of 4.0 in order to obtain research gaps that may be executed in organizational systems on the value chain.
Book

Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance

TL;DR: This book explores the emerging study of meta-heuristics optimization algorithms and methods and their role in innovated real world practical applications and aims to be a comprehensive reference for decision makers, managers, engineers, researchers, scientists, financiers, and economists as well as industrialists.
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Soft-sensing of level of satisfaction in TOC product-mix decision heuristic using robust fuzzy-LP

TL;DR: Key objective of this work is to guide DMs in finding out the optimal product-mix with higher degree of satisfaction with lesser degree of fuzziness under tripartite fuzzy environment.
Journal ArticleDOI

A holistic review on optimization strategies for combined economic emission dispatch problem

TL;DR: In this article, a comprehensive review on the uses of different optimization techniques to solve the combined economic emission dispatch (CEED) problem is provided, where advanced nature-inspired methods are found to be the most suitable and successful.