L
Lakshman S. Thakur
Researcher at University of Connecticut
Publications - 34
Citations - 1710
Lakshman S. Thakur is an academic researcher from University of Connecticut. The author has contributed to research in topics: Scheduling (production processes) & Job shop scheduling. The author has an hindex of 18, co-authored 34 publications receiving 1459 citations. Previous affiliations of Lakshman S. Thakur include Shippensburg University of Pennsylvania.
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Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain
TL;DR: This study presents an integrated approach for selecting the appropriate supplier in the supply chain, addressing the carbon emission issue, using fuzzy-AHP and fuzzy multi-objective linear programming.
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Near optimal solutions for product line design and selection: beam search heuristics
TL;DR: Improved heuristics based on a beam search approach for solving product line design problems are developed and are closer to the optimal, have smaller standard deviation over replicates, take less computation time, obtain optimal solutions more often and identify a number of "good" product lines explicitly.
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A big data driven sustainable manufacturing framework for condition-based maintenance prediction
TL;DR: A big data analytics framework that optimizes the maintenance schedule through condition-based maintenance (CBM) optimization and also improves the prediction accuracy to quantify the remaining life prediction uncertainty and outpaces the classical methods in terms of classification accuracy and other statistical performance evaluation metrics.
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Total Interpretive Structural Modeling (TISM): approach and application
TL;DR: This is the first study that analyzes the inhibitors of SMED by utilizing TISM approach and highlights the significance of TISM over conventional interpretive structural modeling (ISM) in order to provide interpretation for direct as well as significant transitive linkages in a directed graph.
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A big data MapReduce framework for fault diagnosis in cloud-based manufacturing
TL;DR: A MapReduce framework for automatic pattern recognition based on fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM) and a comparative study shows that the methods used in the proposed framework outperform the traditional ones.