Author
Lakshman S. Thakur
Other affiliations: Shippensburg University of Pennsylvania
Bio: 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.
Papers
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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.
Abstract: Environmental sustainability of a supply chain depends on the purchasing strategy of the supply chain members. Most of the earlier models have focused on cost, quality, lead time, etc. issues but not given enough importance to carbon emission for supplier evaluation. Recently, there is a growing pressure on supply chain members for reducing the carbon emission of their supply chain. 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. Fuzzy AHP (FAHP) is applied first for analyzing the weights of the multiple factors. The considered factors are cost, quality rejection percentage, late delivery percentage, green house gas emission and demand. These weights of the multiple factors are used in fuzzy multi-objective linear programming for supplier selection and quota allocation. An illustration with a data set from a realistic situation is presented to demonstrate the effectiveness of the proposed model. The proposed approach can handle realistic situation when there is information vagueness related to inputs.
552 citations
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.
Abstract: Many practical product line design problems have large numbers of attributes and levels. In this case, if most attribute level combinations define feasible products, constructing product lines directly from part-worths data is necessary. For three typical formulations of this important problem, Kohli and Sukumar Kohli, R., R. Sukumar. 1990. Heuristics for product-line design using conjoint analysis. Management Sci.36 1464-1478. present state-of-the-art heuristics to find good solutions. In this paper, we develop improved heuristics based on a beam search approach for solving these problems.
In our computations for 435 simulated problems, significant improvements occur in five important performance measures used. Our heuristic solutions 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. Computation times for these problems are no more than 22 seconds on a PC, small enough for adequate sensitivity analysis. We also apply the heuristics to a real data set and clarify computational steps by giving a detailed example.
153 citations
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.
127 citations
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.
Abstract: Purpose
The purpose of this paper is to elucidate the methodology of total interpretive structural modeling (TISM) in order to provide interpretation for direct as well as significant transitive linkages in a directed graph.
Design/methodology/approach
This study begins by unfolding the concepts and advantages of TISM. The step-by-step methodology of TISM is exemplified by employing it to analyze the mutual dependence among inhibitors of smartphone manufacturing ecosystem development (SMED). Cross-impact matrix multiplication applied to the classification analysis is also performed to graphically represent these inhibitors based on their driving power and dependence.
Findings
This study highlights the significance of TISM over conventional interpretive structural modeling (ISM). The inhibitors of SMED are explored by reviewing existing literature and obtaining experts’ opinions. TISM is employed to classify these inhibitors in order to devise a five-level hierarchical structure based on their driving power and dependence.
Practical implications
This study facilitates decision makers to take required actions to mitigate these inhibitors. Inhibitors (with strong driving power), which occupy the bottom level in the TISM hierarchy, require more attention from top management and effective monitoring of these inhibitors can assist in achieving the organizations’ goals.
Originality/value
By unfolding the benefits of TISM over ISM, this study is an endeavor to develop insights toward utilization of TISM for modeling inhibitors of SMED. This paper elaborates step-by-step procedure to perform TISM and hence makes it simple for researchers to understand its concepts. To the best of the authors’ knowledge, this is the first study that analyzes the inhibitors of SMED by utilizing TISM approach.
122 citations
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.
Abstract: This research develops a MapReduce framework for automatic pattern recognition based on fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM). Fault diagnosis in a CBM system significantly contributes to reduce the product testing cost and enhances manufacturing quality. One of the major challenges facing the big data analytics in CBM is handling of data-sets, which are highly imbalanced in nature due to poor classification result when machine learning techniques are applied on such data-sets. The framework proposed in this research uses a hybrid approach to deal with big data-set for smarter decisions. Furthermore, we compare the performance of radial basis function-based Support Vector Machine classifier with standard techniques. Our findings suggest that the most important task in CBM is to predict the effect of data errors on quality due to highly imbalance unstructured data-set. The proposed framework is an original contribution to the body of literature, where our pr...
98 citations
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TL;DR: A systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection is provided by using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches.
Abstract: Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection.
825 citations
TL;DR: In this paper, a green vehicle routing problem (G-VRP) is formulated and solution techniques are developed to aid organizations with alternative fuel-powered vehicle fleets in overcoming difficulties that exist as a result of limited vehicle driving range in conjunction with limited refueling infrastructure.
Abstract: A Green Vehicle Routing Problem (G-VRP) is formulated and solution techniques are developed to aid organizations with alternative fuel-powered vehicle fleets in overcoming difficulties that exist as a result of limited vehicle driving range in conjunction with limited refueling infrastructure The G-VRP is formulated as a mixed integer linear program Two construction heuristics, the Modified Clarke and Wright Savings heuristic and the Density-Based Clustering Algorithm, and a customized improvement technique, are developed Results of numerical experiments show that the heuristics perform well Moreover, problem feasibility depends on customer and station location configurations Implications of technology adoption on operations are discussed
763 citations
05 Sep 2001
TL;DR: Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.
Abstract: Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.
636 citations