C
Chandrasekharan Rajendran
Researcher at Indian Institute of Technology Madras
Publications - 197
Citations - 10083
Chandrasekharan Rajendran is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Job shop scheduling & Supply chain. The author has an hindex of 52, co-authored 192 publications receiving 9404 citations. Previous affiliations of Chandrasekharan Rajendran include Indian Institutes of Technology & VIT University.
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A simulation‐based genetic algorithm for inventory optimization in a serial supply chain
TL;DR: It is found that the solutions generated by the proposed GA do not significantly differ from the optimal solution obtained through complete enumeration for different supply chain settings, thereby showing the effectiveness of the proposedGA.
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Heuristic algorithms for continuous flow-shop problem
TL;DR: In this article, two heuristic preference relations are used as the basis for job insertion to build up a schedule by the heuristics, when evaluated over a large number of problems of various sizes, they were found to be very effective in yielding near-optimal solutions.
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Two-Stage Flowshop Scheduling Problem with Bicriteria
TL;DR: The two-stage flowshop scheduling problem with the objective of minimizing total flowtime subject to obtaining the optimal makespan is discussed and a branch-and-bound algorithm and two heuristic algorithms have been developed.
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Scales to measure and benchmark service quality in tourism industry: A second‐order factor approach
TL;DR: In this article, the authors developed and validated scales to measure and benchmark service quality in tourism industry, and the second-order confirmatory factor analysis is employed to validate the instrument, which has been modeled which have significant impact on customer satisfaction separately from those which do not have a significant impact.
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The value of information sharing in a multi-product, multi-level supply chain: Impact of product substitution, demand correlation, and partial information sharing
TL;DR: The results suggest that firms, especially those that are upstream in the supply chain, may face a significant risk of over-estimating the value of information sharing if they ignore substitution, demand correlation, and partial information sharing effects.