R
Remica Aggarwal
Researcher at Birla Institute of Technology and Science
Publications - 85
Citations - 215
Remica Aggarwal is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Selection (genetic algorithm) & Supply chain. The author has an hindex of 7, co-authored 85 publications receiving 186 citations. Previous affiliations of Remica Aggarwal include Indian Institute of Technology Delhi & University of Delhi.
Papers
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Journal ArticleDOI
AHP and Extent Fuzzy AHP Approach for Prioritization of Performance Measurement Attributes
Remica Aggarwal,Sanjeet Singh +1 more
TL;DR: This study aims at defining a methodology to improve the quality of prioritization of an employee’s performance measurement attributes under fuzziness and proposes a methodology based on the Extent Fuzzy Analytic Hierarchy Process.
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Chance constraint-based multi-objective stochastic model for supplier selection
TL;DR: An attempt is made to model Stochastic Multi-objective Supplier Selection Problem (SMoSSP) applying chance constraint approach and the proposed model considers operational risks involving uncertainties-related supplier’s capacity, product demand, transportation and variable costs and lead time probability distributions.
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Optimal media planning for multi-products in segmented market
TL;DR: A media planning problem for allocating the available budget in multiple media that are found suitable for the advertising of multiple products considering marketing segmentation aspect of advertising is formulated.
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Optimal inventory policies for successive generations of a high technology product
Udayan Chanda,Remica Aggarwal +1 more
TL;DR: In this article, an attempt has been made to generate economic inventory policies for technology products under the condition of its diminishing demand, based on the assumption that technological advancements do not essentially imply that existing generation products will be withdrawn from the market immediately.
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Integrated dynamic vendor selection and order allocation problem for the time dependent and stochastic data
TL;DR: The proposed multi-objective IDVSP is solved using both non-pre-emptive goal programming (GP) and weighted sum aggregate objective function (AOF) technique, which integrates dynamic as well as stochastic nature of supply chain simultaneously coupled with the concept of incremental quantity discounts on lot sizes.