{"id":2735,"date":"2023-02-21T19:21:22","date_gmt":"2023-02-21T19:21:22","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/speakers\/heping-liu-copy\/"},"modified":"2023-02-21T19:28:58","modified_gmt":"2023-02-21T19:28:58","slug":"madhushini-narayana-prasad","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/speakers\/madhushini-narayana-prasad\/","title":{"rendered":"Madhushini Narayana Prasad"},"content":{"rendered":"\n
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\n Madhushini Narayana Prasad <\/h3>\n <\/div>\n \n <\/div>\n <\/div>\n
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Madhushini Narayana Prasad<\/span><\/h1>\n

Lead Data Scientist at Cargill R&D<\/span><\/h1>\n
Madhu Narayana Prasad is a lead data scientist at Engineering and Data Sciences Division of Cargill R&D. She is passionate about designing and implementing efficient and scalable optimization algorithms to solve large-scale high-value industry problems. Madhu holds a PhD degree in Operations Research and Industrial Engineering from The University of Texas at Austin and has 7+ years industry experience in the field of supply chain analytics and optimization. She specializes in the application of operations research methodology to industries spanning heavy equipment manufacturing, airline systems, oil & gas and food & agriculture.<\/div> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Track: Supply Chain<\/h6> <\/div>\n<\/div>\n\n
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Multi-Echelon Mix and Blend Optimization in Grains Supply Chain<\/h2>\n

This work focuses on designing an agile supply chain model to optimize the flow of soybean and corn grains through a multi-echelon network of facilities in Brazil. The key objective of this model is multifold starting from maximizing the throughput utilization of facility assets, gains from mixing and blending to minimizing the demurrage of river barges, while proactively tackling business constraints such as limited storage capacity, drying capacity and higher production volumes during the peak harvest seasons. While the problem statement seems straight-forward, the main challenge arises from blending equations being quadratic in nature rendering the problem computationally intractable. We employ novel approximation techniques to replace the large MINLP formulation and the proposed MILP is solved using Gurobi, to derive a 15-day tactical plan of good solution quality within reasonable computational effort.<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n","protected":false},"excerpt":{"rendered":"

Madhushini Narayana Prasad Madhushini Narayana Prasad Lead Data Scientist at Cargill R&D Madhu Narayana Prasad is a lead data scientist at Engineering and Data Sciences Division of Cargill R&D. She is passionate about designing and implementing efficient and scalable optimization algorithms to solve large-scale high-value industry problems. Madhu holds a PhD degree in Operations Research […]<\/p>\n","protected":false},"author":1001094,"featured_media":2648,"parent":712,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"class_list":["post-2735","page","type-page","status-publish","has-post-thumbnail","hentry","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"acf":[],"yoast_head":"\nMadhushini Narayana Prasad - 2023 INFORMS Business Analytics Conference<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/speakers\/madhushini-narayana-prasad\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Madhushini Narayana Prasad\" \/>\n<meta property=\"og:description\" content=\"Madhushini Narayana Prasad Madhushini Narayana Prasad Lead Data Scientist at Cargill R&D Madhu Narayana Prasad is a lead data scientist at Engineering and Data Sciences Division of Cargill R&D. 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She is passionate about designing and implementing efficient and scalable optimization algorithms to solve large-scale high-value industry problems. Madhu holds a PhD degree in Operations Research and Industrial Engineering from The University of Texas at Austin and has 7+ years industry experience in the field of supply chain analytics and optimization. She specializes in the application of operations research methodology to industries spanning heavy equipment manufacturing, airline systems, oil & gas and food & agriculture.\n<h6>Track: Supply Chain<\/h6>\n<h2>Multi-Echelon Mix and Blend Optimization in Grains Supply Chain<\/h2> <p>This work focuses on designing an agile supply chain model to optimize the flow of soybean and corn grains through a multi-echelon network of facilities in Brazil. 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