Distributionally robust multi-item newsvendor problem with covariate information
This dissertation investigates robust optimization for use in demand forecasting. Techniques of robust optimization such as construction of ambiguity set, robust counterpart and affine recourse approximation are carefully studied. In addition, we’ve included the use of machine-learning technique in...
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Nanyang Technological University
2020
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sg-ntu-dr.10356-1390252023-02-28T23:10:44Z Distributionally robust multi-item newsvendor problem with covariate information Lee, Jonathan Heng Yow Yan Zhenzhen School of Physical and Mathematical Sciences yanzz@ntu.edu.sg Science::Mathematics This dissertation investigates robust optimization for use in demand forecasting. Techniques of robust optimization such as construction of ambiguity set, robust counterpart and affine recourse approximation are carefully studied. In addition, we’ve included the use of machine-learning technique in our ambiguity set construction and evaluated methods of machine-learning such as K-means Clustering and Classification and Regression Tree (CART). In our project, we considered the problem of a manager selling multiple product in a single period model. We evaluated cases where the seller considers/include uncertain covariates and/or cross-price elasticity using two different linear decision rule, i.e. Partial Affine Recourse Approximation (PARA) and Full Affine Recourse Approximation (FARA). Bachelor of Science in Mathematics and Economics 2020-05-15T01:27:06Z 2020-05-15T01:27:06Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/139025 en application/pdf Nanyang Technological University |
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Science::Mathematics Lee, Jonathan Heng Yow Distributionally robust multi-item newsvendor problem with covariate information |
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This dissertation investigates robust optimization for use in demand forecasting. Techniques of robust optimization such as construction of ambiguity set, robust counterpart and affine recourse approximation are carefully studied. In addition, we’ve included the use of machine-learning technique in our ambiguity set construction and evaluated methods of machine-learning such as K-means Clustering and Classification and Regression Tree (CART). In our project, we considered the problem of a manager selling multiple product in a single period model. We evaluated cases where the seller considers/include uncertain covariates and/or cross-price elasticity using two different linear decision rule, i.e. Partial Affine Recourse Approximation (PARA) and Full Affine Recourse Approximation (FARA). |
author2 |
Yan Zhenzhen |
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Yan Zhenzhen Lee, Jonathan Heng Yow |
format |
Final Year Project |
author |
Lee, Jonathan Heng Yow |
author_sort |
Lee, Jonathan Heng Yow |
title |
Distributionally robust multi-item newsvendor problem with covariate information |
title_short |
Distributionally robust multi-item newsvendor problem with covariate information |
title_full |
Distributionally robust multi-item newsvendor problem with covariate information |
title_fullStr |
Distributionally robust multi-item newsvendor problem with covariate information |
title_full_unstemmed |
Distributionally robust multi-item newsvendor problem with covariate information |
title_sort |
distributionally robust multi-item newsvendor problem with covariate information |
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Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/139025 |
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1759852924800860160 |