Explainable subgradient tree boosting for prescriptive analytics in operations management.
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| Title: | Explainable subgradient tree boosting for prescriptive analytics in operations management. |
|---|---|
| Authors: | Notz, Pascal M.1 (AUTHOR) pascal.notz@uni-wuerzburg.de, Pibernik, Richard1,2 (AUTHOR) |
| Source: | European Journal of Operational Research. Feb2024, Vol. 312 Issue 3, p1119-1133. 15p. |
| Subjects: | Decision support systems, Subgradient methods, Data analytics, Function spaces, Cost functions |
| Abstract: | • This paper proposes a novel prescriptive analytics approach for Operations Management: Subgradient Tree Boosting (STB). • STB can be used to solve complex stochastic optimization problems with convex cost functions. • STB provides detailed explanations for the prescribed decisions using Shapley additive explanations. • Case study conducted using real historical demand and feature data for two capacity planning problems. • Results suggest that STB provides prescriptions of similar quality as kERM or wSAA while also providing explanations. Motivated by the success of gradient boosting approaches in machine learning and driven by the need for explainable prescriptive analytics approaches in operations management (OM), we propose subgradient tree boosting (STB) as an explainable prescriptive analytics approach to solving convex stochastic optimization problems that frequently arise in OM. The STB approach combines the well-known method of subgradient descent in function space with sample average approximation, and prescribes decisions from a problem-specific loss function, historical demand observations, and prescriptive features. The approach provides a decision-maker with detailed explanations for the prescribed decisions, such as a breakdown of individual features' impact. These explanations are particularly valuable in practice when the decision-maker has the discretion to adjust the recommendations made by a decision support system. We show how subgradients can be derived for common single-stage and two-stage stochastic optimization problems; demonstrate the STB approach's applicability to two real-world, complex capacity-planning problems in the service industry; benchmark the STB approach's performance against those of two prescriptive approaches—weighted sample average approximation (wSAA) and kernelized empirical risk minimization (kERM); and show how the STB approach's prescriptions can be explained by estimating the impact of individual features. The results suggest that the quality of the STB approach's prescriptions is comparable to that of wSAA's and kERM's prescriptions while also providing explanations. [ABSTRACT FROM AUTHOR] |
| Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 172366202 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Explainable subgradient tree boosting for prescriptive analytics in operations management. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Notz%2C+Pascal+M%2E%22">Notz, Pascal M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pascal.notz@uni-wuerzburg.de</i><br /><searchLink fieldCode="AR" term="%22Pibernik%2C+Richard%22">Pibernik, Richard</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. Feb2024, Vol. 312 Issue 3, p1119-1133. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Subgradient+methods%22">Subgradient methods</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Function+spaces%22">Function spaces</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • This paper proposes a novel prescriptive analytics approach for Operations Management: Subgradient Tree Boosting (STB). • STB can be used to solve complex stochastic optimization problems with convex cost functions. • STB provides detailed explanations for the prescribed decisions using Shapley additive explanations. • Case study conducted using real historical demand and feature data for two capacity planning problems. • Results suggest that STB provides prescriptions of similar quality as kERM or wSAA while also providing explanations. Motivated by the success of gradient boosting approaches in machine learning and driven by the need for explainable prescriptive analytics approaches in operations management (OM), we propose subgradient tree boosting (STB) as an explainable prescriptive analytics approach to solving convex stochastic optimization problems that frequently arise in OM. The STB approach combines the well-known method of subgradient descent in function space with sample average approximation, and prescribes decisions from a problem-specific loss function, historical demand observations, and prescriptive features. The approach provides a decision-maker with detailed explanations for the prescribed decisions, such as a breakdown of individual features' impact. These explanations are particularly valuable in practice when the decision-maker has the discretion to adjust the recommendations made by a decision support system. We show how subgradients can be derived for common single-stage and two-stage stochastic optimization problems; demonstrate the STB approach's applicability to two real-world, complex capacity-planning problems in the service industry; benchmark the STB approach's performance against those of two prescriptive approaches—weighted sample average approximation (wSAA) and kernelized empirical risk minimization (kERM); and show how the STB approach's prescriptions can be explained by estimating the impact of individual features. The results suggest that the quality of the STB approach's prescriptions is comparable to that of wSAA's and kERM's prescriptions while also providing explanations. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ejor.2023.08.037 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1119 Subjects: – SubjectFull: Decision support systems Type: general – SubjectFull: Subgradient methods Type: general – SubjectFull: Data analytics Type: general – SubjectFull: Function spaces Type: general – SubjectFull: Cost functions Type: general Titles: – TitleFull: Explainable subgradient tree boosting for prescriptive analytics in operations management. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Notz, Pascal M. – PersonEntity: Name: NameFull: Pibernik, Richard IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 03772217 Numbering: – Type: volume Value: 312 – Type: issue Value: 3 Titles: – TitleFull: European Journal of Operational Research Type: main |
| ResultId | 1 |
