Academic Journal
Approximate Resolution of Stochastic Choice-Based Discrete Planning.
| Title: | Approximate Resolution of Stochastic Choice-Based Discrete Planning. |
|---|---|
| Authors: | Zhang, Jiajie1 (AUTHOR) jiajiez@u.nus.edu, Lin, Yun Hui2 (AUTHOR) linyhie@gmail.com, Berbeglia, Gerardo3 (AUTHOR) g.berbeglia@mbs.edu |
| Source: | INFORMS Journal on Computing. Jan/Feb2026, Vol. 38 Issue 1, p232-252. 21p. |
| Subject Terms: | *Simulation methods & models, *Decision making, Mixed integer linear programming, Decomposition method, Location problems (Programming), Approximation algorithms |
| Abstract: | Stochastic choice-based discrete planning is a broad class of decision-making problems characterized by a sequential decision-making process involving a planner and a group of customers. The firm or planner first decides a subset of options to offer to the customers who, in turn, make selections based on their utilities of those options. This problem has extensive applications in many areas, including assortment planning, product line design, and facility location. A key feature of these problems is that the firm cannot fully observe the customers' utilities or preferences, which results from intrinsic and idiosyncratic uncertainties. Most works in the literature have studied a specific type of uncertainty, resulting in customized decision models that are subsequently tackled using ad hoc algorithms designed to exploit the specific model structure. In this paper, we propose a modeling framework capable of solving this family of sequential problems that works for a large variety of uncertainties. We then leverage an approximation scheme and develop an adaptable mixed-integer linear programming method. To speed up the solution process, we further develop an efficient decomposition approach. We show that our solution framework can yield solutions proven to be (near-)optimal for a broad class of problems. We illustrate this by applying our approach to three classical application problems: constrained assortment optimization and two facility location problems. Through extensive computational experiments, we demonstrate the performance of our approach in terms of both solution quality and computational speed, and we provide computational insights. In particular, when we use our method to solve the constrained assortment optimization problem under the exponomial choice model, it improves the state of the art. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Y. H. Lin was supported by the National Natural Science Foundation of China [Grant 72288101]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0694) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0694). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR] |
| Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences 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: | Business Source Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: bsx DbLabel: Business Source Index An: 191521609 RelevancyScore: 1470 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1470.35424804688 |
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| Items | – Name: Title Label: Title Group: Ti Data: Approximate Resolution of Stochastic Choice-Based Discrete Planning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Jiajie%22">Zhang, Jiajie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jiajiez@u.nus.edu</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Yun+Hui%22">Lin, Yun Hui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> linyhie@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Berbeglia%2C+Gerardo%22">Berbeglia, Gerardo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> g.berbeglia@mbs.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22INFORMS+Journal+on+Computing%22">INFORMS Journal on Computing</searchLink>. Jan/Feb2026, Vol. 38 Issue 1, p232-252. 21p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br />*<searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Decomposition+method%22">Decomposition method</searchLink><br /><searchLink fieldCode="DE" term="%22Location+problems+%28Programming%29%22">Location problems (Programming)</searchLink><br /><searchLink fieldCode="DE" term="%22Approximation+algorithms%22">Approximation algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Stochastic choice-based discrete planning is a broad class of decision-making problems characterized by a sequential decision-making process involving a planner and a group of customers. The firm or planner first decides a subset of options to offer to the customers who, in turn, make selections based on their utilities of those options. This problem has extensive applications in many areas, including assortment planning, product line design, and facility location. A key feature of these problems is that the firm cannot fully observe the customers' utilities or preferences, which results from intrinsic and idiosyncratic uncertainties. Most works in the literature have studied a specific type of uncertainty, resulting in customized decision models that are subsequently tackled using ad hoc algorithms designed to exploit the specific model structure. In this paper, we propose a modeling framework capable of solving this family of sequential problems that works for a large variety of uncertainties. We then leverage an approximation scheme and develop an adaptable mixed-integer linear programming method. To speed up the solution process, we further develop an efficient decomposition approach. We show that our solution framework can yield solutions proven to be (near-)optimal for a broad class of problems. We illustrate this by applying our approach to three classical application problems: constrained assortment optimization and two facility location problems. Through extensive computational experiments, we demonstrate the performance of our approach in terms of both solution quality and computational speed, and we provide computational insights. In particular, when we use our method to solve the constrained assortment optimization problem under the exponomial choice model, it improves the state of the art. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Y. H. Lin was supported by the National Natural Science Foundation of China [Grant 72288101]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0694) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0694). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences 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: 10.1287/ijoc.2024.0694 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 232 Subjects: – SubjectFull: Simulation methods & models Type: general – SubjectFull: Decision making Type: general – SubjectFull: Mixed integer linear programming Type: general – SubjectFull: Decomposition method Type: general – SubjectFull: Location problems (Programming) Type: general – SubjectFull: Approximation algorithms Type: general Titles: – TitleFull: Approximate Resolution of Stochastic Choice-Based Discrete Planning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Jiajie – PersonEntity: Name: NameFull: Lin, Yun Hui – PersonEntity: Name: NameFull: Berbeglia, Gerardo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan/Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10919856 Numbering: – Type: volume Value: 38 – Type: issue Value: 1 Titles: – TitleFull: INFORMS Journal on Computing Type: main |
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