Flexible Enterprise Optimization with Constraint Programming

Sytze P.E. Andringa; Neil Yorke-Smith

doi:10.1007/978-3-031-11520-2_5

Flexible Enterprise Optimization with Constraint Programming

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Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

Simulation–optimization is often used in enterprise decision-making processes, both operational and tactical. This paper shows how an intuitive mapping from descriptive problem to optimization model can be realized with Constraint Programming (CP). It shows how a CP model can be constructed given a simulation model and a set of business goals. The approach is to train a neural network (NN) on simulation model inputs and outputs, and embed the NN into the CP model together with a set of soft constraints that represent business goals. We study this novel simulation–optimization approach through a set of experiments, finding that it is flexible to changing multiple objectives simultaneously, allows an intuitive mapping from business goals expressed in natural language to a formal model suitable for state-of-the-art optimization solvers, and is realizable for diverse managerial problems.

Original language	English
Title of host publication	Advances in Enterprise Engineering XV - 11th Enterprise Engineering Working Conference, EEWC 2021, Revised Selected Papers
Editors	David Aveiro, Henderik A. Proper, Sérgio Guerreiro, Marne de Vries
Publisher	Springer
Pages	58-73
Number of pages	16
ISBN (Electronic)	978-3-031-11520-2
ISBN (Print)	978-3-031-11519-6
DOIs	https://doi.org/10.1007/978-3-031-11520-2_5
Publication status	Published - 2022
Event	11th Enterprise Engineering Working Conference, EEWC 2021 - Virtual, Online Duration: 16 Dec 2021 → 17 Dec 2021

Publication series

Name	Lecture Notes in Business Information Processing
Volume	441 LNBIP
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Conference

Conference	11th Enterprise Engineering Working Conference, EEWC 2021
City	Virtual, Online
Period	16/12/21 → 17/12/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Constraint Programming
Deep learning
Enterprise simulation
Simulation–optimization

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10.1007/978-3-031-11520-2_5

Andringa_Yorke_Smith2022_Chapter_FlexibleEnterpriseOptimizationFinal published version, 990 KB

Cite this

Andringa, S. P. E., & Yorke-Smith, N. (2022). Flexible Enterprise Optimization with Constraint Programming. In D. Aveiro, H. A. Proper, S. Guerreiro, & M. de Vries (Eds.), Advances in Enterprise Engineering XV - 11th Enterprise Engineering Working Conference, EEWC 2021, Revised Selected Papers (pp. 58-73). (Lecture Notes in Business Information Processing; Vol. 441 LNBIP). Springer. https://doi.org/10.1007/978-3-031-11520-2_5

Andringa, Sytze P.E. ; Yorke-Smith, Neil. / Flexible Enterprise Optimization with Constraint Programming. Advances in Enterprise Engineering XV - 11th Enterprise Engineering Working Conference, EEWC 2021, Revised Selected Papers. editor / David Aveiro ; Henderik A. Proper ; Sérgio Guerreiro ; Marne de Vries. Springer, 2022. pp. 58-73 (Lecture Notes in Business Information Processing).

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Andringa, SPE & Yorke-Smith, N 2022, Flexible Enterprise Optimization with Constraint Programming. in D Aveiro, HA Proper, S Guerreiro & M de Vries (eds), Advances in Enterprise Engineering XV - 11th Enterprise Engineering Working Conference, EEWC 2021, Revised Selected Papers. Lecture Notes in Business Information Processing, vol. 441 LNBIP, Springer, pp. 58-73, 11th Enterprise Engineering Working Conference, EEWC 2021, Virtual, Online, 16/12/21. https://doi.org/10.1007/978-3-031-11520-2_5

Flexible Enterprise Optimization with Constraint Programming. / Andringa, Sytze P.E.; Yorke-Smith, Neil.
Advances in Enterprise Engineering XV - 11th Enterprise Engineering Working Conference, EEWC 2021, Revised Selected Papers. ed. / David Aveiro; Henderik A. Proper; Sérgio Guerreiro; Marne de Vries. Springer, 2022. p. 58-73 (Lecture Notes in Business Information Processing; Vol. 441 LNBIP).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Andringa, Sytze P.E.

AU - Yorke-Smith, Neil

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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N2 - Simulation–optimization is often used in enterprise decision-making processes, both operational and tactical. This paper shows how an intuitive mapping from descriptive problem to optimization model can be realized with Constraint Programming (CP). It shows how a CP model can be constructed given a simulation model and a set of business goals. The approach is to train a neural network (NN) on simulation model inputs and outputs, and embed the NN into the CP model together with a set of soft constraints that represent business goals. We study this novel simulation–optimization approach through a set of experiments, finding that it is flexible to changing multiple objectives simultaneously, allows an intuitive mapping from business goals expressed in natural language to a formal model suitable for state-of-the-art optimization solvers, and is realizable for diverse managerial problems.

AB - Simulation–optimization is often used in enterprise decision-making processes, both operational and tactical. This paper shows how an intuitive mapping from descriptive problem to optimization model can be realized with Constraint Programming (CP). It shows how a CP model can be constructed given a simulation model and a set of business goals. The approach is to train a neural network (NN) on simulation model inputs and outputs, and embed the NN into the CP model together with a set of soft constraints that represent business goals. We study this novel simulation–optimization approach through a set of experiments, finding that it is flexible to changing multiple objectives simultaneously, allows an intuitive mapping from business goals expressed in natural language to a formal model suitable for state-of-the-art optimization solvers, and is realizable for diverse managerial problems.

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ER -

Andringa SPE, Yorke-Smith N. Flexible Enterprise Optimization with Constraint Programming. In Aveiro D, Proper HA, Guerreiro S, de Vries M, editors, Advances in Enterprise Engineering XV - 11th Enterprise Engineering Working Conference, EEWC 2021, Revised Selected Papers. Springer. 2022. p. 58-73. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-031-11520-2_5

Flexible Enterprise Optimization with Constraint Programming

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Supplementary code to the paper: Flexible Enterprise Optimization With Constraint Programming

Cite this

Flexible Enterprise Optimization with Constraint Programming

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Datasets

Supplementary code to the paper: Flexible Enterprise Optimization With Constraint Programming

Cite this