On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty

In this work a modeling framework and a solution approach have been presented for a multi-period stochastic mixed 0–1 problem arising in tactical supply chain planning (TSCP). A multistage scenario tree based scheme is used to represent the parameters’ uncertainty and develop the related Determinist...

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Detalles Bibliográficos
Autores: Escudero, Laureano F., Monge Ivars, Juan Francisco, Romero Morales, Dolores
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Miguel Hernández de Elche
Repositorio:REDIUMH. Depósito Digital de la UMH
OAI Identifier:oai:dspace.umh.es:11000/6432
Acceso en línea:http://hdl.handle.net/11000/6432
Access Level:acceso abierto
Palabra clave:Tactical supply chain planning
Nonlinear separable objective function
Multistage stochastic integer optimization
Risk management
Time-consistency
Stochastic nested decomposition
517 - Análisis
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spelling On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertaintyEscudero, Laureano F.Monge Ivars, Juan FranciscoRomero Morales, DoloresTactical supply chain planningNonlinear separable objective functionMultistage stochastic integer optimizationRisk managementTime-consistencyStochastic nested decomposition517 - AnálisisIn this work a modeling framework and a solution approach have been presented for a multi-period stochastic mixed 0–1 problem arising in tactical supply chain planning (TSCP). A multistage scenario tree based scheme is used to represent the parameters’ uncertainty and develop the related Deterministic Equivalent Model. A cost risk reduction is performed by using a new time-consistent risk averse measure. Given the dimensions of this problem in real-life applications, a decomposition approach is proposed. It is based on stochastic dynamic programming (SDP). The computational experience is twofold, a compar- ison is performed between the plain use of a current state-of-the-art mixed integer optimization solver and the proposed SDP decomposition approach considering the risk neutral version of the model as the subject for the benchmarking. The add-value of the new risk averse strategy is confirmed by the compu- tational results that are obtained using SDP for both versions of the TSCP model, namely, risk neutral and risk averse.The authors would like to thank to the two anonymous reviewers for their help on clarifying some concepts presented in the manuscript and strongly improving its presentatioDepartamentos de la UMH::Estadística, Matemáticas e Informática2020202020202020info:eu-repo/semantics/articleapplication/pdf17application/pdfhttp://hdl.handle.net/11000/6432reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheIngléshttp://dx.doi.org/10.1016/j.cor.2017.07.011info:eu-repo/semantics/openAccessoai:dspace.umh.es:11000/64322026-05-27T13:36:21Z
dc.title.none.fl_str_mv On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty
title On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty
spellingShingle On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty
Escudero, Laureano F.
Tactical supply chain planning
Nonlinear separable objective function
Multistage stochastic integer optimization
Risk management
Time-consistency
Stochastic nested decomposition
517 - Análisis
title_short On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty
title_full On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty
title_fullStr On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty
title_full_unstemmed On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty
title_sort On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty
dc.creator.none.fl_str_mv Escudero, Laureano F.
Monge Ivars, Juan Francisco
Romero Morales, Dolores
author Escudero, Laureano F.
author_facet Escudero, Laureano F.
Monge Ivars, Juan Francisco
Romero Morales, Dolores
author_role author
author2 Monge Ivars, Juan Francisco
Romero Morales, Dolores
author2_role author
author
dc.contributor.none.fl_str_mv Departamentos de la UMH::Estadística, Matemáticas e Informática
dc.subject.none.fl_str_mv Tactical supply chain planning
Nonlinear separable objective function
Multistage stochastic integer optimization
Risk management
Time-consistency
Stochastic nested decomposition
517 - Análisis
topic Tactical supply chain planning
Nonlinear separable objective function
Multistage stochastic integer optimization
Risk management
Time-consistency
Stochastic nested decomposition
517 - Análisis
description In this work a modeling framework and a solution approach have been presented for a multi-period stochastic mixed 0–1 problem arising in tactical supply chain planning (TSCP). A multistage scenario tree based scheme is used to represent the parameters’ uncertainty and develop the related Deterministic Equivalent Model. A cost risk reduction is performed by using a new time-consistent risk averse measure. Given the dimensions of this problem in real-life applications, a decomposition approach is proposed. It is based on stochastic dynamic programming (SDP). The computational experience is twofold, a compar- ison is performed between the plain use of a current state-of-the-art mixed integer optimization solver and the proposed SDP decomposition approach considering the risk neutral version of the model as the subject for the benchmarking. The add-value of the new risk averse strategy is confirmed by the compu- tational results that are obtained using SDP for both versions of the TSCP model, namely, risk neutral and risk averse.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/11000/6432
url http://hdl.handle.net/11000/6432
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1016/j.cor.2017.07.011
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
17
application/pdf
dc.source.none.fl_str_mv reponame:REDIUMH. Depósito Digital de la UMH
instname:Universidad Miguel Hernández de Elche
instname_str Universidad Miguel Hernández de Elche
reponame_str REDIUMH. Depósito Digital de la UMH
collection REDIUMH. Depósito Digital de la UMH
repository.name.fl_str_mv
repository.mail.fl_str_mv
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