Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 201-214 |
| Seitenumfang | 14 |
| Fachzeitschrift | OMEGA-INT J MANAGE S |
| Jahrgang | 59 |
| Publikationsstatus | Veröffentlicht - 1 März 2016 |
Abstract
We present a stochastic version of a three-layer supply network planning problem that includes the selection of vendors that must be equipped with company-specific tools. The configuration of a supply network must be determined by using demand forecasts for a long planning horizon to meet a given service level. The risk induced by the uncertain demand is explicitly considered by incorporating the conditional value at risk. The objective is to maximize the weighted sum of the expected net present value of discounted cash flows and the conditional value at risk. This would lead to a non-linear model formulation that is approximated by a mixed-integer linear model. This approximation is realized by a piecewise linearization of the expected backlogs and physical inventory as non-linear functions of cumulative production quantities. A two-stage stochastic programming approach is proposed. Our numerical analysis of generic test instances indicates that solving the linearized model formulation yields a robust and stable supply network configuration when demand is uncertain.
ASJC Scopus Sachgebiete
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Strategie und Management
- Entscheidungswissenschaften (insg.)
- Managementlehre und Operations Resarch
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
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in: OMEGA-INT J MANAGE S, Jahrgang 59, 01.03.2016, S. 201-214.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Strategic supply network planning with vendor selection under consideration of risk and demand uncertainty
AU - Sahling, Florian
AU - Kayser, Ariane
N1 - Publisher Copyright: © 2015 Elsevier Ltd. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2016/3/1
Y1 - 2016/3/1
N2 - We present a stochastic version of a three-layer supply network planning problem that includes the selection of vendors that must be equipped with company-specific tools. The configuration of a supply network must be determined by using demand forecasts for a long planning horizon to meet a given service level. The risk induced by the uncertain demand is explicitly considered by incorporating the conditional value at risk. The objective is to maximize the weighted sum of the expected net present value of discounted cash flows and the conditional value at risk. This would lead to a non-linear model formulation that is approximated by a mixed-integer linear model. This approximation is realized by a piecewise linearization of the expected backlogs and physical inventory as non-linear functions of cumulative production quantities. A two-stage stochastic programming approach is proposed. Our numerical analysis of generic test instances indicates that solving the linearized model formulation yields a robust and stable supply network configuration when demand is uncertain.
AB - We present a stochastic version of a three-layer supply network planning problem that includes the selection of vendors that must be equipped with company-specific tools. The configuration of a supply network must be determined by using demand forecasts for a long planning horizon to meet a given service level. The risk induced by the uncertain demand is explicitly considered by incorporating the conditional value at risk. The objective is to maximize the weighted sum of the expected net present value of discounted cash flows and the conditional value at risk. This would lead to a non-linear model formulation that is approximated by a mixed-integer linear model. This approximation is realized by a piecewise linearization of the expected backlogs and physical inventory as non-linear functions of cumulative production quantities. A two-stage stochastic programming approach is proposed. Our numerical analysis of generic test instances indicates that solving the linearized model formulation yields a robust and stable supply network configuration when demand is uncertain.
KW - Conditional value at risk
KW - Linearization
KW - Robust optimization
KW - Stochastic demand
KW - Supply network planning
UR - http://www.scopus.com/inward/record.url?scp=84949316452&partnerID=8YFLogxK
U2 - 10.1016/j.omega.2015.06.008
DO - 10.1016/j.omega.2015.06.008
M3 - Article
VL - 59
SP - 201
EP - 214
JO - OMEGA-INT J MANAGE S
JF - OMEGA-INT J MANAGE S
SN - 0305-0483
ER -