Incorporating uncertainty into a supplier selection problem

Supplier selection is an important strategic supply chain design decision. Incorporating uncertainty of demand and supplier capacity into the optimization model results in a robust selection of suppliers. A two-stage stochastic programming (SP) model and a chance-constrained programming (CCP) model...

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Bibliographic Details
Published in:International journal of production economics Vol. 134; no. 2; pp. 344 - 356
Main Authors: Li, Lei, Zabinsky, Zelda B.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.12.2011
Elsevier
Elsevier Sequoia S.A
Series:International Journal of Production Economics
Subjects:
Chance-constraint programming
Cost analysis
Decision making models
Demand
Hierarchies
Integer programming
Lieferantenbewertung
Lieferkette
Marketing
Mathematical models
Mathematische Optimierung
Meetings
Multi-parametric programming
Pareto optimum
Plan for uncertainty
Programming
Risikomanagement
Risk
Robust supplier selection
Robust supplier selection Plan for uncertainty Stochastic programming Chance-constraint programming Multi-parametric programming Tradeoffs between risk and cost
Stochastic programming
Studies
Suppliers
Supply chain management
Tradeoffs between risk and cost
Uncertainty
Robust supplier selection
Multi-parametric programming
Plan for uncertainty
Tradeoffs between risk and cost
Chance-constraint programming
Stochastic programming
ISSN:0925-5273, 1873-7579
Online Access:Get full text
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Summary:Supplier selection is an important strategic supply chain design decision. Incorporating uncertainty of demand and supplier capacity into the optimization model results in a robust selection of suppliers. A two-stage stochastic programming (SP) model and a chance-constrained programming (CCP) model are developed to determine a minimal set of suppliers and optimal order quantities with consideration of business volume discounts. Both models include several objectives and strive to balance a small number of suppliers with the risk of not being able to meet demand. The SP model is scenario-based and uses penalty coefficients whereas the CCP model assumes a probability distribution and constrains the probability of not meeting demand. Both formulations improve on a deterministic mixed integer linear program and give the decision maker a more complete picture of tradeoffs between cost, system reliability and other factors. We present Pareto-optimal solutions for a sample problem to demonstrate the benefits of the SP and CCP models. In order to describe the tradeoffs between costs and risks in an analytical form, we use multi-parametric programming techniques to more completely analyze the alternative Pareto-optimal supplier selection solutions in the CCP model. This analysis gives insights into the robustness of the solutions with respect to number of suppliers, costs and probability of not meeting demand.
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ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2009.11.007