A multi-objective stochastic programming approach for supply chain design considering risk

In this paper, we develop a multi-objective stochastic programming approach for supply chain design under uncertainty. Demands, supplies, processing, transportation, shortage and capacity expansion costs are all considered as the uncertain parameters. To develop a robust model, two additional object...

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Veröffentlicht in:International journal of production economics Jg. 116; H. 1; S. 129 - 138
Hauptverfasser: Azaron, A., Brown, K.N., Tarim, S.A., Modarres, M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.11.2008
Elsevier
Elsevier Sequoia S.A
Schriftenreihe:International Journal of Production Economics
Schlagworte:
Costs
Decision making models
Multiple-objective programming
Pareto optimum
Risk
Risk management
Stochastic models
Stochastic programming
Studies
Supply chain management
Supply chain management Stochastic programming Multiple-objective programming Risk management
Supply chains
Uncertainty
Supply chain management
Multiple-objective programming
Risk management
Stochastic programming
ISSN:0925-5273, 1873-7579
Online-Zugang:Volltext
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Zusammenfassung:In this paper, we develop a multi-objective stochastic programming approach for supply chain design under uncertainty. Demands, supplies, processing, transportation, shortage and capacity expansion costs are all considered as the uncertain parameters. To develop a robust model, two additional objective functions are added into the traditional comprehensive supply chain design problem. So, our multi-objective model includes (i) the minimization of the sum of current investment costs and the expected future processing, transportation, shortage and capacity expansion costs, (ii) the minimization of the variance of the total cost and (iii) the minimization of the financial risk or the probability of not meeting a certain budget. The ideas of unreliable suppliers and capacity expansion, after the realization of uncertain parameters, are also incorporated into the model. Finally, we use the goal attainment technique to obtain the Pareto-optimal solutions that can be used for decision-making.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2008.08.002