Supply chain design under uncertainty using sample average approximation and dual decomposition

Schütz, P.; Tomasgård, Asgeir; Ahmed, Shabbir

doi:10.1016/j.ejor.2008.11.040

Cited by 235 publications

(97 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…But as is known, in the realization of each scenario, the resulting profit of the SC can be different from this expected amount. Some of the researches that have been done in the field of risk management in SC network design problems ignore these deviations and only optimize the expected value of objective functions (Mirhassani Al-e-Hashem et al, 2011;Tsiakis et al, 2001;Santoso et al, 2005;Qiet al, 2009;Xanthopoulos et al, 2012;Georgiadis et al, 2011;CardonaValdés et al, 2010;Schütz et al, 2009;Goh et al, 2007). They used stochastic programming techniques for modeling their problems.…”

Section: Robust Solutionmentioning

confidence: 99%

Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs

Mohammaddust

Rezapour

Farahani

et al. 2017

International Journal of Production Economics

View full text Add to dashboard Cite

This paper investigates how organization should design their supply chains (SCs) and use risk mitigation strategies to meet different performance objectives. To do this, we develop two mixed integer nonlinear (MINL) lean and responsive models for a four-tier SC to understand these four strategies: i) holding back-up emergency stocks at the DCs, ii) holding back-up emergency stock for transshipment to all DCs at a strategic DC (for risk pooling in the SC), iii) reserving excess capacity in the facilities, and iv) using other facilities in the SC's network to back-up the primary facilities. A new method for designing the network is developed which works based on the definition of path to cover all possible disturbances. To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works 2 based on a piecewise linear transformation. The efficiency of the solution technique is tested for two prevalent distribution functions. We then explore how these models operate using empirical data from an automotive SC. This enables us to develop a more comprehensive risk mitigation framework than previous studies and show how it can be used to determine the optimal SC design and risk mitigation strategies given the uncertainties faced by practitioners and the performance objectives they wish to meet.

show abstract

Section: Robust Solutionmentioning

confidence: 99%

Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs

Mohammaddust

Rezapour

Farahani

et al. 2017

International Journal of Production Economics

View full text Add to dashboard Cite

show abstract

“…Henn and Ottomanelli (2006) analyze how uncertainty affects the drivers' route choice process in traffic assignment models and discuss the adequacy of deterministic and stochastic models in supporting traffic assignment calculations. Shutz et al (2009) examine how the use of stochastic models of demand influences strategic capacity and location decisions in a meat processing supply chain as compared to using a deterministic model.…”

Section: Related Literaturementioning

confidence: 99%

Strategic analysis of technology and capacity investments in the liquefied natural gas industry

Sönmez

Kekre

Scheller‐Wolf

et al. 2013

European Journal of Operational Research

View full text Add to dashboard Cite

“…One example is provided by Schütz et al (2009) who include short-term variations in a strategic model for the Norwegian meat industry. The model is a two-stage stochastic model where a supply chain is designed in the first stage and then operated under uncertain demand in the second stage.…”

Section: Introductionmentioning

confidence: 99%

Multi-horizon stochastic programming

et al. 2013

Self Cite

View full text Add to dashboard Cite

Infrastructure-planning models are challenging because of their combination of different time scales: while planning and building the infrastructure involves strategic decisions with time horizons of many years, one needs an operational time scale to get a proper picture of the infrastructure's performance and profitability. In addition, both the strategic and operational levels are typically subject to significant uncertainty, which has to be taken into account. This combination of uncertainties on two different time scales creates problems for the traditional multistage stochasticprogramming formulation of the problem due to the exponential growth in model size.In this paper, we present an alternative formulation of the problem that combines the two time scales, using what we call a multi-horizon approach, and illustrate it on a stylized optimization model. We show that the new approach drastically reduces the model size compared to the traditional formulation and present two real-life applications from energy planning.

show abstract

Supply chain design under uncertainty using sample average approximation and dual decomposition

Cited by 235 publications

References 14 publications

Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs

Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs

Strategic analysis of technology and capacity investments in the liquefied natural gas industry

Multi-horizon stochastic programming

Contact Info

Product

Resources

About