Optimal design of supply chain networks under uncertain transient demand variations

Georgiadis, Michael C.; Tsiakis, Panagiotis; Longinidis, Pantelis; Sofioglou, Maria K.

doi:10.1016/j.omega.2010.07.002

Cited by 174 publications

(85 citation statements)

References 38 publications

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“…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

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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.

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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

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“…These researches typically considered demand as the uncertainty parameter of the supply chain (El-Sayed et al 2010;Rodriguez et al 2014;Georgiadis et al 2011;Cardona-Valdés and Alvarez 2011). Mirzapour Al-ehashem et al (2011) proposed a multi-site, multi-period, multi-product aggregate production planning problem in a supply chain consisting of multiple suppliers, multiple plants and multiple customers under cost and demand uncertainty.…”

Section: Supply Chain Network Design Under Uncertaintymentioning

confidence: 99%

Optimizing decentralized production–distribution planning problem in a multi-period supply chain network under uncertainty

et al. 2017

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Decentralized supply chain management is found to be significantly relevant in today's competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final product is appropriated with stochastic and fuzzy numbers. We provide mathematical formulation of the problem as a bi-level mixed integer linear programming model. Due to problem's convolution, a structure to solve is developed that incorporates a novel heuristic algorithm based on Kth-best algorithm, fuzzy approach and chance constraint approach. Ultimately, a numerical example is constructed and worked through to demonstrate applicability of the optimization model. A sensitivity analysis is also made.

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“…Melo et al (2006) dealt with multi-period facility reconfiguration decisions in a multi-commodity, dynamic supply chain context, involving reallocation of facilities' capacities assuming that all existing facilities are operating at the beginning of the planning horizon; if an existing facility is closed, it cannot be reopened, while when a new facility is opened, it will remain in operation. Georgiadis et al (2011) and Wilhelm et al (2013) formulated similar problems by allowing the dynamic reconfiguration of the network over time (i.e., by opening or closing facilities, expanding, downsizing or contracting their capacities) to accommodate changing trends in demand or costs.…”

Section: -Literature Backgroundmentioning

confidence: 99%

Capacity management in public service facility networks: a model, computational tests and a case study

2015

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Optimal design of supply chain networks under uncertain transient demand variations

Cited by 174 publications

References 38 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

Optimizing decentralized production–distribution planning problem in a multi-period supply chain network under uncertainty

Capacity management in public service facility networks: a model, computational tests and a case study

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