Supply Chain Design Based on Mean-Cvar Two-Stage Stochastic Programming with the Possibility of Disruption in Distribution Centers

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Gholamian Elham; Davoodi Sayyed Mohammad Reza; Sharifi Qazvini Mohammad Reza

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Journal: JOURNAL OF TRANSPORTATION RESEARCH Year:2024 | Volume: | Issue: Page(s): 277-292

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Information Journal Paper

Title

Supply Chain Design Based on Mean-Cvar Two-Stage Stochastic Programming with the Possibility of Disruption in Distribution Centers

Author(s)

Gholamian Elham | Davoodi Sayyed Mohammad Reza | Sharifi Qazvini Mohammad Reza | Issue Writer Certificate

Keywords

Supply chainQ4

Two stage stochastic programmingQ4

CVaRQ4

DisruptionQ4

Abstract

In recent years, a strong move has been made towards integrating strategic and tactical decisions through the development of location-allocation models. Strategic decisions such as location allocation have long-term effects and are not easily changed, and tactical-level decisions involve medium-term planning over a one-year period such as inventory management policies. Integrating different levels of decision making in the Supply chain helps reduce overall costs and improve performance. In the present study, a stochastic two-stage mean-conditional value at risk model is used to allocate locations and calculate the flow of materials and goods constructed of a multi-product-multi-level Supply chain. In the research model, distribution centers can be selected in two types: reliable (without the possibility of Disruption) and unreliable (with the possibility of Disruption). Sources of uncertainty in the model include shipping costs, end customer demand, and the possibility of Disruption in distribution centers. The research model uses the conditional value at risk along with the risk aversion factor to control the risk of long distances. The designed model is eventually transformed into a single-level linear programming with the help of Monte Carlo simulation. Finally, with a numerical example, the model is implemented and its sensitivity is analyzed.

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