Stochastic multi-site supply chain planning in textile and apparel industry under demand and price uncertainties with risk aversion
Houssem Felfel (),
Wafa Ben Yahia (),
Omar Ayadi () and
Faouzi Masmoudi ()
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Houssem Felfel: University of Sfax, National Engineering School of Sfax (ENIS)
Wafa Ben Yahia: University of Sfax, National Engineering School of Sfax (ENIS)
Omar Ayadi: University of Sfax, National Engineering School of Sfax (ENIS)
Faouzi Masmoudi: University of Sfax, National Engineering School of Sfax (ENIS)
Annals of Operations Research, 2018, vol. 271, issue 2, No 11, 574 pages
Abstract:
Abstract A multi-product, multi-period, multi-site supply chain production and transportation planning problem, in the textile and apparel industry, under demand and price uncertainties is considered in this paper. The problem is formulated using a two-stage stochastic programming model taking into account the production amount, the inventory and backorder levels as well as the amounts of products to be transported between the different plants and customers in each period. Risk management is addressed by incorporating a risk measure into the stochastic programming model as a second objective function, which leads to a multi-objective optimization model. The objectives aim to simultaneously maximize the expected net profit and minimize the financial risk measured. Two risk measures are compared: the conditional-value-at-risk and the downside risk. As the considered objective functions conflict with each other’s, the problem solution is a front of Pareto optimal robust alternatives, which represents the trade-off among the different objective functions. A case study using real data from textile and apparel industry in Tunisia is presented to illustrate the effectiveness of the proposed model and the robustness of the obtained solutions.
Keywords: Multi-site supply chain; Textile and apparel industry; Multi-objective optimization; Stochastic programming; Risk management (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)
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DOI: 10.1007/s10479-018-2980-2
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