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A Dynamic Model for Facility Location in Closed-Loop Supply Chain Design

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Part of the book series: Operations Research Proceedings ((ORP))

Abstract

This paper presents a new mixed-integer linear programming (MILP) model for facility location for the simultaneous design of forward and reverse supply chains. The main contribution of this study is the formulation of a dynamic (i.e., multi-period), multi-echelon, multi-commodity capacitated facility location problem in a strategic planning context. To assess the long-term impact of the problem, our model intends to maximize the net present value (NPV) of expected cash flows for the whole supply chain. We take into consideration various features of the problem: location, capacity allocation, processing-distribution system, supplier selection and supply chain subcontracting. We examine issues related to relocation and capacity expansion under increasing deterministic, dynamic product demands and returns. Numerical results for the issues presented are given through a case study from the generated test data.

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Notes

  1. 1.

    These uni/bidirectional facilities are developed from the idea of Sahyouni et al. [3].

  2. 2.

    The model is developed from the models of Demirel and Gökçen [1], and Melo et al. [2].

References

  1. Demirel, N.Ö., Gökçen, H.: A mixed integer programming model for remanufacturing in reverse logistics environment. Int. J. Adv. Manuf. Technol. 39, 1197–1206 (2008)

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  2. Melo, M.T., Nickel, S. Saldanha da Gama, F.: Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning. Comput. Oper. Res. 33, 181–208 (2006)

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  3. Sahyouni, K., Savaskan, R.C., Daskin, M.S.: A facility location model for bidirectional flows. Transp. Sci. 41, 484–499 (2007)

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Correspondence to Orapadee Joochim .

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© 2014 Springer International Publishing Switzerland

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Joochim, O. (2014). A Dynamic Model for Facility Location in Closed-Loop Supply Chain Design. In: Helber, S., et al. Operations Research Proceedings 2012. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-00795-3_81

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