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An Optimization Model for Planning Operations in a Meat Packing Plant

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 577))

Abstract

This paper presents an optimization model for supporting planning operations in a meat packing plant. The production system considers processing raw material (carcasses) by applying cutting patterns in order to meet a given demand of different products. Major decisions in the planning problem include the number of times each cutting pattern is applied on the available carcasses, the total yield per product and its corresponding levels of inventory at each time period; as a result a Mixed Integer Linear Programming Model (MILP) is proposed. The main contribution is to give an optimal decision that maximizes the economic profit and thereby reducing the shortages for products with the highest profits and the inventory for those with low level demand, taking into account the perishability of the products, labour capacities, and the fact that it may exists different types of carcasses with different yields.

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Acknowledgements

This research was partially supported by CONICYT, Departamento de Relaciones Internacionales “Programa de Cooperacion Cientifica International” (Grant PPCI 12041), DGIP of Universidad Tecnica Federico Santa Maria (Grant USM 28.15.20), and by Consejo Nacional de Ciencia y Tecnologa, CONACYT, Grant (No. 188902).

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Correspondence to Sara V. Rodriguez .

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© 2015 Springer International Publishing Switzerland

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Albornoz, V.M., Gonzalez-Araya, M., Gripe, M.C., Rodriguez, S.V., Juventino Treviño, E. (2015). An Optimization Model for Planning Operations in a Meat Packing Plant. In: de Werra, D., Parlier, G., Vitoriano, B. (eds) Operations Research and Enterprise Systems. ICORES 2015. Communications in Computer and Information Science, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-27680-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-27680-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27679-3

  • Online ISBN: 978-3-319-27680-9

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