2022
DOI: 10.1007/978-3-030-96753-6_8
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A Case Study of Smart Industry in Uruguay: Grain Production Facility Optimization

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Cited by 2 publications
(3 citation statements)
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“…The problem is analyzed both theoretically and empirically, and the presented case study is one of the first reported research of applying ad hoc systematic optimization models for a real grain production company in Uruguay. The article extends our previous conference publication 'A case study of smart industry in Uruguay: grain production facility optimization' [9] presented at IV Ibero-American Congress on Smart Cities, Cancún, México, December 2021. New content and contributions in this article include: (i) an expanded review of the related literature about optimization and the application of MILP models for grain production problems; (ii) the NP-completeness of the problem is demonstrated; (iii) a novel bounds-based scheme for speeding up convergence is introduced; and (iv) the real test case is run with 16 input sets where the original variables are altered to illustrate the performance of the algorithm under different scenarios.…”
Section: Introductionsupporting
confidence: 57%
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“…The problem is analyzed both theoretically and empirically, and the presented case study is one of the first reported research of applying ad hoc systematic optimization models for a real grain production company in Uruguay. The article extends our previous conference publication 'A case study of smart industry in Uruguay: grain production facility optimization' [9] presented at IV Ibero-American Congress on Smart Cities, Cancún, México, December 2021. New content and contributions in this article include: (i) an expanded review of the related literature about optimization and the application of MILP models for grain production problems; (ii) the NP-completeness of the problem is demonstrated; (iii) a novel bounds-based scheme for speeding up convergence is introduced; and (iv) the real test case is run with 16 input sets where the original variables are altered to illustrate the performance of the algorithm under different scenarios.…”
Section: Introductionsupporting
confidence: 57%
“…These variables are used as auxiliary variables for the calculation of the number of cleanings avoided in the production lines. The constraints in Equations ( 6) and ( 7) operate together to activate the variable y s v,l if and only if some quantity of a certain product v is produced on the line l in the week s. The constraint in Equation (8) states that the necessary condition for the variable p s v,l to be activated (meaning that v is the first product to be produced on line l in week v) is that some quantity of product v has been produced in the week s. The constraint in Equation (9) assures that the first product produced in week s on line l is unique, thus guaranteeing a consistent definition for variable p s v,l . Inequalities in Equations ( 10) and ( 11) are analogous to the constraints in Equations ( 8) and ( 9), but in this case are applied to the last product produced in the week.…”
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confidence: 99%
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