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Data of a novel MILP model for the production, lot-sizing and scheduling of automotive plastic components on parallel flexible injection machines

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Data of a novel MILP model for the production, lot-sizing and scheduling of automotive plastic components on parallel flexible injection machines

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Andrés Navarro, B.; Guzmán Ortiz, BE.; Poler Escoto, R. (2021). Data of a novel MILP model for the production, lot-sizing and scheduling of automotive plastic components on parallel flexible injection machines. Universitat Politècnica de València. https://doi.org/10.4995/Dataset/10251/161642

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/161642

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Title: Data of a novel MILP model for the production, lot-sizing and scheduling of automotive plastic components on parallel flexible injection machines
Author: Andrés Navarro, Beatriz Guzmán Ortiz, Brunnel Eduardo Poler Escoto, Raúl
UPV Unit: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Issued date:
Abstract:
[EN] The data set contains the input/output data that was entered into the mathematical model to develop the case study. The data is synthetically generated and developed with an algorithm encoded in Python 3.7, the ...[+]
Subjects: Lot-sizing , Scheduling , Injection moulding , Parallel machines , Mixed integer linear programming , Automotive industry
Copyrigths: Reconocimiento (by)
related items: https://doi.org/10.1155/2021/6667516
DOI: 10.4995/Dataset/10251/161642
Publisher:
Universitat Politècnica de València
Project ID:
info:eu-repo/grantAgreement/GVA//ACIF%2F2018%2F170/
info:eu-repo/grantAgreement/EC/H2020/636909/EU/Cloud Collaborative Manufacturing Networks (C2NET)/
info:eu-repo/grantAgreement/EC/H2020/825631/EU/Zero Defect Manufacturing Platform/
Thanks:
This work was supported by the Conselleria de Educación, Investigación, Cultura y Deporte - Generalitat Valenciana for hiring predoctoral research staff with Grant (ACIF/2018/170) and European Social Fund with Grant ...[+]
Type: Dataset

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