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dc.contributor.author
Rossit, Daniel Alejandro  
dc.contributor.author
Toncovich, Adrián Andrés  
dc.contributor.author
Rossit, Diego Gabriel  
dc.contributor.author
Nesmachnow, Sergio  
dc.contributor.other
Hussain, Chaudhery Mustansar  
dc.contributor.other
Di Sia, Paolo  
dc.date.available
2022-03-04T12:51:43Z  
dc.date.issued
2021  
dc.identifier.citation
Rossit, Daniel Alejandro; Toncovich, Adrián Andrés; Rossit, Diego Gabriel; Nesmachnow, Sergio; Flow Shop Scheduling Problems in Industry 4.0 Production Environments: Missing Operation Case; Springer; 2021; 1-23  
dc.identifier.isbn
978-3-030-58675-1  
dc.identifier.uri
http://hdl.handle.net/11336/152904  
dc.description.abstract
The Fourth Industrial Revolution or Industry 4.0 is forcing a completely reorganization of the manufacturing systems in order to implement increasingly automatized processes and customized products. Within this context, advanced computer-aid tools can contribute to give support to decision-makers in this increasingly complex conditions. As a contribution to this process, this chapter addresses an optimization problem that has become progressively common within the Industry 4.0 context: the missing operations flow shop scheduling problem. Conversely, to the traditional flow shop, this problem considers the customization of the final products based on the requirements of the clients. Thus, several operations of the manufacturing cell can be skipped. Moreover, the missing operations can vary from one client to another, increasing the difficulty of the decision-making process. In this chapter we revise the missing operations flow shop scheduling problem under two of the main paradigms of the scheduling literature: considering only permutative schedules, i.e., the same job sequence is used for all the machines involved, and the more computationally expensive case of allowing the optimization problem to consider non-permutative schedules, i.e., different job schedules can be used for different machines in the production line. For these two cases, mathematical formulations that aim at minimizing total tardiness are presented. Furthermore, a two-echelon resolution approach is discussed. This involves firstly a Genetic Algorithm (GA), which only considers permutative schedules, and secondly, a Simulated Annealing algorithm, which taking as an input the solution of the GA it expands the search space by considering non-permutative schedules. Computer experimentation was performed on a set of instances with different proportions of missing operations in order to represent a large variety of the situations that occur in practice at real-world manufacturing cells.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Springer  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
INDUSTRY 4.0  
dc.subject
FLOW SHOP  
dc.subject
MISSING OPERATION  
dc.subject
GENETIC ALGORITHM  
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SIMULATED ANNEALING  
dc.subject.classification
Otras Ingenierías y Tecnologías  
dc.subject.classification
Otras Ingenierías y Tecnologías  
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS  
dc.title
Flow Shop Scheduling Problems in Industry 4.0 Production Environments: Missing Operation Case  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.type
info:eu-repo/semantics/bookPart  
dc.type
info:ar-repo/semantics/parte de libro  
dc.date.updated
2021-12-13T20:41:00Z  
dc.journal.pagination
1-23  
dc.journal.pais
Suiza  
dc.journal.ciudad
Cham  
dc.description.fil
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina  
dc.description.fil
Fil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina  
dc.description.fil
Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina  
dc.description.fil
Fil: Nesmachnow, Sergio. Universidad de la República; Uruguay  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/referenceworkentry/10.1007/978-3-030-58675-1_71-1  
dc.conicet.paginas
2810  
dc.source.titulo
Handbook of Smart Materials, Technologies, and Devices