A Mixed-Integer Linear Model for Solving the Open Shop Scheduling Problem

Daniel Morillo-Torres, Gustavo Gatica

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva

Resumen

This paper addresses the Open Shop Scheduling Problem with non-identical parallel machines. In this context, a finite set of jobs must be processed by a finite set of machines in any order. However, the machines can only process a single job at a time. The objective is to minimize the maximum completion time of the jobs, known as Cmax or makespan. In this paper, a mixed-integer linear programming model is presented for this problem; it uses time-based decision variables and disjunctive constraints. The model allows each job to have a different number of operations. Computational results are tested with the Gurobi solver and the three best-known benchmark libraries from the literature. These results show that the mathematical model proposed efficiently solves open shop scheduling problems with 6 machines and 6 jobs and its optimal value has a maximal deviation of 6.18 %.

Idioma originalInglés
Título de la publicación alojadaService Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future - Proceedings of SOHOMA LATIN AMERICA 2021
EditoresDamien Trentesaux, Theodor Borangiu, Paulo Leitão, Jose-Fernando Jimenez, Jairo R. Montoya-Torres
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas301-310
Número de páginas10
ISBN (versión impresa)9783030809058
DOI
EstadoPublicada - 2021
Evento1st Latin-American Workshop on Service-Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, SOHOMA LATIN AMERICA 2021 - Bogota, Colombia
Duración: 27 ene 202128 ene 2021

Serie de la publicación

NombreStudies in Computational Intelligence
Volumen987
ISSN (versión impresa)1860-949X
ISSN (versión digital)1860-9503

Conferencia

Conferencia1st Latin-American Workshop on Service-Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, SOHOMA LATIN AMERICA 2021
País/TerritorioColombia
CiudadBogota
Período27/01/2128/01/21

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial

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