Comparison of two Meta-Heuristics for the Bi-Objective Flexible Job Shop Scheduling Problem with Sequence Dependent Setup Times

João Sacramento, João Pedrosa, Nelson Chibeles-Martins, Tânia Pinto-Varela

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

The increasingly competitivity in the plastic container market is driving companies toward a greater focus on efficiency, and mass production customisation, which triggers the increase of productivity by implementing more efficient and faster IT solutions. This work is based on a Portuguese case study, to develop a scheduling model considering the specific characteristics of this type of facilities and increase its competitiveness. To this end, two different approaches, the Tabu Search and Genetic Algorithm, were developed to solve a flexible job shop scheduling problem under a make-to-order production strategy. Each approach was validated using the case study, and the model's applicability were testes trough five instances. The results have shown that Tabu Search has a better efficacy and the Genetic Algorithm shows better efficiency.

Original languageEnglish
Title of host publication30th European Symposium on Computer Aided Process Engineering, Proceedings
EditorsSauro Pierucci, Flavio Manenti, Giulia Luisa Bozzano, Davide Manca
PublisherElsevier B.V.
Pages181-186
Number of pages6
DOIs
Publication statusPublished - Jan 2020
Event30tth European Symposium on Computer Aided Process Engineering. ESCAPE 30 - Virtual, Online, Milano, Italy
Duration: 31 Aug 20202 Sep 2020

Publication series

NameComputer Aided Chemical Engineering
PublisherElsevier B.V.
Volume48
ISSN (Print)1570-7946

Conference

Conference30tth European Symposium on Computer Aided Process Engineering. ESCAPE 30
CountryItaly
CityMilano
Period31/08/202/09/20

Keywords

  • Flexible Job Shop Problem
  • Genetic Algorithm
  • Metaheuristics
  • Multi-objective
  • Tabu Search

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