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On Pareto optimal solution for production and maintenance jobs scheduling problem in a job shop and flow shop with an immune algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

In the paper a job shop and flow shop scheduling problems with availability time constraint for maintenance are considered. Unavailability time due to maintenance is estimated basing on information about predicted Mean Time To Failure/To First Failure and Mean Time of Repair of a machine. Maintenance actions are introduced into a schedule to keep the machine available in a good operation condition. The efficiency of predictive schedules (PS) is evaluated using criteria: makespan, flow time, total tardiness, idle time. The efficiency of reactive schedules (RSs) is evaluated using criteria: solution and quality robustness. For basic schedule generation Multi Objective Immune Algorithm is applied. For predictive scheduling Minimal Impact of Disturbed Operation on the Schedule is applied. After doing computer simulations for the job shop scheduling problem following question arises: do dominated Pareto optimal basic schedules achieve better PSs? Although a single Pareto-optimal solution is achieved on Pareto-optimal frontier three different schedules have the same quality in the flow shop scheduling problem.

Original languageEnglish
Title of host publicationModern Technologies in Industrial Engineering II
EditorsAlexander Mikhaylov, Dumitru Nedelcu, Alexei Toca, Andrzej Wrobel, Constantin Carausu, Emil Oanta
PublisherTrans Tech Publications Ltd.
Pages875-880
Number of pages6
ISBN (Electronic)9783038352556
DOIs
Publication statusPublished - 2014

Publication series

NameAdvanced Materials Research
Volume1036
ISSN (Print)1022-6680
ISSN (Electronic)1662-8985

Keywords

  • Flow shop
  • Immune algorithm
  • Job shop with parallel machines
  • MTTF/MTTFF
  • MTTR
  • Maintenance
  • Pareto optimal solution
  • Predictive scheduling

ASJC Scopus subject areas

  • General Engineering

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