35 Scopus citations


This research proposes two heuristics and a Genetic Algorithm (GA) to find non-dominated solutions to multiple-objective unrelated parallel machine scheduling problems. Three criteria are of interest, namely: makespan, total weighted completion time, and total weighted tardiness. Each heuristic seeks to simultaneously minimize a pair of these criteria; the GA seeks to simultaneously minimize all three. The computational results show that the proposed heuristics are computationally efficient and provide solutions of reasonable quality. The proposed GA outperforms other algorithms in terms of the number of non-dominated solutions and the quality of its solutions.

Original languageEnglish (US)
Pages (from-to)239-253
Number of pages15
JournalEuropean Journal of Operational Research
Issue number2
StatePublished - Jun 1 2013


  • Genetic algorithm
  • Multiple-objective heuristics
  • Scheduling

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


Dive into the research topics of 'Multiple-objective heuristics for scheduling unrelated parallel machines'. Together they form a unique fingerprint.

Cite this