Abstract

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
Volume227
Issue number2
DOIs
StatePublished - Jun 1 2013

Fingerprint

Multiple Objectives
Parallel Machines
Nondominated Solutions
Scheduling
Genetic Algorithm
Heuristics
Genetic algorithms
Total Weighted Completion Time
Minimise
Parallel Machine Scheduling
Tardiness
Computational Results
Scheduling Problem
Multiple objectives
Genetic algorithm
Parallel machines

Keywords

  • Genetic algorithm
  • Multiple-objective heuristics
  • Scheduling

ASJC Scopus subject areas

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

Cite this

Multiple-objective heuristics for scheduling unrelated parallel machines. / Lin, Yang Kuei; Fowler, John; Pfund, Michele E.

In: European Journal of Operational Research, Vol. 227, No. 2, 01.06.2013, p. 239-253.

Research output: Contribution to journalArticle

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AB - 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.

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