A Genetic Algorithm Methodology for Complex Scheduling Problems

Bryan A. Norman, James C. Bean

Research output: Contribution to journalArticlepeer-review

117 Scopus citations


This paper considers the scheduling problem to minimize total tardiness given multiple machines, ready times, sequence dependent setups, machine downtime and scarce tools. We develop a genetic algorithm based on random keys representation, elitist reproduction, Bernoulli crossover and immigration type mutation. Convergence of the algorithm is proved. We present computational results on data sets from the auto industry. To demonstrate robustness of the approach, problems from the literature of different structure are solved by essentially the same algorithm.

Original languageEnglish
Pages (from-to)199-211
Number of pages13
JournalNaval Research Logistics
Issue number2
StatePublished - Mar 1999


  • Genetic algorithm
  • Heuristic
  • Scheduling


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