Heuristics for workforce planning with worker differences

John Fowler, Pornsarun Wirojanagud, Esma Gel

Research output: Contribution to journalArticlepeer-review

84 Scopus citations


This study considers decisions in workforce management assuming individual workers are inherently different as measured by general cognitive ability (GCA). A mixed integer programming (MIP) model that determines different staffing decisions (i.e., hire, cross-train, and fire) in order to minimize workforce related costs over multiple periods is described. Solving the MIP for a large problem instance size is computationally burdensome. In this paper, two linear programming (LP) based heuristics and a solution space partition approach are presented to reduce the computational time. A genetic algorithm was also implemented as an alternative method to obtain better solutions and for comparison to the heuristics proposed. The heuristics were applied to realistic manufacturing systems with a large number of machine groups. Experimental results shows that performance of the LP based heuristics performance are surprisingly good and indicate that the heuristics can solve large problem instances effectively with reasonable computational effort.

Original languageEnglish (US)
Pages (from-to)724-740
Number of pages17
JournalEuropean Journal of Operational Research
Issue number3
StatePublished - Nov 1 2008


  • Cross-training
  • Genetic algorithms
  • Heuristics
  • Workforce planning

ASJC Scopus subject areas

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


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