Staff Scheduling by a Genetic Algorithm

Document Type : Research Paper


1 Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran

2 Ragheb-Isfahani University, Isfahan, Iran


This paper describes a Genetic Algorithms approach to a
manpower-scheduling problem arising at a Petrochemical Company. Although
Genetic Algorithms have been successfully used for similar problems
in the past, they always had to overcome the limitations of the
classical Genetic Algorithms paradigm in handling the conflict between
objectives and constraints. The approach taken here is to use an indirect
coding based on permutations of the personnel’s, and a heuristic
decoder that builds schedules from these permutations. Computational
experiments based on 52 weeks of live data are used to evaluate three different
decoders with varying levels of intelligence, and four well-known
crossover operators. The results reveal that the proposed algorithm is
able to find high quality solutions and is both faster and more flexible
than a recently published Taboo Search approach


Volume 1, Issue 4 - Serial Number 4
October 2015
Pages 73-86
  • Receive Date: 01 September 2014
  • Revise Date: 01 October 2014
  • Accept Date: 01 November 2014
  • First Publish Date: 01 October 2015