Premium
A Genetic Algorithm Approach to Solving Stochastic Job‐shop Scheduling Problems
Author(s) -
Yoshitomi Yasunari
Publication year - 2002
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/1475-3995.00368
Subject(s) - roulette , mathematical optimization , fitness proportionate selection , computer science , genetic algorithm , stochastic programming , job shop scheduling , scheduling (production processes) , fitness function , stochastic optimization , mathematics , schedule , geometry , operating system
This paper proposes a method for solving stochastic job‐shop scheduling problems based on a genetic algorithm. The genetic algorithm was expanded for stochastic programming. In this expansion, the fitness function is regarded as representing fluctuations that may occur under stochastic circumstances specified by the distribution functions of stochastic variables. In this study, the Roulette strategy is adopted for selecting the optimum solution in terms of the expected value. Within this algorithm, it is expected that the individual that appears most frequently must give the optimum solution. The effectiveness of this approach is confimed by applying it to stochastic job‐shop scheduling problems. I compare the approximately optimum solutions found by this approach with the truly or approximately optimum solutions obtained by other conventional methods, and discuss the performance and effectiveness of this approach.