A Genetic Algorithm with Modified Crossover Operator for a Two-Agent Scheduling Problem



Department of Industrial Engineering, University of Tehran, Tehran, Iran


The problem of scheduling with multi agent has been studied
for more than one decade and significant advances have been made
over the years. However, most work has paid more attention to the condition
that machines are available during planning horizon. Motivated
by the observations, this paper studies a two-agent scheduling model
with multiple availability constraint. Each agent aims at minimizing a
function which depends only on the completion times of its jobs. The
problem is to find a schedule that minimizes the objective function of
one agent, subject to the objective function of the other agent does not
exceed a given threshold Q. some new dominance properties for this
problem percent and next, using these properties, we develop a genetic
algorithm with modified crossover for the problem. Computational results
are also presented to determine the performance of the proposed
genetic algorithms.