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

Abstract

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