Abstracto

Research on multi-objective job shop scheduling based on ant colony algorithm

Yicheng Xu, Wenan Tan

As the most pivotal part of Enterprise Resource Planning, effective scheduling algorithms can benefit enterprise to the maximal extent. In recent years, some intelligent algorithms have been used for this point. In this paper, ant colony algorithm has become the research focus because of its great ability of finding new solutions, robustness and essential parallelism. This paper introduces the classification, characteristics and model of Job- Shop problem, then summarizes the various methods used in such problem. This paper also describes the principle, characteristics, operation processes and key modules of ant colony algorithm in detail. We integrate actual manufacture, use adaptive ant colony algorithm to solve actual schedule problem, developed production scheduling system, combined theory and fact. New state transition rule and parameter adaptive rule was developed for the ant colony algorithm. Such rules improved the performance of ant colony algorithm.