Overlapping production scheduling planning with multiple objectives—An ant colony approach

Abstract

This study presents a novel simple and effective search model for solving the problem of overlapping production schedule planning with multiple objectives using ant colony optimization (ACO). The problem is solved using a network structure and recursive computing of ACO. This study also presents a novel ACO model based on three performance indices: machine idle time, job waiting time, and tardiness. This study also compares the approximate solution obtained via ACO and the optimal solution obtained using LINGO 7.0. Comparison results indicate that ACO performs well, and is effective and robust. Finally, this study uses a large data sample to test ACO, producing research results that are elastic and closely reflect the actual situation.