Spinning joint scheduling strategy and its optimization method based on data and empirical knowledge

Abstract

Spinning end breakage is a major factor limiting the efficiency of the spinning process, and this paper proposes a digital method of spinning joint management. Based on the broken ends data collected by a single spindle monitoring system and guided by the empirical knowledge obtained from a factory investigation, a genetic algorithm-based spinning joint scheduling model is built with the minimum spinning machine idle time as the optimization objective. Three different heuristic rules are introduced in generating the initial population, and their relationship with the distribution of broken ends is discussed; to curb the potential efficiency loss, the broken ends are classified by the data obtained from the single spindle monitoring, and the priority joint task is introduced in the model. The experimental results show that, compared with the traditional S-tour, the model with heuristic rule 2 can reduce the machine idle time by 43% on average, and the priority-based model can reduce it by 42% on average. They both have comparable optimization capabilities, but the priority-based model avoids more serious production loss and is the superior choice.