A dyeing workshop hierarchical scheduling strategy based on genetic algorithm and multi-agent system

Abstract

Aimed to the problem of optimal scheduling in the dyeing and printing industry, a hierarchical scheduling strategy of dip-dye systems based on genetic algorithm and multi-agent system was proposed. In this method, the algorithm of hierarchical scheduling integrated static and dynamic strategy was used. The static strategy supports GA under the constraints of multi-product batch processing, non-equivalence of dye vats, pre-orders, delivery orders, as well as the switching cost. The dynamic strategy is based on the static strategy and supports multi-agent coordination of dynamic optimization algorithm at running status. Through the algorithm with multi-constraint conditions of production and many factors of dynamic changes, the dynamic optimal design with dye vat tasks can be obtained. The simulation results compared to SGA scheduling show that the strategy based on data-driven hierarchical scheduling can achieve the objective of energy-saving & pollution emission reduction. The results in the enterprise also show that the method is effective and feasible.