A Constraint-Fusion Strategy Based Dual-Population Evolutionary Algorithm for Multi-Stage Production and Line Scheduling

With the expansion of production scale and the diversification of market demand in intelligent manufacturing industry, how to efficiently schedule production tasks has become a key issue for enterprises to improve their competitiveness. This paper studies the Multi-stage production and production line scheduling optimization problem (MSPLP), which involves the production scheduling of multiple product categories on multiple production lines and multiple stages. The purpose is to complete the task of reducing cost and increasing efficiency under the premise of meeting production demand. Therefore, based on the production data of an actual production enterprise, this paper extracts and constructs a data set, and solves the corresponding MSPLP problem by designing a multi-objective optimization algorithm based on the characteristics of the MSPLP problem. Aiming at the complex constraints of the problem, the algorithm proposed a repair optimization strategy based on constraint fusion and a phased crossover strategy based on a relationship matrix to effectively deal with the characteristics of the problem and obtain better optimization results. The experimental results on multiple datasets with 400-1200 decision variables show that the proposed algorithm is more effective than the existing multi-objective evolutionary algorithms.