Research on Production Scheduling and Assembly Integration Optimization in Window and Door Manufacturing Enterprises Based on Improved NSGA-II Algorithm

This study delves into the workshop scheduling problem for window and door manufacturing enterprises, focusing on the two-stage flexible job shop scheduling considering both processing and assembly phases. Initially, the study provides a detailed description of the problem, considering dynamic arrival of jobs, assembly of window and door products from multiple jobs, and the requirement for jobs to reach the assembly station simultaneously. A mathematical model is established with the objectives of minimizing makespan and simultaneousness degree. Subsequently, an improved ENT-NSGA-II algorithm is proposed, employing STPS and RSGS strategies to generate the initial population. The genetic parameters are adaptively adjusted with the iteration count, and three neighborhood search mechanisms are applied to individuals that remain unimproved after crossover and mutation. Testing on 15 instances of varying scales demonstrates that, compared to traditional genetic algorithms and artificial bee colony algorithms, the proposed ENT-NSGA-II algorithm achieves superior solutions in a shorter timeframe, showcasing remarkable performance advantages.