Research on Just-in-Time Scheduling for Assembly Workshops Based on Multi-Rule Collaborative Initialization

Traditional job shop scheduling research primarily focuses on regular performance measures such as makespan. However, in a Just-in-Time (JIT) production environment, the objective shifts toward minimizing non-regular measures, specifically the weighted sum of earliness and tardiness (E/T) penalties, as excessive earliness leads to increased work-in-process inventory costs. Addressing the JIT scheduling problem in Assembly Job-shop Scheduling Problem (AJSP) is challenging, as traditional genetic algorithms (GAs) often suffer from premature convergence due to the randomness of initial populations. This paper proposes an Improved Genetic Algorithm (IGA) based on a multi-rule collaborative initialization mechanism. The algorithm explicitly incorporates assembly tree structure constraints during the encoding phase. For population initialization, a hybrid strategy is designed by integrating forward scheduling, backward scheduling, and forward-scheduling-based delay adjustment rules to ensure both the quality and diversity of the initial solutions. Simulation experiments and ablation studies demonstrate that the proposed IGA consistently achieves lower total weighted costs across various problem scales compared to standard algorithms. The results validate that the collaborative initialization strategy effectively balances global exploration and local exploitation, providing a robust solution for AJSP under JIT constraints.