Multi-Objective Comprehensive Approach for Flexible Assembly Job Shop Scheduling Problem with AGVs

This paper addresses the comprehensive scheduling problem in a flexible assembly job shop with AGV handling (FAJSP-AGVs), where machining processes and assembly activities between different processes are conducted. A mathematical model is established with objectives to minimize makespan, total machine energy consumption, and AGV working time. An improved adaptive NSGA-II algorithm (IA-NSGA-II) is proposed, incorporating a process constraint matrix-based encoding method, adaptive crossover and mutation operators, and a variable neighborhood search (VNS) to avoid local optima. Simulation experiments validate the effectiveness of the proposed algorithm, showing superior performance compared to existing methods such as INSGA-II and IGA. An ablation study further demonstrates the contributions of the adaptive probability mechanism and VNS, highlighting their roles in enhancing solution quality and avoiding local optima. The proposed method is also tested in dynamic rescheduling scenarios, demonstrating its stability and adaptability in handling machine breakdowns. Experimental results indicate that IA-NSGA-II achieves better scheduling outcomes with shorter makespan, lower energy consumption, and reduced AGV working time. (c) 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.