Adaptive active decoding and novel disjunctive graph-based improved genetic algorithm for multi-type machine robot cell scheduling in mass customization

Mass customization represents a critical evolution in modern manufacturing. To achieve efficient large-scale production of low-volume and high-variety products, designing optimized robot cells for flexible automation has become a universal challenge for manufacturers. While our prior research has effectively addressed scheduling problem in robot cells with discrete processing machines (DPMs, each processing one job at a time), the integration of both DPMs and batch processing machines (BPMs, each process multiple jobs simultaneously) introduces significant complexity for fully utilizing productive capacities. This paper investigates the Multi-Type Machine Robot Cell Scheduling Problem (MRCSP) incorporating both DPMs and BPMs and the objective is to minimize makespan. Firstly, a mixed-integer linear programming (MILP) model is formulated to describe MRCSP exactly. Recognizing the challenge of converting batch-aware two-vector encoding into feasible schedules, an adaptive active decoding strategy termed selective insertion batch decoding (SIBD) is proposed. An improved genetic algorithm (IGA) is then developed integrating this tailored encoding/decoding approach and a novel disjunctive graph. Furthermore, a batch neighborhood structure (BN) leveraging problem-specific characteristics is designed. The proposed MILP and IGA were validated on three FJSP-BPM benchmarks. Computational results demonstrate that IGA outperforms existing methods across all instances. In real-world production case studies, the approach achieved a 15.02 % average makespan reduction compared to prior methods, significantly improving resource utilization at a robot cell in southern China.