Can Shared Buffers and Mobile Robots Revolutionize Manufacturing Scheduling?

In the evolving manufacturing landscape, the push toward customized production has introduced new complexities in operational planning. Variability in job processing times, fluctuating workloads, and spatial constraints around machines challenge traditional scheduling and material handling approaches. Buffers — spaces used to temporarily store workpieces — play a critical role in mitigating these issues.
However, most existing models focus solely on local buffers, overlooking the potential of shared buffers, especially mobile ones, to enhance system performance. The paper “Integrated Material Handling and Machine Scheduling with Shared Buffers” by Amir Hosseini, Alena Otto, and Maximilian Schiffer addresses this gap by proposing a comprehensive optimization framework that integrates machine scheduling with material handling using shared buffer systems.

The Initial Situation

Manufacturing systems increasingly face the challenge of balancing customization with efficiency. Customization leads to greater variability in job processing times, which in turn causes machine starvation and blocking—conditions that reduce throughput and increase operational costs. Buffers help alleviate these issues by allowing temporary storage of jobs between machines. While local buffers are commonly used, they are limited by space and cost constraints. Shared buffers, which serve multiple machines and can be either stationary (common buffers) or mobile (e.g., mobile robots or MRs), offer a promising alternative. Despite their advantages, shared buffers are rarely considered in integrated scheduling models, and the coordination between machine scheduling and material handling remains underdeveloped.

The Findings

The authors present a mixed-integer programming (MIP) model that integrates machine scheduling with material handling, explicitly accounting for buffer capacities, transportation tasks, and the role of mobile robots. The model comprises three interconnected components:

1. Flow-shop scheduling: Determines job sequences and processing times across machines.
2. Generalized vehicle routing: Models MR movements, including loaded and empty trips, and their use as mobile buffers.
3. Continuous-time variables: Link the scheduling and routing components, capturing waiting times, machine blocking, and buffer availability.

An illustrative example demonstrates the model’s effectiveness. In a flow-shop with three machines and one MR, three scenarios are compared:

• No buffers: Results in a makespan of 40 units, with significant blocking on machine 2.
• Infinite buffers (unrealistic): Yields a makespan of 36, but is infeasible due to ignored buffer constraints.
• MR as mobile buffer: Achieves a 5% improvement in makespan (38 units) and significantly reduces machine blocking.

The model shows that even a single MR acting as a mobile buffer can replicate the benefits of adding a local buffer, highlighting the operational value of mobile shared buffers. Additionally, the model supports flexible decision-making regarding whether jobs should be transported directly or via a common buffer.

The Potential Implications

This integrated approach has several important implications:

• Operational Efficiency: Reduces machine idle times and improves throughput by optimizing scheduling and material handling.
• Cost Reduction: Minimizes the need for expensive local buffer space and separate logistics systems.
• Strategic Flexibility: Enables dynamic decision-making in complex manufacturing environments with varying machine configurations and workloads.
• Research Advancement: Provides a foundation for future studies on buffer management and integrated planning, with potential for scalable solution techniques like Benders decomposition.

Conclusion

The paper significantly contributes to production planning by introducing a robust and flexible model that integrates machine scheduling with material handling, emphasizing the strategic role of shared buffers. Through detailed modeling and illustrative examples, the authors demonstrate how mobile shared buffers—enabled by MRs—can enhance system performance, reduce blocking, and improve makespan. The proposed framework addresses a critical gap in existing research and offers practical insights for manufacturing systems aiming to balance customization with efficiency. Future work will focus on developing scalable solution methods and exploring the role of shared buffers across diverse production environments.