Warehouse Optimization:
Turning Complex Optimization Challenges into Practical Solutions

How do you solve a sequencing problem that breaks standard solvers? You rethink the structure.
We achieved a breakthrough in warehouse automation optimization by simplifying cascading constraints into independent, solvable components. Our hybrid strategy combines the precision of exact methods with the speed of heuristics to deliver actionable value at scale.

In one of our research and development projects, we worked with a company to solve a critical warehouse logistics automation optimization challenge: sequencing a very large number of vendor packs while respecting strict physical constraints.

Traditional optimization engines, including constraint programming solvers, were unable to find feasible solutions within reasonable time limits. The problem was not only large, it was structurally complex. This created a significant barrier to achieving warehouse automation optimization at scale.

Overcoming Structural Complexity in Warehouse Automation Optimization

Rather than focusing immediately on solving, our first step was to understand the problem in depth. We analyzed how the operational constraints interacted with the data and identified that complexity was driven less by volume and more by how certain elements behaved together. In particular, some operational patterns created cascading effects that made the problem extremely difficult for standard solvers to handle.

Recognizing these limitations, we shifted our approach by first clarifying the objective of the problem. The goal is to minimize the usage of tote wall, i.e. the work-in-progress space where totes are loaded. In practical terms, this means minimizing the number of ties that have started to be filled but are not yet full, as these partially filled totes consume valuable space and create operational constraints.

With this objective clearly defined, we focused on redefining the problem itself. By structuring the problem into clearer components, we reduced its effective complexity and created a foundation for scalable solutions. This approach allowed us to combine exact optimization methods with pragmatic heuristics, ensuring both robustness and computational efficiency.

This decomposition, illustrated in the diagrams, reduced the effective complexity of the problem and enabled a hybrid approach combining exact optimization techniques with pragmatic heuristics to achieve scalable and computationally efficient solutions.

How Warehouse Automation Optimization Drives Performance

Beyond the technical breakthrough, our approach translated into meaningful business value that standard solvers simply could not unlock. By redefining the problem structure, we moved from theoretical models to high-impact operational results:

• Warehouse & Inventory Infrastructure Efficiency: A 52% reduction in tote wall footprint translates directly to lower CAPEX, allowing for smaller facility builds or increased high-density storage.
• Green Operations: Optimized sequencing minimizes robot travel distance, slashing energy consumption and reducing operational wear-and-tear.
• Peak-Season Readiness: By eliminating combinatorial bottlenecks, the system remains robust and responsive even under maximum seasonal load.

When standard methods reach their limits, our role is to understand why, and to build strategies that bridge the gap between theoretical optimization and real-world feasibility. We analyze, rethink, and redesign problems so that advanced analytics can deliver real, actionable value through warehouse automation optimization.