CHOOSE

Space Utilization and Layout Optimization

Optimize manufacturing facility layouts and warehouse space allocation to minimize material handling costs while maximizing productive floor area utilization.

Understanding the Problem

Space utilization and facility layout optimization directly impacts operational efficiency, costs, and competitiveness in manufacturing. Manufacturers waste up to 30% of usable space due to inefficient layout and storage practices, while 47% of warehouses report needing more capacity. The Quadratic Assignment Problem (QAP) provides the mathematical framework for optimizing facility-to-location assignments to minimize costs from material flows.

THE CHALLENGE

What Makes it Hard

Facility layout optimization requires solving the NP-hard Quadratic Assignment Problem to assign facilities to possible locations minimizing the total cost for material flow. Current models are often single-objective and static, overlooking real-time reconfigurability, energy consumption, and occupational safety while failing to address multi-floor vertical complexities and simultaneous multi-objective optimization.

WHO FACES IT

Plant managers and facility directorsIndustrial engineers and manufacturing engineersWarehouse operations managersSupply chain and logistics executivesOperational excellence and continuous improvement leaders

Multi-objective conflicts between economic efficiency, safety, space utilization, flexibility, and energy consumption

NP-hard problem space with n! possible arrangements making exhaustive search impractical

Current metaheuristics suffer from premature convergence or local optima entrapment in complex scenarios

BUSINESS IMPACT

Close the 25-point utilization gap (54% vs 79% target) and unlock $11,000 annual savings per employee through optimized workspace design.

Utilization Gap

54%

vs 79% Target^[1]

Global office utilization averages just 54% versus organizational targets of 79%, revealing a 25-point gap.

Savings Potential

$11k

Per Employee/Year^[2]

Organizations can achieve up to $11,000 in annual savings per employee by optimizing workspace usage.

Strategic Priority

73%

Prioritize Optimization^[3]

73% of corporate real estate leaders now prioritize portfolio optimization over cost-cutting.

How We Solve It

Solve Quadratic Assignment Problems using hybrid metaheuristic algorithms (genetic algorithms combined with simulated annealing) for medium-to-large facilities, commercial MILP solvers (Gurobi, CPLEX) for smaller exact optimization, and emerging quantum annealing for rapid approximate solutions. Digital twin integration enables 3D simulation validation before physical implementation, while AI-driven multi-objective frameworks optimize material handling, energy consumption, and carbon emissions simultaneously.

Heterogeneous
Hybrid Compute

What We Bring

Quadratic Assignment Problem (QAP) solving for optimal facility-to-location assignments

Hybrid GA-SA metaheuristics balancing global exploration with local search effectiveness

Quantum annealing providing solutions one order of magnitude faster than Gurobi with <3% optimality gap

AI-driven multi-objective optimization with digital twin validation for real-time reconfigurable layouts

FUTURE POSSIBILITIES

The
Quantum Horizon

Quantum annealing solved QAP size 19 with high accuracy on D-Wave Advantage (first time at this scale), while 2025 benchmarking shows quantum solvers achieving 6,561x faster solving time with 0.013% higher accuracy than best classical solvers for specific problem classes. Wind farm layout optimization via QUBO achieved one order of magnitude speedup versus Gurobi with minimal quality trade-off.

Exploratory Work

While exact methods can solve QAP instances up to size 30, quantum computing offers potential for problems impractical on classical computers or considerable speedups. Research on Rydberg atom arrays provides alternative quantum approaches beyond gate-based and annealing paradigms. The hybrid quantum-classical architecture combining quantum speedup for specific computations with classical reliability appears most practical for near-term industrial applications, though current qubit limitations remain significant.