SimRunner
Simulation optimization and experimentation for breakthrough performance
Simulation Optimization & Experimentation
SimRunner is an advanced simulation optimization tool that automatically searches for the best system configuration by intelligently exploring the solution space. Using sophisticated optimization algorithms, it finds optimal parameter settings that maximize or minimize specified performance measures in your simulation models.
From manufacturing systems to service operations, SimRunner transforms simulation from analysis to optimization, enabling organizations to discover breakthrough performance improvements that would be impossible to find through manual experimentation.
Case Study: Distribution Center Optimization
The Challenge
LogisticsPro operated a complex distribution center with hundreds of controllable parameters including staffing levels, equipment allocation, routing policies, and scheduling rules. Manual optimization was impractical due to the vast number of possible combinations and their complex interactions.
The Solution
Using SimRunner with their ProModel simulation, the operations team defined optimization objectives (minimize cost while maintaining service levels) and specified controllable factors. SimRunner automatically ran thousands of simulation experiments to find the optimal configuration.
Results Achieved
Optimization Features
Evolutionary Optimization
Advanced genetic algorithms for finding global optima in complex solution spaces
Neural Network Metamodels
AI-powered metamodels to accelerate optimization and reduce simulation runs
Multi-Objective Optimization
Simultaneous optimization of multiple conflicting objectives with Pareto frontiers
Design of Experiments
Statistical experimental design for efficient factor screening and analysis
Sensitivity Analysis
Identify critical factors and their impact on system performance
Automated Reporting
Comprehensive optimization reports with statistical analysis and recommendations
Optimization Methodologies
Evolutionary Algorithms
- • Genetic algorithms for global optimization
- • Evolution strategies for continuous variables
- • Particle swarm optimization
- • Adaptive parameter control
Statistical Methods
- • Response surface methodology
- • Factorial and fractional designs
- • Central composite designs
- • Taguchi methods
Machine Learning
- • Neural network surrogate models
- • Gaussian process optimization
- • Bayesian optimization
- • Active learning strategies
Hybrid Approaches
- • Multi-method optimization strategies
- • Adaptive algorithm selection
- • Parallel optimization execution
- • Constraint handling techniques
Optimization Applications
Manufacturing Systems
- • Production line balancing
- • Inventory level optimization
- • Maintenance scheduling
- • Quality control parameter tuning
Supply Chain & Logistics
- • Distribution network design
- • Warehouse layout optimization
- • Transportation routing
- • Demand-supply matching
Service Operations
- • Staffing level optimization
- • Service capacity planning
- • Queue management strategies
- • Resource allocation policies
Healthcare Systems
- • Patient flow optimization
- • Operating room scheduling
- • Emergency department design
- • Resource utilization maximization