OPERATIONAL RESEARCH

Routing & Optimization Engine

Constraint-aware optimization for complex, dynamic systems.

Built on operational research foundations to solve problems where heuristics and static planning fail.

SANDBOX DEMO

v4.2.0-stable

Representative simulation. Production systems operate continuously at scale.

Why Optimization Breaks in Production

Competing Constraints

Real-world ops require balancing time windows, vehicle capacities, driver breaks, and hazmat rules simultaneously.

Dynamic Conditions

A perfect plan at 8:00 AM is obsolete by 8:15 AM due to traffic, cancellations, or urgent new orders.

Manual Overrides

When dispatchers manually intervene to fix one problem, they unknowingly break optimality across the entire network.

Static Solvers

Traditional VRP solvers take hours to run, making them useless for real-time operational adjustments.

System Definition

What the Routing & Optimization Engine Is

A real-time optimization system designed to continuously recompute decisions under changing constraints, rather than producing static plans. It treats logistics not as a math problem to be solved once, but as a fluid system to be orchestrated.

real-time optimization system

real-time optimization system

real-time optimization system

Core Capabilities

Constraint-Aware Optimization

Model complex business rules natively. From strict time windows and compatibility matrices to union rules and physical access restrictions.

Constraint-Aware Optimization

Model complex business rules natively. From strict time windows and compatibility matrices to union rules and physical access restrictions.

Constraint-Aware Optimization

Model complex business rules natively. From strict time windows and compatibility matrices to union rules and physical access restrictions.

Dynamic Re-Planning

Trigger re-optimization instantly upon new events without disrupting the rest of the fleet.

Dynamic Re-Planning

Trigger re-optimization instantly upon new events without disrupting the rest of the fleet.

Dynamic Re-Planning

Trigger re-optimization instantly upon new events without disrupting the rest of the fleet.

Multi-Objective Tradeoffs

Balance competing goals: Minimize cost vs. Maximize SLA vs. Equalize driver workload. Tunable in real-time.

Multi-Objective Tradeoffs

Balance competing goals: Minimize cost vs. Maximize SLA vs. Equalize driver workload. Tunable in real-time.

Multi-Objective Tradeoffs

Balance competing goals: Minimize cost vs. Maximize SLA vs. Equalize driver workload. Tunable in real-time.

Human-in-the-Loop Overrides

Respect manual decisions while re-optimizing around them. The engine guides dispatchers rather than fighting them, showing the impact of every override.

Human-in-the-Loop Overrides

Respect manual decisions while re-optimizing around them. The engine guides dispatchers rather than fighting them, showing the impact of every override.

Human-in-the-Loop Overrides

Respect manual decisions while re-optimizing around them. The engine guides dispatchers rather than fighting them, showing the impact of every override.