VRP API Changelog - Solvice Docs

Stay updated with the latest enhancements, features, and improvements to the Solvice Vehicle Routing Problem (VRP) API. Each release brings new capabilities to help you optimize your routing operations more effectively.

Cost model parity, flexible break locations, and depot warm-starts

💶 Cost Model & Weights Parity

The costs and weights optimization models now cover the same ground, so you can express every trade-off in whichever style you prefer.

New Cost Fields
New Weight Fields

{
  "options": {
    "costs": {
      "depotCost": 1.0,
      "depotEndCost": 10.0
    }
  }
}

depotCost - cost per depot visit, controlling the trade-off between routing efficiency and depot cost
depotEndCost - cost for ending a route at a depot

{
  "options": {
    "weights": {
      "overtimeWeight": 1,
      "preferredVehicleWeight": 1,
      "regionMinimisationWeight": 1,
      "distanceWeight": 1
    }
  }
}

overtimeWeight - reluctance to schedule overtime work within the allowed overtime window
preferredVehicleWeight - reluctance to assign a job away from its preferred/planned resource
regionMinimisationWeight - reluctance to send resources outside their fixed region
distanceWeight - weight on total distance travelled

Use Cases:

Mix and match cost- and weight-based tuning across different parts of your configuration
Fine-tune overtime, preferred-resource, and region trade-offs without switching optimization models

🚫 Flexible Unavailability Break Locations

Unavailability breaks can now use separate start and end locations instead of a single fixed location.

{
  "resources": [{
    "name": "driver-1",
    "shifts": [{
      "from": "2026-06-01T08:00:00Z",
      "to": "2026-06-01T17:00:00Z",
      "breaks": [{
        "type": "UNAVAILABILITY",
        "from": "2026-06-01T10:00:00Z",
        "to": "2026-06-01T11:30:00Z",
        "start": {"latitude": 51.1079, "longitude": 17.0385},
        "end": {"latitude": 51.1200, "longitude": 17.0500}
      }]
    }]
  }]
}

Use Cases:

Drop a vehicle at a depot for a training session and pick up a different one afterwards
Model shift changes or vehicle swaps that occur mid-route

The existing single location field still works for breaks that start and end in the same place, but cannot be combined with start/end.

📍 Depot Warm-Starts & Visible Depot Stops

Pin a job to be followed by a specific depot visit with the new initialDepot field, and see depot stops clearly marked in the response.

{
  "jobs": [{
    "name": "collection-42",
    "initialDepot": "central-depot"
  }]
}

Depot stops now appear explicitly in trip visits with "activity": "DEPOT", so maps and polylines can render them accurately alongside regular pickups and deliveries.Use Cases:

Warm-start the solver with a known depot preference for a job
Render depot stops distinctly on maps and timelines

initialDepot requires depots to be defined in the request.

Depot management and pooled resource rules

🏭 Depot Management Suite

A set of new capabilities for routing problems that involve loading, unloading, and disposal sites (waste collection, distribution, returns).

Unload Before End of Shift
Depot End Weight
Smarter Depot Moves

Force a resource to visit a depot to unload its remaining load before finishing its shift.

{
  "resources": [{
    "name": "collection-truck",
    "shifts": [{
      "from": "2026-05-01T06:00:00Z",
      "to": "2026-05-01T14:00:00Z",
      "unloadBeforeEndShift": true
    }]
  }]
}

The unloadBeforeEndShift flag only fires when the vehicle still carries non-zero load at the end of its route, and only when depots are configured. Without it, a vehicle may end its shift while still loaded.

Tune how strongly the solver favors ending routes near a depot with the new depotEndWeight modifier, alongside the existing depotCostWeight.

{
  "options": {
    "weights": {
      "depotCostWeight": 1,
      "depotEndWeight": 10
    }
  }
}

👥 Pooled Resource Period Rules

Period rules (work time, service time, drive time, complexity, and job-type limits per period) can now be shared across a pool of resources using a groupTag.

{
  "resources": [{
    "name": "tech-1",
    "rules": [{
      "period": {"from": "2026-05-01T00:00:00Z", "to": "2026-05-08T00:00:00Z"},
      "jobTypeLimitations": {"Initial Appointment": 10},
      "groupTag": "contractor-install-pool"
    }]
  }, {
    "name": "tech-2",
    "rules": [{
      "period": {"from": "2026-05-01T00:00:00Z", "to": "2026-05-08T00:00:00Z"},
      "jobTypeLimitations": {"Initial Appointment": 10},
      "groupTag": "contractor-install-pool"
    }]
  }]
}

When a groupTag is set, the limit becomes a single shared bound across every resource that declares an identical rule (same period and fields) with that tag — instead of applying per resource.Use Cases:

Cap how many of a job type a team collectively handles per week
Share a contractor budget across multiple vehicles
Pool drive-time or complexity limits across a crew

Duty breaks, partial sequencing, and per-suggestion explanations

☕ Duty Breaks

A new DUTY break type triggers a mandatory break after a cumulative amount of work time (drive + service), complementing the existing DRIVE break (which counts driving time only).

{
  "resources": [{
    "name": "driver-1",
    "shifts": [{
      "from": "2026-04-01T08:00:00Z",
      "to": "2026-04-01T18:00:00Z",
      "breaks": [{
        "type": "DUTY",
        "workTime": 21600,
        "duration": 1800
      }]
    }]
  }]
}

Result: After 6 hours (21600s) of combined driving and service, the driver must take a 30-minute break.

🔗 True Partial Planning for SEQUENCE

SEQUENCE relations now support genuine partial planning — when some jobs in a sequence can’t be assigned, the solver still optimizes the order of the jobs that can be, instead of penalizing the whole relation.

🔍 Explanations Per Suggestion

The suggest endpoint can now return a full explanation for each individual suggestion, so you can see exactly why a proposed change scores the way it does — not just the final recommendation.

🏷️ Request Metadata

Attach arbitrary metadata to any request and have it echoed back on the response — handy for correlating jobs with your own systems.

{
  "metadata": {
    "orderId": "ORD-2026-0412",
    "tenant": "acme-logistics"
  }
}

Curbside-aware routing

🛻 Curbside Routing

Enable curbside-aware routing so vehicles approach each location from the correct side of the road.

{
  "options": {
    "curbside": true
  }
}

Use Cases:

Waste collection on residential streets
Right-hand/left-hand-side pickups and drop-offs
Any operation where the approach side matters for safety or access

Cost-based optimization

💶 Cost-Based Optimization

Optimize routes directly in monetary terms instead of tuning abstract weights. Provide a costs configuration and the solver derives its internal weights from your real business costs in EUR/USD.

{
  "costs": {
    "drivingCostPerHour": 25.0,
    "waitingCostPerHour": 15.0,
    "overtimeCostPerHour": 50.0,
    "distanceCostPerKm": 0.35,
    "timeWindowViolationCostPerHour": 20.0,
    "resourceActivationCost": 100.0
  }
}

Key Points:

Costs are more intuitive than manual weight tuning — they map to real business expenses
Covers driving, waiting, overtime, distance, time-window violations, preferred-resource and skill/ranking violations, workload imbalance, region adherence, and resource activation
If both costs and weights are provided, costs take precedence

Use Cases:

Report optimization results in real currency
Align routing trade-offs with actual P&L
Compare scenarios by total operating cost

AI-assisted routing with MCP integration

🤖 MCP Server Integration (AI-Assisted Routing)

Connect AI assistants directly to the VRP solver through the Model Context Protocol (MCP).

Overview
Use Cases

The MCP server exposes 8 action tools that AI assistants can use to solve routing problems:

vrp-solve / vrp-solve-sync - Full route optimization
vrp-evaluate / vrp-evaluate-sync - Score existing solutions
vrp-suggest / vrp-suggest-sync - Get improvement suggestions
vrp-change / vrp-change-sync - Apply incremental changes

Key Benefits:

Natural language problem description
Real-time streaming via SSE transport
AI understands constraints and trade-offs
Interactive route refinement

Skill-based duration adjustments

⚡ Proficiency-Based Duration Modifier

Adjust job durations based on resource skill levels.

Concept
Configuration

Different resources complete the same job at different speeds. A senior technician might finish an installation in 30 minutes while a junior takes 45 minutes.Proficiency modifies the base job duration without changing the job definition.

{
  "jobs": [{
    "name": "complex-installation",
    "duration": 3600,
    "proficiency": [
      {"resource": "senior-tech", "durationModifier": 0.8},
      {"resource": "junior-tech", "durationModifier": 1.3}
    ]
  }]
}

Result:

Senior tech: 3600 × 0.8 = 2880s (48 min)
Junior tech: 3600 × 1.3 = 4680s (78 min)

Return-to-depot optimization

🏁 Last Location Drive Time Optimization

Improved constraint ordering for efficient return-to-depot routing.The solver now better considers travel time from the last job back to the resource’s end location when ordering constraints, ensuring routes don’t end far from the depot.How it works:

Automatically factors in return travel when comparing route options
Penalizes routes that end geographically far from the depot
Works with existing driveTimeWeight for consistent optimization

Benefits:

Prevents routes ending far from depot
Reduces dead-heading at end of day
Better overall route efficiency
No configuration needed - automatically applied

Clustering, distance limits, and debugging tools

📍 Geographic Clustering & Job Proximity

Improve route compactness with intelligent job proximity scoring.

{
  "options": {
    "weights": {
      "jobProximityWeight": 25
    }
  }
}

Benefits:

Reduces backtracking between distant jobs
Creates more geographically compact routes
Configurable weight to balance with other objectives
Better cluster assignment for multi-vehicle problems

🔷 H3 Grid System for Geographic Clustering

Leverage Uber’s H3 hexagonal grid system for precise geographic clustering via the clustering endpoint.

{
  "locations": [
    {"latitude": 50.8503, "longitude": 4.3517, "name": "Brussels"},
    {"latitude": 51.0543, "longitude": 3.7174, "name": "Ghent"},
    {"latitude": 51.2213, "longitude": 4.4051, "name": "Antwerp"}
  ],
  "options": {
    "clusterSize": 10,
    "maxClusters": 5,
    "h3Resolution": 7
  }
}

H3 Resolution Guide:

Resolution	Hex Size	Use Case
5	~250 km²	Regional planning
7	~5 km²	Urban delivery
9	~174m edge	Default, fine clustering

📏 Maximum Drive Distance

Limit total kilometers per resource shift.

{
  "resources": [{
    "name": "driver-1",
    "maxDriveDistance": 200000,  // 200 km max
    "shift": {
      "from": "2025-01-01T08:00:00Z",
      "to": "2025-01-01T18:00:00Z"
    }
  }]
}

Use Cases:

Vehicle range limitations (EVs)
Company policy compliance
Lease mileage restrictions
Driver safety regulations

🐛 Debug Endpoint

New /debug endpoint for troubleshooting solver behavior.

GET /v2/vrp/debug/{jobId}

Returns:

Internal solver state
Constraint violation details
Score breakdown
Shadow variable values
Diagnostic information for support tickets

Relation weight customization

⚖️ Custom Relation Weights

Fine-tune the importance of individual relations with custom weight modifiers.

{
  "relations": [{
    "type": "SAME_RESOURCE",
    "jobs": ["pickup", "delivery"],
    "weight": 50
  }, {
    "type": "SEQUENCE",
    "jobs": ["prep", "install"],
    "weight": 100
  }]
}

Use Cases:

Prioritize certain relations over others
Make critical sequences non-negotiable (high weight)
Allow flexibility on less important pairings (low weight)
Balance relation penalties with other optimization objectives

Custom maps and relation constraints

🗺️ External Distance Matrices

Use pre-computed distance matrices from external services with time-of-day traffic patterns.External distance matrices allow you to provide custom travel time and distance data instead of relying on built-in routing engines. This is perfect for:

Faster Processing: Skip real-time distance calculations using pre-computed matrices
Custom Traffic: Use your own traffic data or specialized routing services
Time-Based Routing: Different matrices for morning rush, midday, evening periods
Vehicle-Specific: Separate matrices for cars, trucks, bikes with their unique constraints
Enterprise Integration: Seamlessly integrate with existing routing infrastructure

Upload your distance matrices to routing.solvice.io/table/upload and reference them by ID.

{
  "jobs": [
    {"name": "delivery-1", "location": {"latitude": 51.1279, "longitude": 17.0485}},
    {"name": "delivery-2", "location": {"latitude": 51.1350, "longitude": 17.0600}}
  ],
  "resources": [{
    "name": "driver-1",
    "category": "CUSTOM_VEHICLE",
    "shift": {
      "from": "2025-01-01T08:00:00Z",
      "to": "2025-01-01T18:00:00Z"
    }
  }],
  "customDistanceMatrices": {
    "profileMatrices": {
      "CAR": {
        "8": "matrix-car-morning-rush",    // 8 AM matrix ID
        "12": "matrix-car-midday",        // 12 PM matrix ID
        "16": "matrix-car-evening-rush"   // 4 PM matrix ID
      },
      "TRUCK": {
        "6": "matrix-truck-early",
        "12": "matrix-truck-midday"
      }
    }
  }
}

⚖️ Hard Minimum Wait for Job Relations

Fine-tune constraint strength for job relations with the hardMinWait flag.

{
  "relations": [{
    "type": "SEQUENCE",
    "jobs": ["job-a", "job-b"],
    "minTimeInterval": 3600,
    "hardMinWait": true
  }]
}

When hardMinWait: true (default), the minimum time interval becomes a hard constraint that cannot be violated. Set to false to make it a soft preference that can be traded off against other objectives.Use Cases:

Mandatory cooling/drying periods between services
Required waiting time for concrete curing, paint drying
Compliance with safety regulations

Enhanced preference systems and intelligent job handling

🎯 Resource Ranking System

Express nuanced preferences for resource-job assignments with our new flexible ranking system.

Overview
Implementation
Use Cases

The ranking system allows you to specify preferred resources for each job on a 1-100 scale, where lower values indicate stronger preference.Key Benefits:

Implement customer preferences without hard constraints
Balance skill levels across assignments
Optimize for service quality alongside efficiency
Maintain flexibility in resource allocation

{
  "jobs": [{
    "name": "Premium-Service",
    "rankings": [
      {"name": "Senior-Tech-1", "ranking": 10},  // Strongly preferred
      {"name": "Junior-Tech-1", "ranking": 70}   // Acceptable if needed
    ]
  }],
  "weights": {
    "rankingWeight": 10
  }
}

Rankings work alongside existing constraints like tags and regions. They provide soft preferences that the optimizer considers when making assignments.

📍 Location Inheritance

Simplify multi-stop scenarios where jobs share locations through automatic location inheritance.

{
  "jobs": [
    {
      "name": "pickup",
      "location": {"latitude": 51.1279, "longitude": 17.0485}
    },
    {
      "name": "delivery",
      "location": {"latitude": 51.1279, "longitude": 17.0485}  // Duplicate
    }
  ]
}

{
  "jobs": [
    {
      "name": "pickup",
      "location": {"latitude": 51.1279, "longitude": 17.0485}
    },
    {
      "name": "delivery"
      // Location inherited from related job
    }
  ],
  "relations": [{
    "type": "SAME_RESOURCE",
    "jobs": ["pickup", "delivery"]
  }]
}

Perfect for:

Pickup and delivery pairs
Multi-service appointments at same address
Loading/unloading operations
Any co-located job sequences

🔍 Enhanced Unassigned Job Explanations

Get detailed, actionable insights when jobs cannot be assigned to understand exactly why and how to resolve issues.Common Unassignment Reasons:

DATE_TIME_WINDOW_CONFLICT - No overlap between job window and shifts
TRIP_CAPACITY / RESOURCE_CAPACITY - Vehicle capacity insufficient
TAG_HARD / TYPE_REQUIREMENT - Required tags not available
MAX_DRIVE_DISTANCE - Location outside service area

⚖️ Job Complexity & Fair Distribution

Define job difficulty independent of duration to ensure fair workload distribution across your team.

Concept
Configuration
Benefits

Job complexity represents the mental, physical, or technical difficulty of a task, separate from how long it takes.Examples:

Simple delivery: Duration 30min, Complexity 20
Complex installation: Duration 30min, Complexity 80
Heavy lifting: Duration 15min, Complexity 70

{
  "jobs": [{
    "name": "Technical-Installation",
    "duration": 3600,
    "complexity": 80  // High complexity
  }, {
    "name": "Standard-Delivery",
    "duration": 3600,
    "complexity": 20  // Low complexity
  }],
  "options": {
    "fairComplexityPerTrip": true,
    "fairComplexityPerResource": true
  },
  "resources": [{
    "name": "Tech-1",
    "rules": [{
      "maxJobComplexity": 200
    }]
  }]
}

🚦 Full TomTom Traffic Integration

Enhanced real-time and predictive traffic routing with complete TomTom API integration.

{
  "options": {
    "routingEngine": "TOMTOM",
    "traffic": 1.2  // Traffic modifier for TOMTOM routing
  }
}

Key Enhancements:

Live Traffic: Real-time congestion avoidance
Predictive Routing: Historical patterns for future planning
Departure Optimization: Find best start times to avoid traffic
Vehicle-Specific Routes: Truck restrictions and clearances
15% Average Time Savings: Compared to static routing

💰 Resource Hourly Wage Optimization

Optimize routes considering different hourly rates to balance service quality with labor costs.

{
  "resources": [{
    "name": "Senior-Tech",
    "hourlyCost": 75
  }, {
    "name": "Junior-Tech", 
    "hourlyCost": 45
  }]
}

{
  "resources": [{
    "name": "Contractor",
    "hourlyCost": 95
  }],
  "weights": {
    "minimizeResourcesWeight": 150  // Activation cost equivalent
  }
}

Optimization Strategies:

Assign simple tasks to lower-cost resources
Use senior staff for complex/critical jobs
Minimize overtime by balancing workloads
Consider total cost including travel time

🚫 Unavailability Breaks

Model realistic schedules with unavailability periods for meetings, training, or personal time.

{
  "resources": [{
    "name": "Tech-1",
    "shifts": [{
      "from": "2025-01-01T08:00:00Z",
      "to": "2025-01-01T17:00:00Z",
      "breaks": [{
        "type": "UNAVAILABILITY",
        "from": "2025-01-01T10:00:00Z",
        "to": "2025-01-01T11:30:00Z",
        "location": {"latitude": 51.1079, "longitude": 17.0385}
      }, {
        "type": "WINDOWED",
        "from": "2025-01-01T12:00:00Z",
        "to": "2025-01-01T13:00:00Z", 
        "duration": 3600
      }]
    }]
  }]
}

Supported Break Types:

UNAVAILABILITY - Cannot be scheduled during this period
WINDOWED - Flexible timing within window
DRIVE - Mandatory after specified driving time

New relation types and large-scale optimizations

🥇 First Job Relation

Force specific jobs to be scheduled first in a resource’s route by submitting a SEQUENCE relation with a single job ID. When a SEQUENCE relation contains only one job, the solver pins that job to be scheduled first, rather than requiring a separate relation type.

{
  "jobs": [{
    "name": "warehouse-pickup"
  }],
  "relations": [{
    "type": "SEQUENCE",
    "jobs": ["warehouse-pickup"]
  }]
}

{
  "jobs": [{
    "name": "morning-task",
    "windows": [{
      "from": "2025-01-01T08:00:00Z",
      "to": "2025-01-01T08:30:00Z"  // Tight early window
    }],
    "priority": 100
  }]
}

Common Use Cases:

Warehouse pickups before deliveries
Equipment collection at start of day
Mandatory briefings or check-ins
Load vehicles before service rounds

🔤 Group Sequence Relations

Define execution order between groups of jobs using tags with the GROUP_SEQUENCE relation.

{
  "jobs": [
    {"name": "urgent-1", "tags": [{"name": "priority-high"}]},
    {"name": "urgent-2", "tags": [{"name": "priority-high"}]},
    {"name": "normal-1", "tags": [{"name": "priority-normal"}]},
    {"name": "low-1", "tags": [{"name": "priority-low"}]}
  ],
  "relations": [{
    "type": "GROUP_SEQUENCE",
    "tags": ["priority-high", "priority-normal", "priority-low"]
  }]
}

Benefits:

Implement service level agreements
Handle emergency vs routine work
Manage phased operations
Prioritize revenue-generating activities

🚀 Large-Scale TSP Optimizations

Significant performance improvements for Traveling Salesman Problem instances with 100+ stops.

Improvements
Configuration
Best Practices

Algorithm Enhancements:

Advanced nearest neighbor initialization
Parallel 2-opt and 3-opt local search
Adaptive neighborhood sizing
Memory-efficient distance matrix handling

Performance Gains:

65% faster for 500+ job instances
40% memory reduction
Better solution quality (+8% average)
Stable performance up to 10,000 jobs

{
  "options": {
    "partialPlanning": true,
    "minimizeResources": true
  }
}

10,000+ job support and dynamic traffic routing

📈 Enterprise-Scale Problem Handling

Revolutionary improvements for handling massive routing problems with 10,000+ jobs.

Intelligent Chunking

Dynamic partitioning based on geographic clusters and time windows for optimal sub-problem creation.

Parallel Processing

Multi-threaded execution with smart work distribution across CPU cores.

Adaptive Algorithms

Automatic algorithm selection based on problem characteristics and size.

Memory Optimization

Streaming distance calculations and compressed data structures reduce memory by 60%.

Real-World Results:

Before: 5,000 job limit, 45-minute processing
After: 50,000 jobs supported, 15-minute average
Quality: Maintained 98%+ optimality
Stability: 99.9% completion rate

🗺️ TomTom Traffic Integration

Time-dependent routing with real-world traffic conditions for accurate ETAs and optimal departure times.

How It Works

The solver now uses a three-dimensional distance cube (origin × destination × time) instead of a static two-dimensional matrix:

Morning Rush (6-9 AM): Increased travel times on highways
Midday (9 AM-4 PM): Normal traffic conditions
Evening Rush (4-7 PM): City center congestion
Night (7 PM-6 AM): Reduced traffic, faster routes

The optimizer automatically:

Adjusts departure times to avoid traffic
Reroutes around predicted congestion
Updates ETAs based on time of day
Balances traffic avoidance with service windows

Configuration Example:

{
  "options": {
    "routingEngine": "TOMTOM",
    "traffic": 1.1  // Traffic multiplier
  }
}

Extended planning horizons and synchronous processing

📅 Multi-Day Job Support

Execute long-duration jobs across multiple shifts and days with intelligent work continuation.

{
  "jobs": [{
    "name": "major-project",
    "duration": 72000,  // 20 hours total
    "resumable": true,
    "windows": [
      {"from": "2024-07-01T08:00:00Z", "to": "2024-07-01T17:00:00Z"},
      {"from": "2024-07-02T08:00:00Z", "to": "2024-07-02T17:00:00Z"},
      {"from": "2024-07-03T08:00:00Z", "to": "2024-07-03T17:00:00Z"}
    ]
  }],
  "resources": [{
    "name": "tech-1",
    "shifts": [
      {"from": "2024-07-01T08:00:00Z", "to": "2024-07-01T17:00:00Z"},
      {"from": "2024-07-02T08:00:00Z", "to": "2024-07-02T17:00:00Z"},
      {"from": "2024-07-03T08:00:00Z", "to": "2024-07-03T17:00:00Z"}
    ]
  }]
}

{
  "schedule": [
    {"date": "2024-07-01", "work": "08:00-17:00 (8h)"},
    {"date": "2024-07-02", "work": "08:00-17:00 (8h)"},
    {"date": "2024-07-03", "work": "08:00-12:00 (4h)"}
  ],
  "totalDuration": 72000,
  "completionTime": "2024-07-03T12:00"
}

⚡ Synchronous API Endpoints

New /sync/* endpoints for immediate responses perfect for interactive applications.

Overview
When to Use
Example

Available Endpoints:

/sync/solve - Instant route optimization
/sync/evaluate - Real-time solution scoring
/sync/suggest - Live optimization hints

Key Features:

Sub-2 second response times
No webhook configuration
Automatic timeout handling
Perfect for UI integration

// Synchronous call for immediate response
const response = await fetch('/v2/vrp/sync/solve', {
  method: 'POST',
  body: JSON.stringify({
    jobs: [...],  // Limited to 50 jobs
    resources: [...],
    options: { timeout: 2000 }
  })
});

const solution = await response.json();
// Solution returned directly, no polling needed

Synchronous endpoints have strict limits:

Maximum 50 jobs per request
2-second timeout (configurable up to 5s)
Automatic fallback to async for larger problems

Complete architectural overhaul

🎉 VRP API v2 Release

After 2 years of development, v2 brings a complete architectural redesign focused on scalability, reliability, and performance.

Architecture Evolution

Infrastructure
Performance
API Enhancements

From Kubernetes to Serverless:

Google Cloud Run for auto-scaling
Cloud Pub/Sub for async processing
Cloud Storage for results
90% reduction in operational overhead
99.99% uptime SLA

Migration Guide

Update Base URL

Change from api.solvice.io/v1 to api.solvice.io/v2

Review Breaking Changes

vehicle renamed to resource
timeWindow now supports arrays
New required fields in response

Test Thoroughly

Use our migration validator endpoint to check your requests

Gradual Rollout

Run v1 and v2 in parallel during transition

Success Story: Major logistics provider migrated 50,000 daily optimizations to v2 with zero downtime and 35% cost reduction.

Stay Updated

API Reference

Detailed documentation for all endpoints

Migration Guide

Step-by-step v1 to v2 migration

Feature Guides

In-depth guides for each feature

Release Notes

Detailed technical release notes

Subscribe to our RSS feed or follow @solvice_io for real-time updates about new features and improvements.

​💶 Cost Model & Weights Parity

​🚫 Flexible Unavailability Break Locations

​📍 Depot Warm-Starts & Visible Depot Stops

​🏭 Depot Management Suite

​👥 Pooled Resource Period Rules

​☕ Duty Breaks

​🔗 True Partial Planning for SEQUENCE

​🔍 Explanations Per Suggestion

​🏷️ Request Metadata

​🛻 Curbside Routing

​💶 Cost-Based Optimization

​🤖 MCP Server Integration (AI-Assisted Routing)

​⚡ Proficiency-Based Duration Modifier

​🏁 Last Location Drive Time Optimization

​📍 Geographic Clustering & Job Proximity

​🔷 H3 Grid System for Geographic Clustering

​📏 Maximum Drive Distance

​🐛 Debug Endpoint

​⚖️ Custom Relation Weights

​🗺️ External Distance Matrices

​⚖️ Hard Minimum Wait for Job Relations

​🎯 Resource Ranking System

​📍 Location Inheritance

​🔍 Enhanced Unassigned Job Explanations

​⚖️ Job Complexity & Fair Distribution

​🚦 Full TomTom Traffic Integration

​💰 Resource Hourly Wage Optimization

​🚫 Unavailability Breaks

​🥇 First Job Relation

​🔤 Group Sequence Relations

​🚀 Large-Scale TSP Optimizations

​📈 Enterprise-Scale Problem Handling

​🗺️ TomTom Traffic Integration

​📅 Multi-Day Job Support

​⚡ Synchronous API Endpoints

​🎉 VRP API v2 Release

​Architecture Evolution

​Migration Guide

​Stay Updated

API Reference

Migration Guide

Feature Guides

Release Notes

💶 Cost Model & Weights Parity

🚫 Flexible Unavailability Break Locations

📍 Depot Warm-Starts & Visible Depot Stops

🏭 Depot Management Suite

👥 Pooled Resource Period Rules

☕ Duty Breaks

🔗 True Partial Planning for SEQUENCE

🔍 Explanations Per Suggestion

🏷️ Request Metadata

🛻 Curbside Routing

💶 Cost-Based Optimization

🤖 MCP Server Integration (AI-Assisted Routing)

⚡ Proficiency-Based Duration Modifier

🏁 Last Location Drive Time Optimization

📍 Geographic Clustering & Job Proximity

🔷 H3 Grid System for Geographic Clustering

📏 Maximum Drive Distance

🐛 Debug Endpoint

⚖️ Custom Relation Weights

🗺️ External Distance Matrices

⚖️ Hard Minimum Wait for Job Relations

🎯 Resource Ranking System

📍 Location Inheritance

🔍 Enhanced Unassigned Job Explanations

⚖️ Job Complexity & Fair Distribution

🚦 Full TomTom Traffic Integration

💰 Resource Hourly Wage Optimization

🚫 Unavailability Breaks

🥇 First Job Relation

🔤 Group Sequence Relations

🚀 Large-Scale TSP Optimizations

📈 Enterprise-Scale Problem Handling

🗺️ TomTom Traffic Integration

📅 Multi-Day Job Support

⚡ Synchronous API Endpoints

🎉 VRP API v2 Release

Architecture Evolution

Migration Guide

Stay Updated