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Tag & Ranking System

The tag and ranking system enables sophisticated matching between job requirements and resource capabilities, while also allowing preference-based assignments. This guide covers both hard skill requirements and soft preference rankings.

Tag System Overview

Tags represent skills, certifications, equipment, or any other matching criteria:

Tag Matching Rules

Hard Tags

Strict requirements that must be matched:
Hard Tag Rule: If a job has a hard tag, only resources with that same hard tag can be assigned to it.

Soft Tags

Preferences that create penalties when not matched:

Tag Weight Calculation

There is no global tag-weight multiplier. Each soft tag carries its own weight, which is applied directly as the penalty when the tag isn’t matched — measured in the same units as travel time (a weight of 3600 is equivalent to one hour of extra travel time).Example: A soft tag {"name": "senior", "weight": 100} that goes unmatched contributes a penalty of 100, on the same scale as travelTimeWeight.

Ranking System

Rankings provide fine-grained resource preferences on a 1-100 scale:

Ranking Scale

1

1-10: Strongly Preferred

Primary choices for this job
2

11-30: Preferred

Good matches with minor preferences
3

31-70: Neutral

Acceptable assignments
4

71-100: Discouraged

Use only if necessary

Combining Tags and Rankings

Use both systems together for complex requirements:

Real-World Examples

Customer Preferences

Track and honor customer preferences:

Skill-Based Assignment

Match complex skill requirements:

Equipment-Based Matching

Ensure proper equipment for each job:

Advanced Patterns

Hierarchical Skills

Model skill levels and specializations:

Team Assignments

Use tags to keep teams together:

Best Practices

Configuration Strategies

Balanced Approach

There is no global tag-importance weight — tune each soft tag’s own weight value on the job/resource, and balance it against the other objective weights below:

Skill-Critical Operations

Raise the weight on individual soft tags (e.g. {"name": "cisco-certified", "hard": false, "weight": 5000}) so skill mismatches dominate, and pair that with:

Troubleshooting

Common Causes:
  • Missing required hard tags
  • Tag typos (case-sensitive)
  • Resource doesn’t have all required tags
Debug Steps:
  1. Check exact tag spelling
  2. Verify all hard tags present
  3. Use explanation endpoint
  4. Try removing tags one by one
Common Causes:
  • Low ranking/tag weights
  • Competing objectives (travel time)
  • Missing preference data
Solutions:
  • Increase rankingWeight
  • Add explicit rankings
  • Review all constraint weights
Common Causes:
  • Too many unique tags
  • Complex soft tag combinations
  • Many rankings per job
Solutions:
  • Consolidate similar tags
  • Use hard tags when possible
  • Limit rankings to key resources

Integration Examples

With Time Windows

Rankings respect time availability:

With Regions

Resource.region is a single geographic point (not a named zone or list), expressing a soft preference for keeping that resource working near it. There’s no equivalent region field on a job — combine a resource’s region with tags for skill matching and rely on the job’s own location for the geographic side:

Resource Management

Configure resource capabilities

Advanced Constraints

Additional matching rules

Cost Optimization

Balance skills with costs

Job Relations

Complex job dependencies