# Custom Functions Design for Selen Constraint Solver
## Overview
This document explores the design and implementation of custom user-defined functions in the Selen constraint solver. Custom functions would allow end users to extend the solver's capabilities with domain-specific logic, integration hooks, and specialized constraints.
## Use Cases and Situations
### 1. Custom Constraint Logic
Complex business rules that are difficult to express with standard constraints:
```rust
// User-defined constraint function
fn custom_workload_balance(schedule: &Schedule, employees: &[Employee]) -> bool {
// Complex business logic that's hard to express with basic constraints
// e.g., "Senior employees should work max 1 weekend shift per month"
// "No employee should work alone on night shifts"
// "At least one bilingual employee per shift in customer service"
}
fn enforce_seniority_rules(assignments: &[Assignment]) -> bool {
// "Senior staff must supervise training shifts"
// "Junior staff cannot close alone"
// "Minimum experience level per shift type"
}
```
### 2. Domain-Specific Cost Functions
Custom optimization objectives for specialized domains:
```rust
// Custom optimization objective
fn minimize_commute_cost(assignments: &[Assignment]) -> f64 {
// Calculate total commute cost based on employee locations
// Factor in traffic patterns, distance, fuel costs
assignments.iter().map(|a| calculate_commute_cost(a)).sum()
}
fn minimize_overtime_variance(schedule: &Schedule) -> f64 {
// Minimize variance in overtime distribution
// Balance workload fairly across team
}
fn maximize_skill_coverage(assignments: &[Assignment]) -> f64 {
// Ensure optimal skill mix per shift
// Prioritize cross-training opportunities
}
```
### 3. Complex Validation Rules
Business rule validation that goes beyond simple constraints:
```rust
// Business rule validation
fn validate_union_rules(schedule: &Schedule) -> ValidationResult {
// Check complex labor union requirements
// e.g., "No more than 3 consecutive night shifts"
// "Minimum 12 hours between shifts"
// "Maximum 6 days without a day off"
// "Break requirements for shifts over 8 hours"
}
fn validate_compliance_rules(schedule: &Schedule) -> ValidationResult {
// Healthcare: patient-to-nurse ratios
// Aviation: pilot rest requirements
// Manufacturing: safety certification requirements
}
```
### 4. Dynamic Constraint Generation
Generate constraints based on external data sources:
```rust
// Generate constraints based on external data
fn generate_availability_constraints(employee_id: usize, date: Date) -> Vec<Constraint> {
// Query external calendar system
// Check vacation schedules, training sessions, etc.
// Medical appointments, family obligations
// Real-time availability updates
}
fn generate_capacity_constraints(facility_id: usize, date: Date) -> Vec<Constraint> {
// Equipment maintenance schedules
// Room availability and capacity
// Special events or closures
}
```
### 5. Integration Hooks and Callbacks
Integration with external systems and workflows:
```rust
// Integration with external systems
fn notify_schedule_change(old_schedule: &Schedule, new_schedule: &Schedule) {
// Send notifications via Slack/email
// Update external HR systems
// Log changes for compliance auditing
// Trigger payroll system updates
}
fn sync_with_external_systems(schedule: &Schedule) -> Result<(), Error> {
// Update workforce management systems
// Sync with time tracking systems
// Integration with facility management
}
```
### 6. Specialized Heuristics
Domain-specific search strategies and optimization hints:
```rust
fn healthcare_scheduling_heuristic(partial_solution: &PartialSolution) -> Vec<Variable> {
// Prioritize critical care staffing
// Balance experience levels per shift
// Consider patient acuity levels
}
fn manufacturing_shift_heuristic(partial_solution: &PartialSolution) -> Vec<Variable> {
// Prioritize production line coverage
// Ensure quality control staffing
// Balance machine operator skills
}
```
## Potential API Designs
### Option 1: Callback-based Approach
```rust
// Simple callback registration
validate_business_rules(solution)
});
calculate_total_cost(solution)
});
// With context and parameters
});
```
**Pros:**
- Simple and intuitive
- Easy to understand and use
- Low overhead for simple functions
**Cons:**
- Limited error handling
- Harder to compose multiple functions
- Performance concerns with closures
### Option 2: Trait-based System
```rust
trait CustomConstraint {
fn validate(&self, solution: &Solution) -> bool;
fn cost(&self, solution: &Solution) -> f64;
fn description(&self) -> String;
}
trait CustomObjective {
fn evaluate(&self, solution: &Solution) -> f64;
fn is_minimizing(&self) -> bool { true }
}
// Usage
struct WorkloadBalancer {
max_variance: f64,
employee_weights: HashMap<usize, f64>,
}
impl CustomConstraint for WorkloadBalancer {
fn validate(&self, solution: &Solution) -> bool {
// Implementation
}
}
model.add_constraint(Box::new(WorkloadBalancer::new()));
```
**Pros:**
- Type-safe and extensible
- Good error handling capabilities
- Easy to test and debug
- Composable design
**Cons:**
- More verbose for simple cases
- Requires understanding of traits
- Potential for trait object overhead
### Option 3: Plugin System
```rust
// Dynamic loading of constraint plugins
model.load_plugin("vacation_scheduler.so")?;
model.load_plugin("union_rules.dll")?;
model.apply_plugin_constraints();
// Or compile-time plugins
model.register_plugin::<VacationScheduler>();
model.register_plugin::<UnionRulesChecker>();
```
**Pros:**
- Highly modular and reusable
- Can distribute domain-specific logic
- Hot-swappable functionality
**Cons:**
- Complex implementation
- Safety and security concerns
- Platform-specific considerations
### Option 4: Declarative Configuration
```rust
// JSON/YAML-based rule definition
let rules = r#"
{
"constraints": [
{
"type": "max_consecutive",
"shift_type": "night",
"max_count": 3,
"applies_to": "all_employees"
},
{
"type": "min_rest_hours",
"hours": 12,
"between_shift_types": ["day", "night"]
}
]
}
"#;
model.load_rules_from_json(rules)?;
```
**Pros:**
- Non-programmers can define rules
- Easy to version control and share
- Validation and schema support
**Cons:**
- Limited to predefined rule types
- Less flexibility than code-based approaches
- May require custom DSL development
### Option 5: Hybrid Approach
```rust
// Combine multiple approaches for flexibility
model
.add_callback_constraint(|sol| validate_basic_rules(sol))
.add_trait_constraint(Box::new(ComplexWorkloadBalancer::new()))
.load_plugin("industry_specific_rules.so")?
.load_declarative_rules("company_policies.json")?;
```
## Benefits of Custom Functions
### 1. Flexibility and Extensibility
- Handle complex business rules that don't map to standard constraints
- Adapt to industry-specific requirements (healthcare, manufacturing, logistics)
- Support for fuzzy logic and probabilistic constraints
- Custom optimization objectives beyond standard metrics
### 2. Integration Capabilities
- Connect with external systems (databases, APIs, calendars, HR systems)
- Real-time data integration for dynamic scheduling
- Workflow integration and automation
- Legacy system compatibility
### 3. Performance Optimization
- Domain-specific heuristics and search strategies
- Custom pruning and bounding techniques
- Specialized data structures for specific problem types
- Incremental constraint checking
### 4. Domain Expertise Incorporation
- Encode expert knowledge that's hard to formalize
- Handle regulatory and compliance requirements
- Industry best practices and standards
- Cultural and organizational preferences
### 5. Rapid Prototyping and Experimentation
- Quick testing of new constraint ideas
- A/B testing different optimization strategies
- Research and development support
- Educational and learning applications
### 6. Community and Ecosystem Growth
- Community-contributed constraint libraries
- Industry-specific solution packages
- Third-party integrations and extensions
- Knowledge sharing and best practices
## Challenges and Considerations
### 1. Performance Implications
- Custom functions could slow down the solver significantly
- Need efficient integration points and caching strategies
- Memory management and allocation concerns
- Parallel execution considerations
**Mitigation strategies:**
- Lazy evaluation of custom functions
- Caching and memoization
- Performance profiling and optimization hints
- Asynchronous execution where possible
### 2. Safety and Security
- Maintaining Rust's memory safety guarantees
- Preventing crashes from user code
- Security implications of dynamic loading
- Error isolation and recovery
**Mitigation strategies:**
- Sandboxing and isolation mechanisms
- Comprehensive error handling
- Safe API boundaries
- Code signing and verification for plugins
### 3. API Complexity vs. Usability
- Balancing power and flexibility with ease of use
- Learning curve for different skill levels
- Documentation and example requirements
- Backward compatibility considerations
**Mitigation strategies:**
- Layered API design (simple → advanced)
- Comprehensive documentation and tutorials
- Code generation and template tools
- Community examples and patterns
### 4. Debugging and Diagnostics
- Debugging custom constraint logic
- Performance profiling and bottleneck identification
- Error reporting and stack traces
- Testing and validation tools
**Mitigation strategies:**
- Rich debugging APIs and tools
- Constraint visualization capabilities
- Performance monitoring and metrics
- Automated testing frameworks
### 5. Versioning and Compatibility
- API evolution and backward compatibility
- Plugin versioning and dependency management
- Migration tools and strategies
- Long-term support considerations
## Real-World Applications
### Healthcare Staffing
```rust
// Nurse scheduling with patient acuity
fn nurse_patient_ratio_constraint(assignments: &[Assignment]) -> bool {
// ICU: 1:2 ratio, General ward: 1:6 ratio
// Consider nurse experience and certifications
// Handle floating between departments
}
```
### Manufacturing Scheduling
```rust
// Production line staffing
fn production_line_constraint(schedule: &Schedule) -> bool {
// Ensure certified operators for each machine
// Balance experience levels per shift
// Handle equipment maintenance windows
}
```
### Retail Scheduling
```rust
// Store staffing optimization
fn retail_coverage_constraint(schedule: &Schedule) -> bool {
// Peak hour coverage requirements
// Loss prevention staffing
// Customer service language requirements
}
```
### Transportation/Logistics
```rust
// Driver scheduling with regulations
fn driver_hours_constraint(schedule: &Schedule) -> bool {
// DOT hours of service regulations
// Commercial driver license requirements
// Route-specific qualifications
}
```
## Implementation Roadmap
### Phase 1: Basic Callback Support
- Simple function registration API
- Basic validation and objective functions
- Error handling and safety measures
- Documentation and examples
### Phase 2: Trait-based Extensions
- Formal trait definitions for constraints and objectives
- Type-safe custom constraint system
- Performance optimization and caching
- Advanced debugging tools
### Phase 3: Plugin Architecture
- Dynamic plugin loading system
- Security and sandboxing mechanisms
- Plugin registry and distribution
- Community contribution guidelines
### Phase 4: Advanced Features
- Declarative rule definition languages
- Visual constraint editors
- Integration with external systems
- Industry-specific solution packages
## Questions for Further Discussion
1. **API Design**: Which approach (callbacks, traits, plugins) would be most valuable to start with?
2. **Performance**: What are the acceptable performance trade-offs for custom function flexibility?
3. **Safety**: How can we maintain Rust's safety guarantees while allowing custom code execution?
4. **Use Cases**: What are the most important real-world scenarios to support initially?
5. **Integration**: How should custom functions integrate with the existing constraint system?
6. **Testing**: What tools and frameworks are needed to test custom constraints effectively?
7. **Documentation**: What level of documentation and examples would be needed for adoption?
8. **Community**: How can we foster a community around custom constraint development?
## Conclusion
Custom functions would significantly enhance Selen's capabilities and real-world applicability. The key is to design a system that is:
- **Safe**: Maintains Rust's guarantees while allowing extension
- **Performant**: Doesn't significantly impact solver performance
- **Usable**: Accessible to users with varying technical backgrounds
- **Flexible**: Supports a wide range of use cases and integration needs
- **Maintainable**: Sustainable for long-term development and community growth
The implementation should be incremental, starting with simple callback support and evolving toward more sophisticated plugin architectures based on user feedback and real-world requirements.