# 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
model.add_custom_constraint(|solution| {
    // User-defined validation logic
    validate_business_rules(solution)
});

model.set_objective_function(|solution| {
    // Custom cost calculation
    calculate_total_cost(solution)
});

// With context and parameters
model.add_constraint_with_context(employee_data.clone(), |context, solution| {
    validate_with_employee_data(context, solution)
});
```

**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.