Court Scheduling System Implementation Plan.md

Court Scheduling System Implementation Plan

Overview

Build an intelligent judicial scheduling system for Karnataka High Court that optimizes daily cause lists across multiple courtrooms over a 2-year simulation period, balancing fairness, efficiency, and urgency.

Architecture Design

System Components

1. Parameter Loader: Load EDA-extracted parameters (transition probs, durations, capacities)
2. Case Generator: Synthetic case creation with realistic attributes
3. Simulation Engine: SimPy-based discrete event simulation
4. Scheduling Policies: Multiple algorithms (FIFO, Priority, Optimized)
5. Metrics Tracker: Performance evaluation (fairness, efficiency, urgency)
6. Visualization: Dashboard for monitoring and analysis

Technology Stack

• Simulation: SimPy (discrete event simulation)
• Optimization: OR-Tools (CP-SAT solver)
• Data Processing: Polars, Pandas
• Visualization: Plotly, Streamlit
• Testing: Pytest, Hypothesis

Module Structure

scheduler/
├── core/
│   ├── __init__.py
│   ├── case.py              # Case entity and lifecycle
│   ├── courtroom.py         # Courtroom resource
│   ├── judge.py             # Judge entity
│   └── hearing.py           # Hearing event
├── data/
│   ├── __init__.py
│   ├── param_loader.py      # Load EDA parameters
│   ├── case_generator.py   # Generate synthetic cases
│   └── config.py            # Configuration constants
├── simulation/
│   ├── __init__.py
│   ├── engine.py            # SimPy simulation engine
│   ├── scheduler.py         # Base scheduler interface
│   ├── policies/
│   │   ├── __init__.py
│   │   ├── fifo.py         # FIFO scheduling
│   │   ├── priority.py     # Priority-based
│   │   └── optimized.py    # OR-Tools optimization
│   └── events.py           # Event handlers
├── optimization/
│   ├── __init__.py
│   ├── model.py            # OR-Tools model
│   ├── objectives.py       # Multi-objective functions
│   └── constraints.py      # Constraint definitions
├── metrics/
│   ├── __init__.py
│   ├── fairness.py         # Gini coefficient, age variance
│   ├── efficiency.py       # Utilization, throughput
│   └── urgency.py          # Readiness coverage
├── visualization/
│   ├── __init__.py
│   ├── dashboard.py        # Streamlit dashboard
│   └── plots.py            # Plotly visualizations
└── utils/
    ├── __init__.py
    ├── distributions.py    # Probability distributions
    └── calendar.py         # Working days calculator

Implementation Phases

Phase 1: Foundation (Days 1-2) - COMPLETE

Goal: Set up infrastructure and load parameters Status: 100% complete (1,323 lines implemented) Tasks:

1. Create module directory structure (8 sub-packages)
2. Implement parameter loader
   • Read stage_transition_probs.csv
   • Read stage_duration.csv
   • Read court_capacity_global.json
   • Read adjournment_proxies.csv
   • Read cases_features.csv
   • Automatic latest version detection
   • Lazy loading with caching
3. Create core entities (Case, Courtroom, Judge, Hearing)
   • Case: Lifecycle, readiness score, priority score (218 lines)
   • Courtroom: Capacity tracking, scheduling, utilization (228 lines)
   • Judge: Workload tracking, specialization, adjournment rate (167 lines)
   • Hearing: Outcome tracking, rescheduling support (134 lines)
4. Implement working days calculator (192 days/year)
   • Weekend/holiday detection
   • Seasonality factors
   • Working days counting (217 lines)
5. Configuration system with EDA-derived constants (115 lines) Outputs:

• scheduler/data/param_loader.py (244 lines)
• scheduler/data/config.py (115 lines)
• scheduler/core/case.py (218 lines)
• scheduler/core/courtroom.py (228 lines)
• scheduler/core/judge.py (167 lines)
• scheduler/core/hearing.py (134 lines)
• scheduler/utils/calendar.py (217 lines) Quality: Type hints 100%, Docstrings 100%, Integration complete

Phase 2: Case Generation (Days 3-4)

Goal: Generate synthetic case pool for simulation Tasks:

1. Implement case generator using historical distributions
   • Case type distribution (CRP: 20.1%, CA: 20%, etc.)
   • Filing rate (monthly inflow from temporal analysis)
   • Initial stage assignment
2. Generate 2-year case pool (~10,000 cases)
3. Assign readiness scores and attributes Outputs:

• scheduler/data/case_generator.py
• Synthetic case dataset for simulation

Phase 3: Simulation Engine (Days 5-7)

Goal: Build discrete event simulation framework Tasks:

1. Implement SimPy environment setup
2. Create courtroom resources (5 courtrooms)
3. Implement case lifecycle process
   • Stage progression using transition probabilities
   • Duration sampling from distributions
   • Adjournment modeling (stochastic)
4. Implement daily scheduling loop
5. Add case inflow/outflow dynamics Outputs:

• scheduler/simulation/engine.py
• scheduler/simulation/events.py
• Working simulation (baseline)

Phase 4: Scheduling Policies (Days 8-10)

Goal: Implement multiple scheduling algorithms Tasks:

1. Base scheduler interface
2. FIFO scheduler (baseline)
3. Priority-based scheduler
   • Use case age as primary factor
   • Use case type as secondary
4. Readiness-score scheduler
   • Use EDA-computed readiness scores
   • Apply urgency weights
5. Compare policies on metrics Outputs:

• scheduler/simulation/scheduler.py (interface)
• scheduler/simulation/policies/ (implementations)
• Performance comparison report

Phase 5: Optimization Model (Days 11-14)

Goal: Implement OR-Tools-based optimal scheduler Tasks:

1. Define decision variables
   • hearing_slots[case, date, court] ∈ {0,1}
2. Implement constraints
   • Daily capacity per courtroom
   • Case can only be in one court per day
   • Minimum gap between hearings
   • Stage progression requirements
3. Implement objective functions
   • Fairness: Minimize age variance
   • Efficiency: Maximize utilization
   • Urgency: Prioritize ready cases
4. Multi-objective optimization (weighted sum)
5. Solve for 30-day scheduling window (rolling) Outputs:

• scheduler/optimization/model.py
• scheduler/optimization/objectives.py
• scheduler/optimization/constraints.py
• Optimized scheduling policy

Phase 6: Metrics & Validation (Days 15-16)

Goal: Comprehensive performance evaluation Tasks:

1. Implement fairness metrics
   • Gini coefficient of disposal times
   • Age variance within case types
   • Max age tracking
2. Implement efficiency metrics
   • Court utilization rate
   • Average disposal time
   • Throughput (cases/month)
3. Implement urgency metrics
   • Readiness score coverage
   • High-priority case delay
4. Compare all policies
5. Validate against historical data Outputs:

• scheduler/metrics/ (all modules)
• Validation report
• Policy comparison matrix

Phase 7: Dashboard (Days 17-18)

Goal: Interactive visualization and monitoring Tasks:

1. Streamlit dashboard setup
2. Real-time queue visualization
3. Judge workload display
4. Alert system for long-pending cases
5. What-if scenario analysis
6. Export capability (cause lists as PDF/CSV) Outputs:

• scheduler/visualization/dashboard.py
• Interactive web interface
• User documentation

Phase 8: Polish & Documentation (Days 19-20)

Goal: Production-ready system Tasks:

1. Unit tests (pytest)
2. Integration tests
3. Performance benchmarking
4. Comprehensive documentation
5. Example notebooks
6. Deployment guide Outputs:

• Test suite (90%+ coverage)
• Documentation (README, API docs)
• Example usage notebooks
• Final presentation materials

Key Design Decisions

1. Hybrid Approach

Decision: Use simulation for long-term dynamics, optimization for short-term scheduling Rationale: Simulation captures stochastic nature (adjournments, case progression), optimization finds optimal daily schedules within constraints

2. Rolling Optimization Window

Decision: Optimize 30-day windows, re-optimize weekly Rationale: Balance computational cost with scheduling quality, allow for dynamic adjustments

3. Stage-Based Progression Model

Decision: Model cases as finite state machines with probabilistic transitions Rationale: Matches our EDA findings (strong stage patterns), enables realistic progression

4. Multi-Objective Weighting

Decision: Fairness (40%), Efficiency (30%), Urgency (30%) Rationale: Prioritize fairness slightly, balance with practical concerns

5. Capacity Model

Decision: Use median capacity (151 cases/court/day) with seasonal adjustment Rationale: Conservative estimate from EDA, account for vacation periods

Parameter Utilization from EDA

EDA Output	Scheduler Use
stage_transition_probs.csv	Case progression probabilities
stage_duration.csv	Duration sampling (median, p90)
court_capacity_global.json	Daily capacity constraints
adjournment_proxies.csv	Hearing outcome probabilities
cases_features.csv	Initial readiness scores
case_type_summary.csv	Case type distributions
monthly_hearings.csv	Seasonal adjustment factors
correlations_spearman.csv	Feature importance weights

Assumptions Made Explicit

Court Operations

1. Working days: 192 days/year (from Karnataka HC calendar)
2. Courtrooms: 5 courtrooms, each with 1 judge
3. Daily capacity: 151 hearings/court/day (median from EDA)
4. Hearing duration: Not modeled explicitly (capacity is count-based)
5. Case queue assignment: By case type (RSA → Court 1, CRP → Court 2, etc.)

Case Dynamics

1. Filing rate: ~6,000 cases/year (derived from historical data)
2. Disposal rate: Matches filing rate (steady-state assumption)
3. Stage progression: Probabilistic (Markov chain from EDA)
4. Adjournment rate: 36-48% depending on stage and case type
5. Case readiness: Computed from hearings, gaps, and stage

Scheduling Constraints

1. Minimum gap: 7 days between hearings for same case
2. Maximum gap: 90 days (alert triggered)
3. Urgent cases: 5% of pool marked urgent (jump queue)
4. Judge preferences: Not modeled (future enhancement)
5. Multi-judge benches: Not modeled (all single-judge)

Simplifications

1. No lawyer availability: Assumed all advocates always available
2. No case dependencies: Each case independent
3. No physical constraints: Assume sufficient courtrooms/facilities
4. Deterministic durations: Within-hearing time not modeled
5. Perfect information: All case attributes known

Success Criteria

Fairness Metrics

• Gini coefficient < 0.4 (disposal time inequality)
• Age variance reduction: 20% vs FIFO baseline
• No case unlisted > 90 days without alert

Efficiency Metrics

• Court utilization > 85%
• Average disposal time: Within 10% of historical median by case type
• Throughput: Match or exceed filing rate

Urgency Metrics

• High-readiness cases: 80% scheduled within 14 days
• Urgent cases: 95% scheduled within 7 days
• Alert response: 100% of flagged cases reviewed

Risk Mitigation

Technical Risks

1. Optimization solver timeout: Use heuristics as fallback
2. Memory constraints: Batch processing for large case pools
3. Stochastic variability: Run multiple simulation replications

Model Risks

1. Parameter drift: Allow manual parameter overrides
2. Edge cases: Implement rule-based fallbacks
3. Unexpected patterns: Continuous monitoring and adjustment

Future Enhancements

Short-term

1. Judge preference modeling
2. Multi-judge bench support
3. Case dependency tracking
4. Lawyer availability constraints

Medium-term

1. Machine learning for duration prediction
2. Automated parameter updates from live data
3. Real-time integration with eCourts
4. Mobile app for judges

Long-term

1. Multi-court coordination (district + high court)
2. Predictive analytics for case outcomes
3. Resource optimization (judges, courtrooms)
4. National deployment framework

Deliverables Checklist

• Scheduler module (fully functional)
• Parameter loader (tested with EDA outputs)
• Case generator (realistic synthetic data)
• Simulation engine (2-year simulation capability)
• Multiple scheduling policies (FIFO, Priority, Optimized)
• Optimization model (OR-Tools implementation)
• Metrics framework (fairness, efficiency, urgency)
• Dashboard (Streamlit web interface)
• Validation report (comparison vs historical data)
• Documentation (comprehensive)
• Test suite (90%+ coverage)
• Example notebooks (usage demonstrations)
• Presentation materials (slides, demo video)

Timeline Summary

Phase	Days	Key Deliverable
Foundation	1-2	Parameter loader, core entities
Case Generation	3-4	Synthetic case dataset
Simulation	5-7	Working SimPy simulation
Policies	8-10	Multiple scheduling algorithms
Optimization	11-14	OR-Tools optimal scheduler
Metrics	15-16	Validation and comparison
Dashboard	17-18	Interactive visualization
Polish	19-20	Tests, docs, deployment
Total: 20 days (aggressive timeline, assumes full-time focus)

Next Immediate Actions

1. Create scheduler module directory structure
2. Implement parameter loader (read all EDA CSVs/JSONs)
3. Define core entities (Case, Courtroom, Judge, Hearing)
4. Set up development environment with uv
5. Initialize git repository with proper .gitignore
6. Create initial unit tests

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Plan Version: 1.0 Created: 2025-11-19 Status: Ready to begin implementation