Role Overview
We are seeking a highly skilled Operations Research (OR) and Optimization Engineer to design and develop an intelligent crew rostering optimizer for rail operations. The role involves applying advanced optimization techniques—including metaheuristics and mathematical programming—to generate efficient, compliant, and cost-optimized crew schedules. The candidate will also develop production-grade Python code for the optimizer and expose it via APIs for integration with operational systems.
This role combines algorithm design, applied optimization, and production software development.
Key Responsibilities
Optimization Model Design & Development
- Design and implement optimization models for rail crew rostering, considering operational, regulatory, and contractual constraints.
- Apply appropriate optimization techniques such as:
- Mixed Integer Linear Programming (MILP)
- Constraint Programming (CP)
- Metaheuristics (Genetic Algorithm, Simulated Annealing, Tabu Search, Large Neighborhood Search, etc.)
- Hybrid OR + metaheuristic approaches for large-scale optimization
- Model constraints including:
- Duty time limits, rest rules, and labor regulations
- Crew qualifications, route familiarity, and skill requirements
- Shift continuity, fairness, and crew preferences
- Coverage requirements and operational robustness
- Develop objective functions to optimize for cost, utilization, fairness, robustness, and operational efficiency.
Algorithm Engineering & Performance Optimization
- Design scalable and efficient optimization algorithms capable of handling real-world rail network scale.
- Implement solution heuristics and decomposition strategies to improve performance.
- Perform benchmarking, tuning, and performance optimization.
Python Development & API Integration
- Develop modular, production-quality Python code for the optimization engine.
- Expose optimizer functionality via REST APIs (using FastAPI, Flask, or similar frameworks).
- Ensure clean architecture, modular design, and extensibility.
- Package and deploy optimization services for integration with enterprise systems.
Integration & Deployment
- Integrate optimizer with upstream data systems and downstream applications.
- Support deployment in cloud or on-prem environments.
- Ensure robustness, logging, monitoring, and error handling.
Validation & Continuous Improvement
- Validate optimization outputs with domain stakeholders.
- Improve algorithms based on real-world operational feedback.
- Document models, assumptions, and system architecture.
Required Qualifications
Education
- Master's or PhD in one of the following:
- Operations Research
- Industrial Engineering
- Computer Science (with optimization focus)
- Applied Mathematics
- Transportation Engineering
- Artificial Intelligence (with optimization focus)
Technical Skills
Optimization & Algorithms
Strong hands-on experience with:
- Operations Research and optimization modeling
- Metaheuristics such as:
- Genetic Algorithms
- Simulated Annealing
- Tabu Search
- Large Neighborhood Search
- Variable Neighborhood Search
- Mathematical programming techniques:
Experience with optimization tools such as:
- OR-Tools
- Pyomo
- PuLP
- Gurobi / CPLEX / CBC
- OptaPlanner (optional)
