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hajibabaie/README.md
Mohammad Hajibabaie — Operations Research · Combinatorial Optimization · Python

PhD candidate at TU Clausthal, finishing a dissertation on the Vehicle Platooning Problem: coordinating truck routes and schedules so vehicles form fuel-saving convoys on real road networks. I work on both sides of the methodology — exact optimization (MILP with Gurobi) and metaheuristics — and benchmark them against each other at scale. Seven years of university teaching and thesis supervision alongside the research.

What I care about: clean mathematical models, reproducible experiments, and code that separates the problem from the algorithm.

Methods

  • Exact optimization — MILP modeling, Benders decomposition, two-stage stochastic programming (SAA), chance constraints · Gurobi, CPLEX, HiGHS, SCIP
  • Metaheuristics — GA, SA, PSO, ACO, CMA-ES; multi-objective: NSGA-II, MOPSO, SPEA2, PESA-II, MOEA/D — built as reusable, problem-agnostic engines
  • ML × OR — deep learning foundations (DeepLearning.AI specialization); currently building LLM-assisted optimization workflows

Selected repositories

Repository Summary
multi-objective-vehicle-platooning-problem Joint route and speed selection for truck platooning — six multi-objective metaheuristics benchmarked against the exact Pareto front of a MILP solved with Gurobi. The core problem of my PhD.
multi-objective-scheduling Energy-aware tri-objective flow-shop & job-shop scheduling (makespan, tardiness, energy) on the Taillard benchmarks — NSGA-II, MOEA/D, and MOPSO from scratch with adaptive operator selection, Dockerized PostgreSQL experiment tracking, and a full nonparametric statistical comparison.
stochastic-facility-location Two-stage stochastic capacitated facility location with SAA, a service-level chance constraint, and Benders decomposition. Solver-agnostic backends (HiGHS / SCIP / Gurobi).
multi-objective-optimization NSGA-II, MOPSO, SPEA2, PESA-II, and MOEA/D implemented from scratch and compared on benchmark problems.
genetic-algorithm Problem-agnostic GA engine with pluggable encodings (binary, real, permutation, mixed), applied to classic OR problems.
ant-colony-optimization ACO for discrete and continuous optimization on a shared metaheuristic core.

Stack

Python Gurobi CPLEX HiGHS SCIP NumPy pandas PostgreSQL Docker TensorFlow Neo4j Git Linux

Contact

LinkedIn · Germany · Open to OR Scientist / Applied Scientist roles

Pinned Loading

  1. multi-objective-vehicle-platooning-problem multi-objective-vehicle-platooning-problem Public

    Multi-objective optimization vehicle platooning: route and speed selection on a road network, trading off fuel cost against travel time.

    Python

  2. multi-objective-optimization multi-objective-optimization Public

    Five multi-objective evolutionary algorithms (NSGA-II, MOPSO, SPEA2, PESA-II, MOEA/D) on the MOP2 benchmark problem.

    Python 5 4

  3. stochastic-facility-location stochastic-facility-location Public

    Two-stage stochastic capacitated facility location with SAA, a service-level chance constraint, and Benders decomposition (HiGHS/SCIP/Gurobi backends).

    Python

  4. ant-colony-optimization ant-colony-optimization Public

    Ant Colony Optimization applied to discrete and continuous optimization problems, built on a shared metaheuristic engine.

    Python

  5. genetic-algorithm genetic-algorithm Public

    Genetic algorithm framework for combinatorial and continuous optimization. Problem-agnostic engine with pluggable encodings (binary, real, permutation, mixed).

    Python