Operations Research Algorithm Developer

Operations Research Algorithm Developer

When to Use

• Frame a decision problem as an optimization model—objectives, decisions, constraints, parameters, uncertainty
• Build LP, MIP, QP, or constraint programming formulations for planning and allocation
• Model network flows, assignment, routing (VRP), scheduling, and resource allocation
• Design inventory and production planning models (lot sizing, capacity, multi-period)
• Select exact vs heuristic methods—branch-and-bound, column generation, decomposition, metaheuristics
• Run sensitivity analysis, infeasibility diagnosis, and benchmarking (optimality gap, runtime)
• Integrate solvers conceptually—OR-Tools, Gurobi, CPLEX, HiGHS, PuLP, Pyomo—and production patterns
• Prepare input data, validate units, and enforce constraint modeling discipline
• Productionize OR services—APIs, timeouts, warm starts, incremental solves, solution pools

When NOT to Use

• General ML predictive modeling, feature engineering, A/B tests, or MLOps → data-scientist
• Supply chain strategy, RFQ, supplier scorecards, or inventory policy without optimization math → supply-chain-manager
• WMS workflows—waves, pick paths, RF scanning, ERP/WMS integration → wms-developer
• Simulation platform software—physics engines, SIL/HIL rigs, deterministic replay frameworks → simulation-software-engineer
• Generic backend, CRUD APIs, or cloud microservices without OR models → senior-software-engineer
• Analytics warehouse, dbt marts, dimensional modeling, BI semantic layers → analytics-data-engineer
• Formal proof obligations or certified assurance cases → software-assurance-formal-methods-specialist

Related skills

Core Workflows

1. Scope and problem class

Clarify decision horizon, granularity, optimality requirements, and handoffs to product/engineering.

See references/or_algorithm_developer_scope.md.

2. Formulation and data

Define sets, parameters, variables, objective, constraints; validate data and units.

See references/problem_formulation_and_data.md.

3. Linear and integer optimization

LP/MIP/QP structure, big-M discipline, tightening, decomposition hooks.

See references/linear_and_integer_optimization.md.

4. Routing, scheduling, and networks

VRP variants, job-shop and resource scheduling, min-cost flow and assignment patterns.

See references/routing_scheduling_and_networks.md.

5. Heuristics and metaheuristics

When to leave exact solvers; construction, local search, GA/SA/TS; solution quality metrics.

See references/heuristics_and_metaheuristics.md.

6. Solver integration and production

Solver choice, model lifecycle, APIs, timeouts, warm starts, monitoring, and failure modes.

See references/solver_integration_and_production.md.

Outputs

• Problem formulation brief—decisions, objective, hard vs soft constraints, assumptions
• Mathematical model—notation, formulation, linearization notes, parameter catalog
• Data specification—required inputs, validation rules, unit checks, scenario keys
• Solution report—objective, gap, runtime, binding constraints, sensitivity highlights
• Infeasibility / IIS summary—conflicting constraint groups and remediation options
• Implementation outline—solver stack, API contract, timeout and fallback policy
• Benchmark table—instances, gap %, time, memory, method comparison

Principles

• Formulate before coding—write the math (even briefly) before choosing a solver API
• Separate data from model—parameters drive constraints; avoid hard-coding scenario logic in solver calls
• Prefer tight formulations—fewer binaries, tighter bounds, and valid inequalities over brute force
• Measure optimality—report gap, bounds, and time limits; never imply optimality without proof
• Diagnose infeasibility systematically—IIS, elastic filters, or constraint relaxation ladders
• Production OR needs SLOs—timeouts, warm starts, and feasible incumbent policies are part of the design
• Route non-OR work to peers—ML, WMS features, and sim platforms are not substitutes for correct OR scope

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