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
| Need | Skill |
|---|---|
| ML prediction, experimentation, MLOps | data-scientist |
| SCM sourcing, forecast process, supplier QBRs | supply-chain-manager |
| Warehouse management application logic | wms-developer |
| DES/physics sim platforms, digital twins | simulation-software-engineer |
| Enterprise application and API engineering | senior-software-engineer |
| dbt, warehouse modeling, BI pipelines | analytics-data-engineer |
| Executive dashboards and KPI storytelling | bi-analyst |
| Service SLOs and production incident response | site-reliability-engineer |
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
When to load references
| Topic | Reference |
|---|---|
| Role scope, boundaries, RACI | references/or_algorithm_developer_scope.md |
| Sets, parameters, validation | references/problem_formulation_and_data.md |
| LP, MIP, QP, tightening | references/linear_and_integer_optimization.md |
| VRP, scheduling, networks | references/routing_scheduling_and_networks.md |
| Heuristics, metaheuristics | references/heuristics_and_metaheuristics.md |
| Solvers, APIs, production | references/solver_integration_and_production.md |