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operations-research-algorithm-developer

/operations-research-algorithm-developer

Formulate and implement operations research optimization models—LP, MIP/QP, constraint programming, network flows, assignment, VRP, scheduling, resource allocation, inventory/production planning; heuristics and metaheuristics; sensitivity and infeasibility diagnosis; solver integration (OR-Tools, Gurobi, CPLEX, HiGHS, PuLP, Pyomo); production OR APIs (timeouts, warm starts). Use for operations research, OR engineer, optimization model, linear programming, mixed integer programming, MIP, VRP, vehicle routing, scheduling optimization, OR-Tools, Gurobi, constraint programming, resource allocation

Licence
MIT
Last repository change
20.05.2026

Assessment and security

90/ 100Class: good
Format25 / 25
Frontmatter with name and description, valid name, description up to 1,024 characters, name matching the folder.
Discoverability25 / 25
Description explains when to use the skill, has a reasonable length and uses third person.
Content25 / 35
At least 150 words and at most 500 lines, headings, step lists, examples or code.
Hygiene15 / 15
No placeholders such as TODO, no Windows paths, with a licence.
Riskno signals detected in the scan
Files in this package
7
Scripts
0
HTML comments
0
URLs in SKILL.md
0
Deprecation

No signals detected. Repository changed on 20.05.2026, without references to retired models or APIs.

Licence

MIT, open: use and modify while retaining author attribution.

What it does and when to use it

Formulate and implement operations research optimization models—LP, MIP/QP, constraint programming, network flows, assignment, VRP, scheduling, resource allocation, inventory/production planning; heuristics and metaheuristics; sensitivity and infeasibility diagnosis; solver integration (OR-Tools, Gurobi, CPLEX, HiGHS, PuLP, Pyomo); production OR APIs (timeouts, warm starts). Use for operations research, OR engineer, optimization model, linear programming, mixed integer programming, MIP, VRP, vehicle routing, scheduling optimization, OR-Tools, Gurobi, constraint programming, resource allocation

Original author description

Formulate and implement operations research optimization models—LP, MIP/QP, constraint programming, network flows, assignment, VRP, scheduling, resource allocation, inventory/production planning; heuristics and metaheuristics; sensitivity and infeasibility diagnosis; solver integration (OR-Tools, Gurobi, CPLEX, HiGHS, PuLP, Pyomo); production OR APIs (timeouts, warm starts). Use for operations research, OR engineer, optimization model, linear programming, mixed integer programming, MIP, VRP, vehicle routing, scheduling optimization, OR-Tools, Gurobi, constraint programming, resource allocation

How it works

  1. When to Use
  2. When NOT to Use
  3. Related skills
  4. Core Workflows
  5. Outputs
  6. Principles
  7. When to load references
Original SKILL.md headings · 107 lines, 615 words
SKILL.md preview
---
name: operations-research-algorithm-developer
description: |
  Formulate and implement operations research optimization models—LP, MIP/QP, constraint programming,
  network flows, assignment, VRP, scheduling, resource allocation, inventory/production planning;
  heuristics and metaheuristics; sensitivity and infeasibility diagnosis; solver integration
  (OR-Tools, Gurobi, CPLEX, HiGHS, PuLP, Pyomo); production OR APIs (timeouts, warm starts).
  Use for operations research, OR engineer, optimization model, linear programming, mixed integer
  programming, MIP, VRP, vehicle routing, scheduling optimization, OR-Tools, Gurobi, constraint
  programming, resource allocation optimization, infeasible model, metaheuristic—not ML prediction
  (data-scientist), SCM strategy without optimization math (supply-chain-manager), WMS features
  (wms-developer), simulation platforms (simulation-software-engineer), generic backend
  (senior-software-engineer), dbt/warehouse (analytics-data-engineer).
---

# 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

| 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` |
Files in this package (7)
  • SKILL.md 6 KB
  • references/heuristics_and_metaheuristics.md 3 KB
  • references/linear_and_integer_optimization.md 3 KB
  • references/or_algorithm_developer_scope.md 4 KB
  • references/problem_formulation_and_data.md 4 KB
  • references/routing_scheduling_and_networks.md 3 KB
  • references/solver_integration_and_production.md 4 KB

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