mip-modeling-expert

Name: mip-modeling-expert
Availability: InStock
Author: sverzijl

Formulate mixed-integer programming models with indicator variables and Big-M constraints.

1|Updated Oct 1, 2025

Search Tags:#optimization #MIP #linearization #indicator-variables #Big-M #SOS #piecewise-linear

Ready to run

npx skills add https://github.com/sverzijl/planning_latest --skill mip-modeling-expert

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mip-modeling-expert
Source: https://github.com/sverzijl/planning_latest/tree/main/.claude/skills/mip-modeling-expert
Command: npx skills add https://github.com/sverzijl/planning_latest --skill mip-modeling-expert

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The MIP Modeling Expert skill provides practical, field-tested techniques to transform complex optimization problems into solvable mixed-integer programs. It focuses on handling discontinuous variables, logical constraints, and nonlinear terms via linearization and modeling tricks.

Core Features & Use Cases

Indicator variables and discontinous variable modeling to enforce zero-or-bounded ranges.
Big-M, SOS1/SOS2, and logical constraints to encode either-or and conditional relationships.
Piecewise linear approximations and product linearization for nonlinear terms, enabling exact or tightly bounded MILP solutions.
Guidance examples across production, logistics, facility location, and budget problems with actionable formulations for Gurobi/CPLEX.

Quick Start

Use the mip-modeling-expert skill to generate a minimal MIP formulation for a binary-discontinuous variable example, including a small dataset and complete constraints, then derive a tight Big-M bound.

Frequently Asked Questions about mip-modeling-expert

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I formulate logical constraints in mixed-integer programming?▼

Piecewise linear approximations model nonlinear terms in mixed-integer programming by transforming product linearization and discontinuous variable relationships into tightly bounded linear constraints.

How do I derive tight bounds for a Big-M formulation?▼

Deriving tight Big-M bounds requires analyzing the maximum and minimum feasible values of your discontinuous variables to ensure solver efficiency and prevent unbounded or slow branch-and-bound trees.

Can I use indicator variables to enforce zero-or-bounded ranges in Gurobi or CPLEX?▼

Yes, indicator variables can enforce zero-or-bounded ranges in major MILP solvers like Gurobi and CPLEX by triggering variable bounds conditionally based on a binary activation state.

What's the best way to model discontinuous variables in production scheduling problems?▼

The best way to model discontinuous variables in production scheduling is applying SOS1 or SOS2 constraints alongside piecewise linear approximations to handle complex operational either-or decisions.

When should I use SOS constraints instead of Big-M in a facility location model?▼

Use SOS constraints instead of Big-M in facility location models when you need to strictly enforce mutually exclusive choices without relying on large constants that can degrade solver numerical stability.