MiniZinc

MiniZinc is a high-level constraint modeling language for discrete optimization: you declare variables, constraints, and an objective, and MiniZinc translates the model to a solver — choosing from CP, SAT, or MIP engines — without changing a line of the model.

Purpose

MiniZinc separates modeling from solving: one model, many solver technologies, all through the solver-independent FLATZINC intermediate format.

The Problem It Solves

Constraint problems (rostering, routing, packing) used to mean re-coding for each solver’s API. MiniZinc gives a readable, typed language with arrays, sets, and a library of global constraints (alldifferent, cumulative, table…), compiles it to FLATZINC, and lets the IDE or CLI pick Gecode, Chuffed, CP-SAT (OR-Tools), or a MIP solver per run. Analysts model; solvers optimize.

Where It Fits

MiniZinc is the constraint-optimization member of this phase’s logic family: Prolog/Datalog for logic reasoning, Clingo for answer-set search, MiniZinc for constraint satisfaction and optimization with solver independence.

History

MiniZinc came from the Australian constraint-programming community and became the field’s modeling lingua franca.

Origins

MiniZinc was created around 2007 at NICTA (later Data61/CSIRO, Australia) by Peter Stuckey and colleagues. Version 2.0 (2014) redesigned the language and the FLATZINC pipeline that all solvers consume.

Milestones

  • 2008+ — the annual MiniZinc Challenge benchmarks solvers through the same models.
  • 2014–2016 — MiniZinc 2.x: cleaner semantics, better typing, and the modern solver ecosystem.
  • Today — developed at Monash University with OPTIMA support; 2.10.x releases; Python/JS embedding and a browser playground.

Current Status

Mature, actively maintained, and open source (MPL-2.0): the standard way academia and industry teach and practice constraint modeling.

Stage

MiniZinc is battle-tested in research and steadily adopted in industry.

Maturity

Fully mature: a stable language, an annual solver challenge, and a large global-constraint library. Version 2.10.x is current.

Governance & Maintenance

Developed at Monash University with Australian Research Council (OPTIMA) support plus open-source contributors; MPL-2.0 license; binaries for all platforms plus a browser playground.

Popularity & Usability

The default language of constraint-programming education and a strong industry modeling tool.

Adoption

Standard in OR/CP courses and research; used for rostering, logistics, and scheduling products; embedded via Python (minizinc package) and JavaScript.

Learning Curve

Moderate for people comfortable with math: var int, array, and constraint read like equations. The IDE, with interactive search visualization, and the Playground smooth the entry.

Tooling

MiniZinc IDE, CLI (minizinc), bundled solvers (Gecode, Chuffed, Coin-BC, plus CP-SAT via OR-Tools and HiGHS on request), Python/JS APIs, and the online Playground.

Use Cases

Any discrete optimization problem that can be stated as variables plus constraints.

Primary Domains

  • Workforce rostering and timetabling.
  • Vehicle routing, packing, and assignment problems.
  • Production planning and scheduling with resource limits.
  • Teaching operations research and constraint programming.

Strengths

Solver independence (one model, many engines), global constraints that encode domain wisdom, type safety, and a professional IDE with search visualization.

Weak Spots

Modeling is not free: poor models produce slow solves; FLATZINC adds an indirection layer; highly specialized problems may still need a solver-specific encoding.

Performance

MiniZinc’s performance is deliberately the solver’s, not the language’s.

Execution Model

The compiler flattens the model to FLATZINC for the chosen engine: Gecode (CP), Chuffed (lazy clause generation), CP-SAT (SAT-based), or HiGHS (MIP). Global constraints pass through, letting each solver apply its best propagation.

Published Claims

The MiniZinc Challenge’s yearly results are the honest data point: solver choice changes runtimes by orders of magnitude on identical models, with CP-SAT dominating many scheduling benchmarks. The language itself adds negligible overhead — model quality decides everything.

Example

A 0/1 knapsack: choose items to maximize value under a weight limit.

The Model

% knapsack.mzn — maximize value under a weight capacity
int: capacity = 11;                          % knapsack limit
array[1..4] of int: w = [2, 3, 4, 5];        % item weights
array[1..4] of int: v = [3, 4, 5, 6];        % item values

array[1..4] of var 0..1: take;               % 1 if item i is taken

constraint sum(i in 1..4)(w[i] * take[i]) <= capacity;

solve maximize sum(i in 1..4)(v[i] * take[i]);
output [show(take)];

var 0..1 declares a decision variable; the sum(...) syntax builds an expression over an index set. Changing maximize to satisfy turns optimization into search.

How to Run

# Install MiniZinc (minizinc.org), then:
minizinc knapsack.mzn            # uses the default bundled Gecode solver
# output: [1, 1, 1, 0]  (items 1-3, total value 12, weight 9)

Learn More

Official sources and free materials; the full categorized catalog is on the References & Downloads page.

Official Docs & Downloads

Learning Material