Ant Colony Optimization · Job Shop Scheduling

ACO-JSSP visualizer

Pick the size of the job-shop instance, tune the ACO parameters, and inspect the resulting Gantt chart. Small instances also get an exact browser comparison so you can see the ACO gap.

Build and solve a job shop

First define the scheduling problem. Then choose how aggressively the ant colony searches.

Problem parameters

Instance

These define the job-shop instance: how many jobs, how many machines, and how long operations take.

Jobs Machines Min duration Max duration Instance seed

Algorithm parameters

ACO

These control the heuristic search effort. The exact comparison is intentionally capped so the page stays responsive.

Ants Iterations Local search Algorithm seed Exact limit ms

Python report uses HiGHS MILP. The browser uses an exact branch-and-bound comparison for small instances because SciPy/HiGHS does not run inside this static page.

Solver comparison

—ACO time

—Exact time

—Exact makespan

—ACO gap

—ACO makespan

—Operations

—Exact status

—Exact nodes