RL for Production: When Rules-Based Systems Can’t Keep Up

Your production schedule says machine A should start job X at 10am. At 9:45am, machine A breaks down. Now what?

A rule-based system follows its predetermined logic and fails. An RL-based system observes the breakdown, evaluates alternative actions, and adapts the schedule in real-time.

This is why reinforcement learning matters for production: it handles situations your rules didn’t anticipate.

The Core Problem

Production environments are dynamic. Machines break, rush orders arrive, suppliers are late, quality issues emerge. You can’t write rules for every possible scenario.

Traditional approaches:

• Fixed schedules: Created weekly, become obsolete when reality diverges
• Rule-based systems: “If X happens, do Y” — but you need rules for all X
• Human intervention: Operators constantly adjust, but can’t optimize globally

RL offers a different approach: learn a policy that maps states to actions by trying things and observing outcomes. No need to enumerate all scenarios upfront.

RL Basics for Production