This document presents a distributed approach for solving partially flexible job-shop scheduling problems using cyber-physical products (CPPs) with Q-learning. CPPs use reinforcement learning to schedule jobs on heterogeneous machines with partial flexibility. The approach models a cyber-physical production system with intelligent CPPs that learn to schedule jobs in a distributed manner. Simulation results show the CPPs' scheduling performance improves over time as they learn through the Q-learning process.