Design of a Reference Architecture for Production Scheduling Applications based on a Problem Representation including Practical Constraints

Changing customer demands increase the complexity and importance of production scheduling, requiring better scheduling algorithms, e.g., machine learning algorithms. At the same time, current research often neglects practical constraints, e.g., changeovers or transportation. To address this issue, we derive a representation of the scheduling problem and develop a reference architecture for future scheduling applications to increase the impact of future research. To achieve this goal, we apply a design science research approach and, first, rigorously identify the problem and derive requirements for a scheduling application based on a structured literature review. Then, we develop the problem representation and reference architecture as design science artifacts. Finally, we demonstrate the artifacts in an application scenario and publish the resulting prototypical scheduling application, enabling machine learning-based scheduling algorithms, for usage in future development projects. Our results guide future research into including practical constraints and provide practitioners with a framework for developing scheduling applications

Multi-agent Co-evolutionary Scheduling Approach based on Genetic Reinforcement Learning

The paper presents an adaptive iterative distributed scheduling algorithm that operates dynamically to schedule the job in the dynamic job-shop. The manufacturing system is scheduled by the multi-agent system where every machine and job is associated with its own software agent. Each agent learns how to select presumably good schedules, by this way the size of the search space can be reduced. In order to get adaptive behavior, genetic algorithm is incorporated to drive parallel search and the evolution direction. Meanwhile, the reinforcement learning system is done with the phased Q-learning by defining the intermediate state pattern. The paper suggests a cooperation technique for the agents, as well. We also analyze the time and the solution and present some experimental results