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

A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

© 2018 Massachusetts Institute of Technology. Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes