Applications of XAI to Job Sequencing and Scheduling in Manufacturing

Abstract

This chapter discusses a new application field of XAI in manufacturing—job sequencing and scheduling. It first breaks down job sequencing and scheduling into several steps and then mentions AI technologies applicable to some of these steps. It is worth noting that many AI applications focus on the preparation of inputs required for scheduling tasks, rather than the process of scheduling tasks, which is a distinctive feature of the field. Nonetheless, many AI techniques have already been explained in other fields or domains. These explanations can provide a reference for explaining the application of AI in job sequencing and scheduling. Therefore, some general XAI techniques and tools for job sequencing and scheduling are reviewed, including: referring to the classification of job scheduling problems; customizing scheduling rules; text description, pseudocode; decision trees, flowcharts. Furthermore, job sequencing and scheduling problems are often formulated as mathematical programming (optimization) models to be optimized. AI technologies can be applied to find the best solution for the model. Applications of genetic algorithm (GA) are of particular interest because such applications are most common in job scheduling. Furthermore, XAI techniques and tools for explaining GA can be easily extended to other evolutionary AI applications, such as artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) in job scheduling. Applicable XAI techniques and tools include flowcharts, text description, chromosome maps, dynamic line charts, and bar charts with baseline. Some novel XAI techniques and tools for interpreting GAs are also introduced: decision tree-based interpretation and dynamic transition and contribution diagrams.