TY - CHAP
T1 - Applications of XAI to Job Sequencing and Scheduling in Manufacturing
AU - Chen, Tin Chih Toly
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Decision tree-based interpretation
KW - Dynamic transition and contribution diagrams
KW - GA
KW - Job sequencing and scheduling
KW - XAI
UR - http://www.scopus.com/inward/record.url?scp=85151293979&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-27961-4_4
DO - 10.1007/978-3-031-27961-4_4
M3 - Chapter
AN - SCOPUS:85151293979
T3 - SpringerBriefs in Applied Sciences and Technology
SP - 83
EP - 105
BT - SpringerBriefs in Applied Sciences and Technology
PB - Springer Science and Business Media Deutschland GmbH
ER -