This chapter introduces the applications of explainable artificial intelligence (XAI) techniques and tools in job sequencing and scheduling. First, such XAI applications correspond to various steps of the job scheduling process. Therefore, XAI applications for collecting and estimating the data required for job sequencing and scheduling are mentioned first. Applicable XAI techniques and tools include visualization XAI techniques and tools, XAI techniques for evaluating the importance of each input to the output, and XAI techniques for approximating the estimation mechanism. Subsequently, an introduction to XAI applications used to explain the scheduling mechanism is given. For this purpose, generic XAI techniques and tools, Shapley value (SHAP) analysis, decision tree-based methods, local interpretable model-agnostic explanations (LIME), and explainable optimization are suitable. Finally, criteria for evaluating the effectiveness of XAI applications in job sequencing and scheduling are provided.