Many bio-inspired algorithms, such as artificial neural networks (ANN), genetic algorithms (GA), artificial bee colonies (ABC), ant colony optimization (ACO), particle swarm optimization (PSO), and others, have shown their effectiveness in job sequencing and scheduling. However, these bio-inspired algorithms have long been considered black boxes, hindering the credibility and reliability of their applications in job sequencing and scheduling. Therefore, this chapter takes ACO applications in sequencing and scheduling as an example. Such applications have not yet been explained using explainable artificial intelligence (XAI) techniques, and recent successes in explaining the application of GAs in job scheduling can be replicated. First, past research on the application of ACOs in job sequencing and scheduling is reviewed. Motivations for applying XAI techniques and tools are then highlighted. Subsequently, emerging XAI techniques and tools for explaining ACO applications in job sequencing and scheduling are introduced, including a novel decision tree-based XAI approach. The examples reveal that most ants end up following similar paths. In addition, the contrastive gradient salient map precisely locates the convergence point of the ACO evolution process and intuitively displays it to the scheduler. Furthermore, the decision tree-based XAI approach fills the gap in explaining the intrinsic mechanisms of black-box job scheduling methods such as ACO.