This chapter begins by defining the fundamental concepts of production planning and job scheduling in supply chains. It then reviews key case studies from relevant literature, summarizing the issues that need to be addressed in supply chain production planning and job scheduling, as well as the methodologies proposed by practitioners and researchers. Through this review, the chapter emphasizes the difficulties in supply chain production planning and scheduling. To overcome these difficulties, various artificial intelligence (AI) technologies have been applied, particularly bio-inspired algorithms such as genetic algorithms (GAs), ant colony optimization (ACO), and artificial bee colony (ABC). The application of these bio-inspired algorithms helps solve optimization problems in production planning or job scheduling for supply chains. However, some AI applications are difficult to understand, communicate, or trust, thus requiring the use of explainable artificial intelligence (XAI) techniques and tools to explain the reasoning processes and results of these AI applications. This chapter introduces the applications of various XAI techniques and tools in production planning and job scheduling for supply chains, such as twin color-coded chromosome maps, contrastive gradient saliency maps, Shapley value (SHAP) analysis, locally interpretable model-agnostic interpretation (LIME), and color-coded pheromone distribution map.