This article is a preliminary analysis of the existing scientific literature on the use of artificial intelligence algorithms and optimisation techniques for production scheduling and sequencing in Industry 4.0 and 5.0 smart manufacturing environments. Ninety-one relevant articles are identified that address issues related to production planning and job scheduling in smart factories. Approaches like reinforcement learning, genetic algorithms and hybrid systems to improve efficiency, flexibility and sustainability in manufacturing processes are highlighted. It also discusses differences in Industry 4.0 and 5.0 issues and current challenges, and suggests future research areas to optimise scheduling and sequencing in these advanced manufacturing environments.