Production systems are moving towards new levels of smart manufacturing, which means that production processes become more autonomous, sustainable, and agile. Additionally, the increasing complexity and variety of individualized products makes manufacturing a challenge, since it must be produced by consuming the least number of resources, generating profitability. In mathematical perspective, most production planning problems, such as real-world scheduling and sequencing problems, are classified as NP-Hard problems, and there is most likely no polynomial-time algorithm for these kinds of problems. In addition, advances in information and communication technologies (ICT) are increasing, leading to an already existing trend towards real-time scheduling. In this context, the aim of this research is to develop matheuristic algorithms for the optimization of production planning in the supply chain. The development of matheuristic algorithms allows finding efficient solutions, achieving shorter computational times, providing companies with smart manufacturing skills to quickly respond to the market needs.