# Smart Manufacturing Scheduling Approaches--Systematic Review and Future Directions
## 1 Introduction  
Manufacturing has suffered deep changes over the past decades, mainly driven by the market trends that forced companies to move from traditional mass production lines to more dynamic and flexible manufacturing systems. The increasing demand for highly customized products with several variants led to smaller lot sizes, which requires companies to quickly adapt and adjust to new market opportunities in order to thrive in a very competitive world. Therefore, it is crucial that manufacturers develop approaches that allow for more dynamism, flexibility, and reconfigurability at the factory level. The life cycles of product are becoming smaller and smaller, which leads to companies not adapting their production lines in time for new market opportunities, which takes time and is costly. One of the biggest challenges of humankind has always been to maximize productive work in an efficient and effective way. To do so, it is of huge importance to plan a well-structured schedule with a detailed description of the tasks to execute, where they should be executed and when a task should be performed. This applies to areas such as transportation services, staff distribution, and, unquestionably, production systems. Manufacturing scheduling has been studied for several decades and has been applied in industry in many different forms in an attempt to optimize the production processes and allocate resources as efficiently as possible. However, most of the scheduling has been carried out manually or using simple and limited programs that can barely improve the performance of the system. Until a few decades ago, mass production lines, producinghuge lots of the same products always following the same method, were dominant; today that is not the case, and a better planning approach needs to be considered. In recent years, new production paradigms have been proposed to support companies tackling this problem. These paradigms, such as lean production, agile manufacturing, or smart manufacturing, have been pushed and pulled by industry and academia, which contributes to huge advances in manufacturing. However, the gap between academia and industry is still huge and the link between both of them needs to be reinforced in order to achieve consistent and acceptable results. For instance, both parts should have a closer interaction which will provide more prosperous advances, since academia is often not aware of some manufacturing requirements, such as relevant production technical features, business environment, clients preferences, or societal requirements. On the other hand, companies that do not have a solid research department may not be aware of new technologies and processes being developed, which need to be strongly matureted, mainly by the contribution of industrial partners. Now, more than ever, there is an opportunity to implement robust and efficient schedule solutions, not only on the shopfloor but even along the value chain, since there is more information available than ever before. However, there is a big challenge to implement scheduling solutions in real manufacturing systems. Despite the required complexity of such implementation in the real world, there is not a reference guide in the context of smart manufacturing to assist in the implementation of these solutions. Consequently, the authors see the importance of establishing a baseline that can serve as a starting point when developing manufacturing scheduling systems, with the objective of developing reliable solutions that can be applied to real manufacturing scenarios. Thus, this work aims to identify, analyze, and point out the main trends regarding the adoption of industrial requirements or constraints which academia should focus more on in order to help developing scheduling solutions in smart manufacturing systems. These requirements were identified by analyzing some relevant studies in this area to understand which requirements are being considered when implementing manufacturing scheduling solutions.