Main path and cluster analysis-based review of scheduling in the supply chain

The need for establishing supply chain resilience in critical industries, such as semiconductors, automobiles, and consumer electronics, calls for holistic operations planning and scheduling. Supply chain scheduling (SCS) refers to the coordinated planning of production and logistics operations to enhance performance beyond the optimal solution for each operation. The literature on SCS has garnered growing attention, with a total of 1,288 articles published between 1993 and 2025. A comprehensive review of this domain, informed by algorithmic big-data analyses such as Main Path Analysis (MPA) and Cluster Analysis (CA), helps to delineate a clearer landscape of the field's evolution for a broader academic and practitioner audience. This study sought to analyze the main development trajectories and research topics in the literature on SCS, with the objective of providing an objective, algorithm-based review of the existing body of research. The most influential articles on knowledge-dissemination pathways are identified and reviewed using the MPA algorithm. The prevalent research topics and computational advancements are analyzed using CA. The advances in mathematical modeling and optimization algorithms for SCS are discussed. Drawing on the big picture and the development patterns, the study speculates on emerging development trends and suggests directions for future research.