Abstract: Massive Open Online Courses (MOOCs) have revolutionized access to education, but their scalability presents significant challenges in effective resource allocation and scheduling. This paper explores the application of Operations Research (OR) techniques to optimize key constraints in MOOC environments—specifically, instructor time management, student learning progress, and grading workloads. By formulating the problem as a multi-objective optimization model, we demonstrate how linear programming (LP), integer programming (IP), and queuing theory can support intelligent resource distribution. Case studies and simulation models show that the application of OR methods can significantly reduce bottlenecks in instructional support, balance grading demands, and enhance personalized student pacing. The findings suggest a hybrid OR framework can be embedded within MOOC platforms to improve efficiency and learning outcomes.
Keywords: Resource Allocation, Scheduling Optimization, Grading Workload, Multi-objective Optimization, Linear Programming.
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DOI:
10.17148/IJARCCE.2025.14727
