dynamicscheduling

Dynamic scheduling is a class of scheduling methods that revise task sequences and resource allocations during execution in response to real-time information or disturbances. Unlike static scheduling, which fixes the order of tasks and allocations before work begins, dynamic scheduling continuously evaluates the current state and may resequence operations, reassign resources, or introduce new tasks as conditions change. The term dynamicscheduling is occasionally seen as a single word, but most literature uses dynamic scheduling.

In manufacturing, dynamic scheduling handles disturbances such as machine breakdowns, rush orders, and variable processing times. In computing, dynamic scheduling refers to CPU or parallel system schedulers that adapt to process arrivals and system load. In data processing and cloud environments, it can allocate resources and reorder tasks to meet service level objectives.

Approaches include online scheduling and rolling-horizon planning; exact methods (MILP, constraint programming) for smaller problems, and heuristics or metaheuristics (greedy rules, local search, genetic algorithms, tabu search) for larger or uncertain problems. Learning-based methods, including reinforcement learning, are increasingly explored to adapt policies from experience.

Common objectives: minimize makespan, total tardiness, or response time; maximize throughput; minimize setup times; maintain schedule stability or fairness. Constraints include precedence, resource capacities, due dates, and time windows. Uncertainty in processing times and arrivals is central to the dynamic setting.

Challenges include computational complexity of on-line decision making, measurement noise, communication delays, and disruption management. Research areas involve robustness, prognostic methods for breakdowns, integration with planning and control systems, and the development of benchmarks such as dynamic versions of RCPSP or job-shop scheduling problems.