Focal Interest
Focal Interest

Scheduling (production processes)

  1. Spectral metrics predicted requirements integration effort
    Spectral graph metrics derived from NLP-extracted requirement networks predict integration effort with 95% correlation, outperforming traditional structural complexity measures for early-stage.
  2. Enhanced grey wolf optimization improved cloud load balancing
    Enhanced optimization algorithm for cloud load balancing achieves 25% improvement in resource utilization and outperforms standard metaheuristic approaches in benchmark testing.
  3. PAT: Accelerating LLM Decoding via P refix- A ware A t tention with Resource Efficient Multi-Tile Kernel
    PAT optimizes LLM decode-phase attention by exploiting shared request prefixes and adaptive kernel tiling, reducing memory bandwidth bottlenecks in multi-request serving scenarios.
  4. Scheduling model integrates pallets, machines, and setup stations
    Mixed-integer programming and mutation-based algorithm for scheduling flexible manufacturing with pallet automation, setup stations, and fixture pallets to minimize makespan.
  5. Optimizing integrated energy systems with a virtual energy station framework: Exergy-based scheduling and multi-energy integration
    Bi-level exergy-based optimization for integrated energy systems incorporating electricity, heat, gas, and hydrogen with improved metaheuristic scheduling algorithm.
  6. Proactive VM consolidation cuts energy use and SLA violations
    Framework for VM consolidation combining workload prediction and physics-constrained reinforcement learning. Achieves 23.2% energy reduction and 43.5% SLA violation reduction in cloud datacenters.
  7. Hybrid deep learning improved edge-cloud task scheduling in simulation
    Deep reinforcement learning framework for adaptive task scheduling in edge-cloud computing with improved SLA compliance, reduced operational costs, and lower task rejection rates.
  8. Hybrid scheduling algorithm cuts pharmaceutical production costs
    Improved particle swarm optimization algorithm reduces pharmaceutical manufacturing costs by 6.3% through enhanced scheduling efficiency, improving equipment utilization and delivery rates.
  9. AGENT improved makespan in heterogeneous cloud task allocation
    AGENT framework improves task allocation in cloud systems using elitism-guided genetic algorithm with adaptive parameters, achieving 3-29% makespan improvements for heterogeneous VM scheduling.
  10. Online genetic programming improved scheduling in dynamic job shops
    Online Genetic Programming evolves dynamic flexible job shop scheduling rules in real-time without simulation models, achieving superior performance through adaptive fitness and population.
  11. Liger+ dynamically balances latency and throughput in large model inference
    Distributed inference system using interleaved parallelism to dynamically balance latency-throughput trade-offs via task-aware batch management and strategic kernel scheduling across multiple GPUs.