Bridging Speed and Optimality in Job Scheduling: A Hybrid Ant Colony Optimization Approach for Distributed Systems

Efficient job scheduling in distributed systems faces exponential complexity growth as systems scale. While queue-based methods (e.g., FIFO) generate schedules rapidly but suboptimally, optimization tools achieve higher quality at significant computational cost. We propose a hybrid ant colony optimization (HACO) algorithm bridging this gap. HACO uses queue-based warm-start initialization for pheromone levels, constructs disjunctive graphs modeling precedence and resource constraints, and applies parallel local search on selected subgraphs to escape local optima. Our approach combines the speed of heuristics with optimization quality through strategic pheromone updates and OR-Tools integration. Experimental evaluation on job shop scheduling (JSSP), flexible job shop (FJSP), and synthetic large-scale problems demonstrates 3-5% deviation from optimality with 5-10x speedup over state-of-the-art solvers. Results show consistent performance across varying problem scales, making HACO compelling for large-scale distributed scheduling where computational efficiency is critical.