Scheduling and Optimization Algorithms

Manufacturing scheduling research examines how to sequence and allocate jobs, machines, and resources across production systems to minimize costs, time, and energy consumption. In complex environments like the flexible job-shop, where each job can follow different routes through a set of machines, finding a near-optimal schedule is computationally hard, so researchers rely on methods such as genetic algorithms and hybrid optimization to search large solution spaces efficiently. Agent-based control adds a further dimension by allowing scheduling decisions to adapt dynamically as conditions change on the shop floor, such as machine breakdowns or rush orders. Active open questions include how to balance competing objectives—throughput, energy use, and due-date adherence—simultaneously, and how to design algorithms that scale reliably from small test cases to the messy, high-variability conditions of real industrial plants.

Works

103,492

Total citations

1,298,461

Keywords

SchedulingManufacturingFlexible Job-shopGenetic AlgorithmAgent-based ControlEnergy-efficient