Scheduling and Optimization Algorithms

Scheduling and optimization in manufacturing engineering concerns how to sequence and allocate jobs, machines, and resources across production systems so that competing goals—throughput, cost, energy use, and delivery time—are met as efficiently as possible. Problems like the flexible job-shop, where each operation can run on multiple machines, and flowshop sequencing, where jobs move through a fixed series of stages, are deceptively hard: even modest real-world instances can have solution spaces too vast for exhaustive search. Researchers address this by developing hybrid methods that combine genetic algorithms, agent-based control, and other heuristics to find near-optimal schedules under realistic constraints such as sequence-dependent setup times, batch processing, and dynamic disruptions on the shop floor. Active open questions include how to balance multiple conflicting objectives—particularly energy efficiency alongside productivity—and how to build scheduling systems that can adapt in real time when machines fail or new orders arrive unexpectedly.

Works

102,572

Total citations

1,281,158

Keywords

SchedulingManufacturingFlexible Job-shopGenetic AlgorithmAgent-based ControlEnergy-efficient