Abstract:
Effective job scheduling is crucial in industrial manufacturing planning, where
each job, consisting of multiple operations, must be allocated to the machines that are
available machines for processing. Each job has a specific interval, and every machine
can only handle one operation at a time. Efficient job allocation is essential to minimise
the makespan and reduce machine idle time. In Job Shop Scheduling (JSS), job
operations follow a specified order. Genetic Algorithms (GA) have emerged as a
popular heuristic for tackling various scheduling problems. This study introduces a
Genetic Algorithm Integrating Python (GAIP) with feasibility-preserving solution
representation, initialization, and operators tailored for the JSS problem. The proposed
GAIP achieves the best-known results with high success rates on the Muth and
Thomson and Lawrence benchmark datasets. Experimental results demonstrate the
GA's rapid convergence towards optimal solutions. Incorporating GA with local search
and two selection methods at the same time is done to further enhance solution quality
and success rates.
