000			01693nam a22001697a 4500
003			NUST
082			_a670
100			_a YOUSAF, SAMRA _9125658
245			_aTo improve the Makespan of a Standard Job Shop Scheduling problem incorporating GA by using Python / _cSAMRA YOUSAF
264			_aIslamabad ; _bSMME-NUST _c2024.
300			_a87p. ; _bsoftcopy , _c30cm.
500			_aEffective 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.
650			_aMS Design and Manufacturing Engineering _9119567
700			_aSupervisor: DR. SHAHID IKRAMULLAH BUTT _9119623
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/46044
942			_2ddc _cTHE
999			_c611378 _d611378