• Shubham Meena
• Mr Vineet Dwivedi

The job shop scheduling problem is extremely difficult to solve since it is a strong NP-hard issue. The two most common techniques for addressing JSP are direct and heuristic methods. This study has sought and developed effective hybrid heuristics for dealing with issues of massive scale and producing high-quality answers. The multi-objective optimization takes into account more than one goal. The first goal is to find a set of solutions that are as close as possible to the Pareto-optimal front. The second goal is to come up with as many different solutions as possible. Many problems in the real world have more than one goal, which makes them more interesting to solve. This research has tried to find a solution to this problem of multi-objective optimization. Multi- objective benchmark tasks are used to design and test algorithms for the Hybrid Sheep Flocks Heredity Model and the Artificial Immune System Shifting Bottleneck Algorithm. When compared to the Artificial Immune System Shifting Bottleneck Algorithm and other findings from the literature, the Hybrid Sheep Flocks Heredity Model Algorithm is shown to be an effective tool for tackling multi objective problems issue.

SFHM, MULTI-OBJECTIVE OPTIMIZATION, JSP, GA

"Optimization of Job Shop Scheduling Performance Measures using Hybrid Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.b743-b753, January-2023, Available :http://www.jetir.org/papers/JETIR2301200.pdf

"Optimization of Job Shop Scheduling Performance Measures using Hybrid Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppb743-b753, January-2023, Available at : http://www.jetir.org/papers/JETIR2301200.pdf