Application of genetic algorithm, GA, to solve a flow shop scheduling problem with changeover times in operations: a case study
Journal Title: BOHR International Journal of Operations Management Research and Practices - Year 2024, Vol 3, Issue 1
Abstract
Flow Shop Scheduling (FSS) Problems are examples of combinatorial optimization issues that are classified as NP-hard.Becauseof theNP-hardstructureofFSS problems,itcanbeextremelychallenging tofindmathematical modeling methodologies that will result in an optimal solution for these problems. The Genetic Algorithm (GA), which is a metaheuristic approach, is one of the most important factors in the process of locating near-optimal answers to NP-hard optimization issues. In this research, a GA model for addressing an FSS problem was developed with the goal of lowering the overall weighted tardiness time and placing a constraint on the operation changeover time. When compared with the performance of the standard heuristics EDD, being used in the companyunderstudy,theGAmodel’sperformancewasshowntobesuperior.Basedonthefindings,itcan beshownthattheobjectivevaluewascutby43%,goingfrom215.95(h)to123.07(h).Thisdemonstratesthatthe GA model is an effective strategy for addressing FSS problems.
Authors and Affiliations
Phong Nguyen Nhu Kim Ngan Nguyen Thi Thanh Huyen Tran Vo Thi
Keywords
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- EP ID EP743578
- DOI 10.54646/bijomrp.2024.25
- Views 9
- Downloads 0
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