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Research Article Open Access
The objective is to investigate a well-known scheduling problem, namely the Permutation Flow-Shop Scheduling with the makespan as the objective function to be minimized. Various techniques, ranging from the simple constructive algorithms to the state-of-the-art techniques, such as Genetic Algorithms (GA), have been cited in the pertinent literature to solve this type of scheduling problem. A new GA-based solution methodology was developed and implemented. In this context, the performance of a stand-alone genetic algorithm (referred to as the non-hybrid genetic algorithm) and a novel hybridized genetic algorithm amalgamated with an iterative greedy algorithm were studied. The parameters of the hybrid and the non-hybrid genetic algorithms were tuned using a Full Factorial Experimental Design and Analysis of Variance. The performance of the properly tuned hybridized GA-based algorithm was examined on the existing standard benchmark problems of Taillard and it was shown that the proposed hybridized genetic algorithm performs very well on the benchmark problems.
Flow-shop scheduling, Meta-heuristics, Genetic algorithms, Greedy search, Design of experiments, Analysis of variance, Applied Statistics, Cognitive Systems Engineering, Commercialization of New Techniques, Design and Microfabrication, Dynamical System, Health care management, Industrial Crystallization, Logistics, Management Cybernetics, Manufacturing system, Materials Management, Nonlinear Dynamics, Operations Research, Process Engineering, Production and Operations Management, Reliability engineering, Stochastic control, Technologies Management