article · Facta Universitatis Series Mechanical Engineering
Efficient scheduling in manufacturing and processing environments is critical for productivity. A hybrid computational method combines genetic algorithms with penguin search optimisation to solve the flow shop scheduling problem. Genetic algorithms contribute diverse solution exploration through mechanisms resembling natural selection, including selection, crossover, and mutation. Meanwhile, penguin search optimisation mimics the cooperative foraging behaviour of penguins to achieve rapid convergence on optimal solutions. The combined technique incorporates problem-specific modifications tailored directly to flow shop operations. Computational testing demonstrates that this hybrid algorithm outperforms standalone genetic algorithms, independent penguin search optimisation, and several other standard metaheuristic approaches in finding effective scheduling arrangements.
Determining the best sequence of tasks in industrial workflows directly affects throughput, machine idle time, and operating costs. By improving the speed and quality of scheduling solutions, advanced optimisation algorithms help complex facilities streamline resource allocation, reduce operational delays, and raise overall productivity across competitive production environments.
The algorithm addresses flow shop scheduling, which is relevant to manufacturing and logistics operations seeking to sequence tasks effectively. The abstract reports algorithmic benchmarking rather than industrial trials, placing the tool at an early computational stage. Software developers building production planning tools could potentially incorporate this method, but real-world testing in live industrial environments is needed to verify operational performance.
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This paper presents a novel hybrid approach, fusing genetic algorithms (GA) and penguin search optimization (PSeOA), to address the flow shop scheduling problem (FSSP). GA utilizes selection, crossover, and mutation inspired by natural selection, while PSeOA emulates penguin foraging behavior for efficient exploration. The approach integrates GA's genetic diversity and solution space exploration with PSeOA's rapid convergence, further improved with FSSP-specific modifications. Extensive experiments validate its efficacy, outperforming pure GA, PSeOA, and other metaheuristics.
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DOI: 10.22190/fume230615028m
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