Genetic algorithms for minimizing makespan in a flow shop with two capacitated batch processing machines

Manjeshwar, Praveen Kumar; Damodaran, Purushothaman; Srihari, K.

doi:10.1007/s00170-010-3150-0

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…Hamdi et al 30 proposed 6 versions of the genetic algorithms based on different genetic operators to minimize the makespan in a two-machine cross-docking FSP. Praveen et al 31 proposed a GA approach to minimize the makespan for two batch processing machines in a flow shop and experimental study indicated that the GA approach outperforms the other approaches by reporting better solution. Pavol et al 32 proposed a hybrid improvement heuristic for FSP based on the idea of GA and heuristic method.…”

Section: Introductionmentioning

confidence: 99%

A computational efficient optimization of flow shop scheduling problems

Liang

Zhong

Liu

et al. 2022

Sci Rep

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Flow shop scheduling problems are NP-hard problems. Heuristic algorithms and evolutionary metaheuristic algorithms are commonly used to solve this kind of problem. Although heuristic algorithms have high solving speed, the solution quality is not good. Evolutionary algorithms make up for this defect in small-scale problems, but the solution performance will deteriorate with the expansion of the problem scale and there will be premature problems. In order to improve the solving accuracy of flow shop scheduling problems, a computational efficient optimization approach combining NEH and niche genetic algorithm (NEH-NGA) is developed. It is strengthened in the following three aspects: NEH algorithm is used to optimize the initial population, three crossover operators are used to enhance the genetic efficiency, and the niche mechanism is used to control the population distribution. A concrete application scheme of the proposed method is introduced. The results of compared with NEH heuristic algorithm and standard genetic algorithm (SGA) evolutionary metaheuristic algorithm after testing on 101 FSP benchmark instances show that the solution accuracy has been significantly improved.

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Section: Introductionmentioning

confidence: 99%

A computational efficient optimization of flow shop scheduling problems

Liang

Zhong

Liu

et al. 2022

Sci Rep

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“…With the development of mathematics, two important metaheuristics have been presented to solve de BPM when an exact method does not find the optimal value in a determinate time: Genetic Algorithm (GA) and Simulated Annealing (SA). For GA, Manjeshwar et al [20], they used GA to solve flow shop with to machines and contrast with SA and Simplex and generated their instances;…”

Section: Introductionmentioning

confidence: 99%

A Novel Heuristic to Minimize the Bottlenecks Presence in Batch Generation. Case of Study.

Eg²,

Ds³

et al. 2021

Preprint

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This paper presents a new heuristic method capable of minimizing the presence of bottlenecks generated when production batches have a distinct makespan. The proposed heuristic groups the jobs into items, where the one with the longest processing time in the batch determines the makespan. To test the heuristic, information was collected from a real paint process with two stations: one with a single cabin and the other with two parallel cabins. The capacity of processing jobs is limited by the cabin dimensions where jobs have different sizes and processing times. A makespan comparison between the heuristic proposed versus the First in First out (FIFO) dispatching rule that the case of study uses. Additionally, ten random instances based on data taken from the real process were created with the purpose to compare the new heuristic method versus Genetic Algorithm (GA) and Simulated Annealing (SA). The result of the comparison to FIFO, GA and SA showed that the proposed heuristic minimizes the bottleneck in a and creating batches almost with the same makespan. Results indicated a bottleneck time reduction of 96% when new heuristic method were compared to FIFO rule, while compared to Generic Algorithm and Simulated Annealing the bottleneck reduction were around 89% in both cases.

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