no. 1
Modeling and optimization of batch production based on layout and cutting problems under uncertainty
RAIRO. Operations Research, Tome 55 (2021) no. 1, pp. 61-81

This paper presents modeling and optimization of batch production based on layout, cutting and project scheduling problems by considering scenario planning. In order to solve the model, a novel genetic algorithm with an improvement procedure based on variable neighborhood search (VNS) is presented. Initially, the model is solved in small sizes using Lingo software and the combined (proposed) genetic algorithm; then the results are compared. Afterwards, the model is solved in large sizes by utilizing the proposed algorithm and simple genetic algorithm. The main findings of this paper show: (1) The suggested algorithm is valid and able to achieve optimal and near-optimal solutions. This conclusion was made after proving the validity of the proposed method by solving a case study by employing the classical method (employing Lingo 11). And when the results were compared with the ones obtained by the proposed algorithm, they were found to be the same in both cases. (2) The combined genetic algorithm is more effective in obtaining optimal boundaries and the solutions close to them in all cases compared to the classical (simple) genetic algorithm. In other words, the main finding of this paper is a combined genetic algorithm to optimize batch production modeling problems, which is more efficient than the methods provided in the literature.

DOI : 10.1051/ro/2020105
Classification : 90B30, 90-08
Keywords: Genetic algorithms, project scheduling, batch production, scenarios, layout and cutting problems 
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     title = {Modeling and optimization of batch production based on layout and cutting problems under uncertainty},
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     year = {2021},
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     language = {en},
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Saeedi, Mohammadhossein; Feizi, Ramyar. Modeling and optimization of batch production based on layout and cutting problems under uncertainty. RAIRO. Operations Research, Tome 55 (2021) no. 1, pp. 61-81. doi: 10.1051/ro/2020105

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