A genetic algorithm for preemptive scheduling of a single machine

Golmohammadi, Amir-Mohammad; Bani-Asadi, Hamid; Zanjani, Hamed Jafar; Tikani, Hamid

doi:10.5267/j.ijiec.2016.3.004

Cited by 14 publications

(7 citation statements)

References 12 publications

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“…In the selection process, the roulette wheel method was used; in the crossover, the method called single-point or simple method was used (Golmohammadi et al, 2016), the ones with best fits based on the optimization tests.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence in seeding density optimization and yield simulation for oat

Dornelles

Kraisig

Silva

et al. 2018

Rev. bras. eng. agríc. ambient.

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A B S T R A C TArtificial intelligence may represent an efficient strategy for simulation and optimization of important processes in agriculture. The main goal of the study is to propose the use of artificial intelligence, namely artificial neural networks and genetic algorithms, respectively, in the simulation of oat grain yield and optimization of seeding density, considering the main succession systems of southern Brazil. The study was conducted in a randomized complete block design with four replicates, following a 4 x 2 factorial scheme, for seeding densities (100, 300, 600 and 900 seeds m -2 ) and oat cultivars (Brisasul and URS Taura), in succession systems of corn/oats and soybean/oats. A multi-layered artificial neural network and a genetic algorithm were implemented in Java programming language, and the results obtained from this implementation were compared with traditional polynomial regression. The use of artificial intelligence through neural networks and genetic algorithms allows the efficient simulation of oat grain yield and better optimization of seeding density in comparison to polynomial regression, considering the main succession systems in southern Brazil.Inteligência artificial na otimização da densidade de semeadura e simulação da produtividade da aveia R E S U M O O uso de inteligência artificial pode representar uma estratégia eficiente de simulação e otimização de processos importantes na agricultura. O objetivo deste estudo é propor o uso de inteligência artificial via redes neurais artificiais e algoritmos genéticos, respectivamente, na simulação da produtividade de grãos de aveia (Avena sativa) e na otimização da densidade de semeadura, nos principais sistemas de sucessão do sul do Brasil. O estudo foi conduzido em blocos ao acaso com quatro repetições em esquema fatorial 4 x 2, para as densidades de semeadura (100, 300, 600 e 900 sementes m -2 ) e cultivares de aveia (Brisasul e URS Taura), nos sistemas de sucessão milho/aveia e soja/aveia. Implementou-se uma rede neural artificial de múltiplas camadas e um algoritmo genético, em linguagem de programação Java, e comparou-se os resultados obtidos desta implementação com análises tradicionais de regressão polinomial. O uso de inteligência artificial via redes neurais artificiais e algoritmos genéticos permite simular com eficiência a produtividade de grãos de aveia e melhor otimização da densidade de semeadura na comparação com regressão polinomial, considerando os principais sistemas de sucessão no sul do Brasil.

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Section: Methodsmentioning

confidence: 99%

Artificial intelligence in seeding density optimization and yield simulation for oat

Dornelles

Kraisig

Silva

et al. 2018

Rev. bras. eng. agríc. ambient.

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“…Using Eq. ( 3), electrical generation by photovoltaic system can be calculated with attention to ambient temperature and solar irradiation [31].…”

Section: Modelling Photovoltaicmentioning

confidence: 99%

Economic Performance of a Hybrid Renewable Energy System with Optimal Design of Resources

Dwijendra

Sharma

Asary

et al. 2022

Environmental and Climate Technologies

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The renewable energy is expanding in the sub-systems of distribution electrical grids, due to having low energy costs and high reliability. In this study, off-grid design of the hybrid energy system based-renewable resources is proposed for a house in Hamadan city in Iran. The resources are designed based on economic and climate data such as battery, solar cells, wind turbines, and electrolyser/fuel cell. The design of mentioned resources is done with attention to twofold optimization modelling including operation cost (OC) and net present cost (NPC). The modelling of resources is implemented in HOMER software based on optimal sizing and installation costs. In the numerical simulation, the three optimal designs are presented to meet electrical demand with minimum costs and high reliability.

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“…Golmohammadi et al [10] developed a new mathematical model for minimizing the earliness tardiness objectives. This proposed model used for solving small scale problems such as the optimal solution's availability and NPhard.…”

Section: Literature Surveymentioning

confidence: 99%

Improved Whale Optimization with Buffer Setup Time

Anandapadmanabhan*,

Eshwaramurthy

2019

IJITEE

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The job shop problem is widespread in industrial applications due to lateness in getting a better solution. Buffer plays an important role in the completion of jobs. Proper utilization of buffer by fixing optimal buffer setup time can lead to optimal completion time so that lateness is minimized. This work introduced Improved Whale Optimization with Buffer Setup time Technique (IWOBS) for setting optimal buffer setup time. Using tabu movements, an optimum solution for the nearest buffer setup time is derived and the genetic algorithm is equipped to find fitness score to gather the best buffer setup time with respect to problem consideration. The proposed IWOBS algorithm is proved to be highly effective compared to other existing algorithms

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A genetic algorithm for preemptive scheduling of a single machine

Cited by 14 publications

References 12 publications

Artificial intelligence in seeding density optimization and yield simulation for oat

Artificial intelligence in seeding density optimization and yield simulation for oat

Economic Performance of a Hybrid Renewable Energy System with Optimal Design of Resources

Improved Whale Optimization with Buffer Setup Time

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