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- 저자 : Mehrdad Naderi (검색결과 3 건)
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Mehrdad Naderi,Alireza Arabpour,Tsung-I Lin,Ahad Jamalizadeh 한국통계학회 2017 Journal of the Korean Statistical Society Vol.46 No.3
This paper presents a new extension of nonlinear regression models constructed by assuming the normal mean–variance mixture of Birnbaum–Saunders distribution for the unobserved error terms. A computationally analytical EM-type algorithm is developed for computing maximum likelihood estimates. The observed information matrix is derived for obtaining the asymptotic standard errors of parameter estimates. The practical utility of the methodology is illustrated through both simulated and real data sets.
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A Multi-Objective Evolutionary Algorithm for Scheduling Flexible Manufacturing Systems
Mehrdad Nouri Koupaei,Mohammad Mohammadi,Bahman Naderi 대한산업공학회 2017 Industrial Engineeering & Management Systems Vol.16 No.2
In recent decades, flexible manufacturing systems have emerged as a response to market demands of high product diversity. Scheduling is one important phase in production planning in all manufacturing systems. Although scheduling in classical manufacturing systems, such as flow and job shops, are well studied. Rarely, any paper studies scheduling of the more recent flexible manufacturing system. Since the problem class is NP-hard, different scheduling algorithms such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) can be designed to solve this problem. This paper investigates a multi-objective evolutionary algorithm for scheduling flexible manufacturing systems to minimizing makespan, earliness and tardiness and startup costs. The distinctive feature of the proposed multi-objective evolutionary algorithm is its ability to search the solution space by an intelligent method, which is unlike other meta-heuristic algorithms avoid the coincidental method. Also, answer with the best quality and highest dispersion to obtain the dominant answer is used. Finally, we carry out computational experiments to demonstrate the effectiveness of our algorithm. The results show that the proposed algorithm has the ability to achieve the good solutions in reasonable computational time.
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A Multi-Objective Evolutionary Algorithm for Scheduling Flexible Manufacturing Systems
Koupaei, Mehrdad Nouri,Mohammadi, Mohammad,Naderi, Bahman Korean Institute of Industrial Engineers 2017 Industrial Engineeering & Management Systems Vol.16 No.2
In recent decades, flexible manufacturing systems have emerged as a response to market demands of high product diversity. Scheduling is one important phase in production planning in all manufacturing systems. Although scheduling in classical manufacturing systems, such as flow and job shops, are well studied. Rarely, any paper studies scheduling of the more recent flexible manufacturing system. Since the problem class is NP-hard, different scheduling algorithms such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) can be designed to solve this problem. This paper investigates a multi-objective evolutionary algorithm for scheduling flexible manufacturing systems to minimizing makespan, earliness and tardiness and startup costs. The distinctive feature of the proposed multi-objective evolutionary algorithm is its ability to search the solution space by an intelligent method, which is unlike other meta-heuristic algorithms avoid the coincidental method. Also, answer with the best quality and highest dispersion to obtain the dominant answer is used. Finally, we carry out computational experiments to demonstrate the effectiveness of our algorithm. The results show that the proposed algorithm has the ability to achieve the good solutions in reasonable computational time.
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