ORCID Profile
0000-0002-9069-5453
Current Organisation
Growing Science
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Publisher: Elsevier BV
Date: 10-2013
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 11-2018
Publisher: Zhejiang University Press
Date: 04-2009
Publisher: SAGE Publications
Date: 08-08-2016
Abstract: During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially genetic algorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed genetic algorithm–robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust genetic algorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust genetic algorithm indicate significant cost improvements compared to the conventional genetic algorithm methods and a selective number of other heuristic and meta-heuristic techniques.
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 06-2012
Publisher: Growing Science
Date: 2017
Publisher: Science Publications
Date: 12-2014
Publisher: SAGE Publications
Date: 16-12-2014
Abstract: This article deals with a redundancy allocation problem in series–parallel systems with a cold standby strategy, in which component time to failure follows an exponential distribution that has constant hazard rate. Ex les of this kind of system are systems composed of electronic components (e.g. transistors, capacitors, resistors and integrated circuits) used in control systems, power generators and the like, which needs to be supported by standby components to prevent unexpected failures. In this article, the reliability of each component is imprecise in terms of interval data, and only the lower and upper bounds of reliabilities are known. The problem is formulated through Min-Max regret criterion, which is commonly used to define robust solutions. The resulted problem formulation contains unlimited numbers of constraints, and Benders’ decomposition method is implemented to deal with the given problem. This method is compared with an enumeration method and a stochastic search method called genetic algorithm to show its effectiveness. The results show that the proposed Benders’ decomposition method is conducive to the same results in a reasonable amount of time. The performance of the proposed model using Benders’ decomposition method is also examined over different problem sizes, and the associated results are analyzed. The results show that for large-sized problems, Benders’ decomposition method is converged with fewer numbers of cuts, and therefore, it is time-economic for solving such problems.
Publisher: Elsevier BV
Date: 10-2014
Publisher: Elsevier BV
Date: 2012
Publisher: Elsevier BV
Date: 04-2010
Location: Iran (Islamic Republic of)
No related grants have been discovered for Seyed jafar Sadjadi.