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Showing 2 results for Jamshidi
Maintenance and work-rest scheduling in Human-machine system according to fatigue and reliability
Rasol Jamshidi,
Volume 27, Issue 3 (IJIEPR 2016)
Abstract

Most manufacturers use human-machine systems to produce high-quality products. Dealing with human-machine systems is very complicated since not only machines should be utilized in proper condition but also appropriate environment should be provided for human resources. Most manufacturers have a maintenance plan for machines but many of them do not have a proper work-rest schedule for human resources. Considering this fact we emphasis on human role in human-machine systems maintenance and propose a mathematical model that obtains the optimal work-rest schedule for humans based on fatigue-recovery models and the optimal maintenance policy for machines based on reliability level. The performance of proposed model was examined by some instances and the obtained results indicate this model can provide effective maintenance policy for human-machine systems.


A nonlinear model to solve multiple attribute decision-making problems with interval-valued neutrosophic numbers
Maryam Arshi, Abdollah Hadi-Vencheh, Adel Aazami, Ali Jamshidi,
Volume 35, Issue 4 (IJIEPR 2024)
Abstract
Linguistic variables (LVs) provide a reliable expression of cognitive information. By inheriting the advantages of LVs, we can express uncertain and incomplete cognitive information in multiple attribute decision-making (MADM), and they do so better than existing methods.  In the decision-making process, we can consider decision experts’ (DEs’) bounded rationality, such as cognition toward loss caused by the DEs’ cognitive limitations during the decision process. Therefore, it is necessary to propose a novel cognitive decision approach to handle MADM problems in which the cognitive information is expressed by LVs. In this paper, we employ LVs to represent uncertain and hesitant cognitive information. Then, we propose a mathematical programming approach to solve the MADM problems where attributes or cognitive preferences are not independent.  Moreover, the validity and superiority of the presented approach are verified by dealing with a practical problem. 

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