Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
Articles by I. Mahdavi
Total Records ( 2 ) for I. Mahdavi
  I. Mahdavi , H. Fazlollahtabar , A. Heidarzade , N. Mahdavi-Amiri and Y.I. Rooshan
  Web-based E-Learning Systems (WELSs) have emerged as new means of skill training and knowledge acquisition, encouraging both academia and industry to invest resources in the adoption of these systems. Traditionally, most pre- and post-adoption tasks related to evaluation are carried out from the viewpoints of technology. Since users have been widely recognized as being a key group of stakeholders in influencing the adoption of information systems, their attitudes about these systems are considered as pivotal. Therefore, based on the theory of multi-criteria decision making and the research results concerning user satisfaction in the fields of human-computer interaction and information systems, a heuristic multi-criteria methodology using the learner satisfaction perspective to support evaluation-based activities occurring at the pre and post-adoption phases of the WELS life cycle is proposed. In addition, using the methodology, we empirically investigate learners` perceptions of the relative importance of decision criteria as beneficial tools for evaluation in management.
  M. Zandieh , I. Mahdavi and A. Bagheri
  A meta-heuristic approach for solving the flexible job-shop scheduling problem (FJSP) is presented in this study. This problem consists of two sub-problems, the routing problem and the sequencing problem and is among the hardest combinatorial optimization problems. We propose a Genetic Algorithm (GA) for the FJSP. Our algorithm uses several different rules for generating the initial population and several strategies for producing new population for next generation. Proposed GA is tested on benchmark problems and with due attention to the results of other meta-heuristics in this field, the results of GA show that our algorithm is effective and comparable to the other algorithms.
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility