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Rezaee Nik, Ebrahim, Molavi, Fariba. (1394). A multi- objective project portfolio selection and scheduling problem under uncertainty (case study: company of Payafanavaran Ferdowsi). , 7(3), 469-488. doi: 10.22059/imj.2015.57260
Ebrahim Rezaee Nik; Fariba Molavi. "A multi- objective project portfolio selection and scheduling problem under uncertainty (case study: company of Payafanavaran Ferdowsi)". , 7, 3, 1394, 469-488. doi: 10.22059/imj.2015.57260
Rezaee Nik, Ebrahim, Molavi, Fariba. (1394). 'A multi- objective project portfolio selection and scheduling problem under uncertainty (case study: company of Payafanavaran Ferdowsi)', , 7(3), pp. 469-488. doi: 10.22059/imj.2015.57260
Rezaee Nik, Ebrahim, Molavi, Fariba. A multi- objective project portfolio selection and scheduling problem under uncertainty (case study: company of Payafanavaran Ferdowsi). , 1394; 7(3): 469-488. doi: 10.22059/imj.2015.57260

A multi- objective project portfolio selection and scheduling problem under uncertainty (case study: company of Payafanavaran Ferdowsi)

Industrial Management Journal
مقاله 4، دوره 7، شماره 3، دی 2015، صفحه 469-488    اصل مقاله (331.59 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/imj.2015.57260
نویسندگان
Ebrahim Rezaee Nik1؛ Fariba Molavi* 2
1Assistant Prof., Faculty of Industrial Engineering, Sadjad University of Technology, Mashhad, Iran
2MSc. Student, Industrial Engineering, Sadjad University of Technology, Mashhad, Iran
چکیده
Nowadays organization especially R&D centers are dealing with project portfolio selection decisions under uncertainty. Moreover in the most of the past research, project portfolio selection and scheduling are often considered to be independent problem. This leads to insufficient result in real world. So in this research simultaneous project portfolio selection and scheduling problem is modeling whose objectives are maximizing expected profit and minimizing risk. Moreover there is autocorrelation among annual earnings. Therefore an efficient time series methodology is used for forecasting. Another advantage of proposed model is considering uncertainty of project success and earnings and also risk of dealing with budget deficiency. Due to the complexity of problem, especially for large size, practical swarm, simulated annealing and genetic algorithm are presented and their efficiency is compared by a hypothetical example. The results show efficiency of simulated annealing algorithm in terms of quality and execution time. Finally the model is validated via its application to a knowledge based company in Ferdowsi University of Mashhad.
کلیدواژه‌ها
Auto-correlated Earning؛ Probability of Project Success؛ Project Portfolio Selection & Scheduling؛ Simulated Annealing
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