نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناسی ارشد دانشگاه علوم و تحقیقات دانشگاه آزاد اسلامی
2 دانشیار دانشکده مهندسی صنایع و مکانیک دانشگاه ازاد اسلامی قزوین
چکیده
در این مقاله، یک مدل بهینه سازی جهت انتخاب بهترین سبد پروژه از بین پروژههای موجود، بهترین سطح
استخدام منابع، سپس زمانبندی پروژههای انتخاب شده جهت بیشینه کردن ارزش خالص فعلی با رعایت
محدودیتها ارائه شده است. چون مدل توسعه یافته در زمره مسایل سخت از نظر محاسباتی قرار دارد، لذا برای
حل این مساله یک الگوریتم فراابتکاری بر مبنای الگوریتم ژنتیک پیشنهاد شده است. در الگوریتم حل
پیشنهادی علاوه بر کاربرد عملگرهای معمول ژنتیک مانند تقاطع و جهش از عملگرهایی هوشمند جهت
جستجوی محلی در حوزه منابع و جابجایی فعالیتهای با جریان مالی منفی استفاده شده است. پارامترهای
کلیدی الگوریتم در راستای تسریع همگرایی آن با استفاده از روش تاگوچی تنظیم شده است. سپس تعداد 90
مساله آزمایشی، شامل 30 مساله در ابعاد کوچک، 30 مساله در ابعاد متوسط و 30 مساله در ابعاد بزرگ با
استفاده از روش پیشنهادی حل شده وکارایی آن گزارش شده است. همچنین در مسائل سایز کوچک
جوابهای حاصل از الگوریتم ژنتیک با جوابهای بهینه موضعی مدل ریاضی بدست آمده با نرمافزار لینگو
مقایسه شده، که میانگین جوابهای حاصل از الگوریتم ژنتیک بهتر از جوابهای بهینه موضعی حاصل از لینگو
بوده است. در مسائل سایز متوسط و بزرگ که هیچ جوابی با استفاده از لینگو در زمان محدود شده بدست
نیامده بود، نتایج نشان میدهد که جوابهای حاصل از الگوریتن پیشنهادی دارای پایداری مناسب میباشند.
کلیدواژه‌ها
عنوان مقاله [English]
An integrated project portfolio selection and resource investment problem to maximize net present value using genetic algorithm
نویسندگان [English]
- Hamidreza Shahabifard 1
- Behrouz Afshar-nadjafi 2
چکیده [English]
In this paper, a mathematical model is proposed for project portfolio
selection and resource availability cost problem to scheduling activities in
order to maximize net present value of the selected projects preserving
precedence and resource constraints. Since the developed model belongs to
NP-hard problems list, so a genetic based meta-heuristic algorithm is
proposed to tackle the developed model. In the proposed algorithm beside
common operators of genetic algorithms such as crossover & mutation, some
intelligent operators are utilized for local search in computed resources and
shifting the activities with negative cash flows. The key parameters of the
algorithm are calibrated using Taguchi method to accelerate convergence of
the proposed algorithm. Then, the algorithm is used to solve 90 test
problems consisting 30 small-scale, 30 middle-scale and 30 large scale
problems to examine the algorithm’s performance. It is observed that, in
small problems, the obtained solutions from the proposed genetic algorithm
have been comparably better than the local optimum solutions stemmed
from Lingo software. On the other hand, for the middle and large size
problems which there is no local optimum available within the limited CPU
time, robustness of the proposed algorithm is appropriate
کلیدواژه‌ها [English]
- Project portfolio selection
- Project scheduling
- Genetic Algorithm
- Resource investment
- Net present value
schedule maintenance policy based on multiple-criteria decision-making.
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