Kaynak Kısıtlı Yazılım Proje Çizelgeleme Probleminin Hibrit Bir Yaklaşım ile Çözümü

Abstract

Bir yazılım proje çizelgesi, proje başarısını doğrudan etkileyen iki temel faktör olan proje süresini ve bütçesini izlemek için gereklidir. Görevler için insan kaynağı ataması, göreve başlama sırası, görev tamamlama süresi, göreve başlamada olası gecikmeler, harcanan para ve kalan bütçe, yazılım proje çizelgesi ile takip edilir. Bu çalışma, yazılım proje yöneticilerine, kaynak kısıtlı yazılım proje çizelgeleme problemini minimum proje süresi, minimum proje bütçesi ve minimum görev bekleme süresi ile çözmek için güçlü bir araç sağlamayı amaçlamaktadır. Bu amaçla, bu çalışmada Genetik Algoritmanın(GA) Bozkurt Optimizasyonu(BO), Yapay Arı Kolonisi Algoritması(YAKA) ve kaotik lojistik harita ile desteklendiği hibrit bir yaklaşım kullanılmıştır. GA'nın yakınsama başarısına katkıda bulunmak için BO'daki hiyerarşi modeli, yerel minimuma takılma sorunundan kaçınmak için YAKA’daki kâşif arı metodolojisi kullanılmıştır. Daha iyi rastgelelik sağlamak adına kaotik lojistik harita kullanılmıştır. Geliştirilen hibrit yöntem Akıllı Çok Amaçlı Proje Çizelgeleme Ortamı (Intelligent Multi Objective Project Scheduling Environment - iMOPSE)’de bulunan veri setleriyle test edilmiştir. Yöntemin sonuçları, literatürdeki yöntemler ile karşılaştırılmış ve parametrik olmayan testler kullanılarak istatistiksel olarak analiz edilmiştir. Test sonuçlarına göre, tekli insan kaynağı atama modelinde %7, çoklu insan kaynağı atama modelinde ise %15 oranına varan iyileşme gözlenmiştir. Sonuçlar, yönteminin çözüm kararlılığı ve optimum çözümlere yakınsama açısından iyi ve rekabetçi bir performansa sahip olduğunu göstermiştir.

Keywords

• Kaynak kısıtlı yazılım proje çizelgeleme problemi
• genetik algoritma
• bozkurt optimizasyonu
• yapay arı kolonisi algoritması
• lojistik harita

A hybrid approach for solving resource-constrained software project scheduling problem

Abstract

A software project schedule management tool is essential for monitoring the project duration and budget, two key factors that will directly affect project success. Assignment of personnel for tasks, initiation order of tasks, task completion time, possible delays in starting a task, and money already spent and the remaining budget are tracked via the software project tool. This study aims to provide software project managers with a powerful tool to solve the Resource-Constrained Software Project Scheduling Problem (RCSPSP) with minimum project duration, minimum project budget, and minimum waiting time of tasks. For this purpose, a hybrid approach is used in this study, in which the Genetic Algorithm (GA) is supported by Grey Wolf Optimization (GWO), Artificial Bee Colony Algorithm (ABC) and chaotic logistic map. The pack hierarchy model in GWO is used to contribute to the convergence success of GA, and scout bee methodology in ABC is adopted into the method to avoid being trapped at local minima. The chaotic logistic map technique is also used to improve randomness. The developed hybrid method has been tested with datasets in the Intelligent Multi-Objective Project Scheduling Environment (iMOPSE). The results are compared with in literature algorithms and statistically analyzed using non-parametrik tests. According to test results, an improvement of up to 7% in the one employee assignment model and up to 15% in the multiemployee assignment model has been observed. The results show that the method has good and competitive performance in terms of solution stability and closeness to optimal solutions.

Keywords

• Resource-constrained software project scheduling problem
• genetic algorithm
• grey wolf optimization
• artificial bee colony
• logistic map

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