Alt Yüklenici Seçimi için Altın Oran Esinli Karşıt Rao-1 Algoritması Kullanılarak Zaman–Maliyet–Kalite Ödünleşim Optimizasyonu

Abstract

İnşaat projeleri genellikle alt yüklenicilere atanan iş paketlerine bölünürken, genel yükleniciler proje genelinde kalite ve zamanında tamamlama sorumluluğunu üstlenmektedir. Erken proje aşamasında her bir iş paketi için uygun alt yüklenicilerin seçilmesi; zaman, maliyet ve kalite hedeflerinin eş zamanlı olarak değerlendirilmesini gerektiren kritik bir zorluktur. Bu çalışma, alt yüklenici seçiminde bu ödünleşimleri açık biçimde ele alan çok amaçlı bir optimizasyon çerçevesi önermektedir. Bu kapsamda, standart Rao-1 algoritmasına altın oran tabanlı karşıt öğrenmenin entegre edilmesiyle yakınsama davranışını ve çözüm kalitesini artıran altın oran tabanlı karşıt Rao-1 (GROL-Rao-1) adlı yeni bir algoritma geliştirilmiştir. Önerilen yöntemin performansı; ceza, prim ve teslim tarihi kısıtlarını içeren 20 faaliyetli gerçek bir proje vaka çalışması üzerinden doğrulanmıştır. Karşılaştırmalı sonuçlar, GROL-Rao-1’in ayrık parçacık sürü optimizasyonu, dinamik karşıt Rao-1, NSGA-II ve NDSII-Rao-1 yöntemlerine kıyasla üstün performans sergilediğini ve hesaplama yükünü yaklaşık %50 oranında azalttığını göstermektedir. Algoritma performansı; amaç fonksiyonu değerlendirme sayısı, hiperhacim ve yayılım göstergeleri kullanılarak değerlendirilmiş ve istatistiksel anlamlılık testleri ile desteklenmiştir. Ayrıca, Pareto-optimal çözümleri önceliklendirmek ve karar vericilerin dengeli proje planlarını belirlemesine yardımcı olmak amacıyla kalabalık mesafe sıralama yöntemi uygulanmıştır. Önerilen çerçeve, erken aşama karar verme süreçlerine etkin destek sağlamakta ve inşaat projelerinde kaynak tahsisi ile planlama sağlamlığını önemli ölçüde artırmaktadır.effective support for early-stage decision-making and improves resource allocation and planning robustness in construction projects.

Keywords

• Çok amaçlı Rao-1 algoritması
• Altın oran tabanlı karşıt öğrenme
• İnşaat yönetimi
• Zaman–maliyet–kalite ödünleşim problemi
• Çok kriterli karar verme analizi

Time Cost–Quality Trade-Off Optimization for Subcontractor Selection Using a Golden Ratio-Inspired Oppositional Rao-1 Algorithm

Abstract

Construction projects are typically divided into tasks assigned to subcontractors, while general contractors retain responsibility for overall quality and timely completion. A critical challenge lies in selecting appropriate subcontractors for each work package at the early project stage, where time, cost, and quality objectives must be evaluated simultaneously. This study proposes a multi-objective optimization framework for subcontractor selection that explicitly addresses these trade-offs. A novel algorithm, termed the golden ratio-based oppositional Rao-1 (GROL-Rao-1), is developed by integrating golden ratio–based opposition learning into the standard Rao-1 algorithm to enhance convergence behavior and solution quality. The performance of the proposed method is validated through a real-world case study involving 20 project activities with penalties, bonuses, and deadline constraints. Comparative results demonstrate that GROL-Rao-1 outperforms discrete particle swarm optimization, dynamic oppositional Rao-1, NSGA-II, and NDSII-Rao-1, while reducing computational effort by approximately 50%. Algorithm performance is evaluated using objective function evaluations, hypervolume, and spread indicators, supported by statistical significance testing. In addition, the crowded distance ranking method is applied to prioritize Pareto-optimal solutions and assist decision-makers in identifying balanced project plans. The proposed framework provides effective support for early-stage decision-making and improves resource allocation and planning robustness in construction projects.

Keywords

• Multi-objective Rao-1 algorithm
• Golden ratio-based oppositional learning
• Construction management
• Time-cost-quality trade-off problem
• multi-criteria decision-making analysis

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