Activity Uncrashing Heuristic with Noncritical Activity Rescheduling Method for the Discrete Time-Cost Trade-Off Problem

SÖNMEZ R., Aminbakhsh S., Atan T.

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, cilt.146, sa.8, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 146 Sayı: 8
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1061/(asce)co.1943-7862.0001870
  • Dergi Adı: JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, ICONDA Bibliographic, INSPEC, Metadex, Public Affairs Index, DIALNET, Civil Engineering Abstracts
  • Anahtar Kelimeler: Scheduling, Optimization, Algorithms, Multiple objective analysis, Project management, PARTICLE SWARM OPTIMIZATION, CONSTRUCTION TIME, EVOLUTIONARY ALGORITHMS, GENETIC ALGORITHMS
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Despite intensive research efforts that have been devoted to discrete time-cost optimization of construction projects, the current methods have very limited capabilities for solving the problem for real-life-sized projects. This study presents a new activity uncrashing heuristic with noncritical activity rescheduling method to narrow the gap between the research and practice for time-cost optimization. The uncrashing heuristic searches for new solutions by uncrashing the critical activities with the highest cost-slope. This novel feature of the proposed heuristic enables identification and elimination of the dominated solutions during the search procedure. Hence, the heuristic can determine new high-quality solutions based on the nondominated solutions. Furthermore, the proposed noncritical activity rescheduling method of the heuristic decreases the amount of scheduling calculations, and high-quality solutions are achieved within a short CPU time. Results of the computational experiments reveal that the new heuristic outperforms state-of-the-art methods significantly for large-scale single-objective cost minimization and Pareto front optimization problems. Hence, the primary contribution of the paper is a new heuristic method that can successfully achieve high-quality solutions for large-scale discrete time-cost optimization problems.