Hybrid Optimization Method for Large-Scale Multimode Resource-Constrained Project Scheduling Problem

SÖNMEZ R., Gurel M.

JOURNAL OF MANAGEMENT IN ENGINEERING, cilt.32, sa.6, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 6
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1061/(asce)me.1943-5479.0000468
  • Dergi Adı: JOURNAL OF MANAGEMENT IN ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Project management, Resource management, Construction planning, Scheduling, Costs, Optimization, PARTICLE SWARM OPTIMIZATION, MULTIPLE EXECUTION MODES, ANT COLONY OPTIMIZATION, GENETIC ALGORITHM, RESTRICTIONS, SEARCH
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Despite many research efforts that have focused on the multimode resource-constrained project scheduling problem (MRCPSP), very little success has been achieved in solving the problem for large-scale projects. In this paper a new hybrid optimization method is presented to achieve an advancement in optimal planning and scheduling of large-scale construction projects with multiple duration/resource execution modes and resource constraints. The proposed method consists of a novel heuristic and unique genetic optimization algorithm. The heuristic is designed to achieve schedules with efficient resource utilizations through dynamic selection of modes. A genetic optimization algorithm is integrated to the proposed optimization method to further improve the quality of the solutions obtained in the heuristic phase. The computational experiment results reveal that the new hybrid method outperforms the state-of-the-art methods and achieves high-quality solutions in significantly less computation time, especially for large projects. The main contribution of the proposed hybrid optimization method is that it enables significant savings by optimal planning and scheduling of medium- and large-scale construction projects with multiple duration/resource execution modes and resource constraints.