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Solving large-scale discrete time-cost trade-off problem using hybrid multi-verse optimizer model.

Pham Vu Hong Son1, Nghiep Trinh Nguyen Dang2

  • 1Department of Construction Engineering and Management, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM), Ho Chi Minh City, Vietnam.

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This study introduces a hybrid multi-verse optimizer (hDMVO) for construction project time-cost optimization. The novel algorithm effectively solves discrete time-cost trade-off problems, outperforming existing methods on large-scale projects.

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Area of Science:

  • Construction Management
  • Optimization Algorithms
  • Computational Intelligence

Background:

  • Time-cost trade-off analysis is critical in construction project management.
  • Existing optimization techniques face challenges with discrete time-cost trade-off problems (DTCTP).

Purpose of the Study:

  • To introduce a novel hybrid multi-verse optimizer model (hDMVO) for DTCTP.
  • To evaluate the performance and competitiveness of hDMVO against established optimization algorithms.

Main Methods:

  • Developed a hybrid multi-verse optimizer model (hDMVO) by combining the multi-verse optimizer (MVO) and the sine cosine algorithm (SCA).
  • Validated the algorithm's optimality using 23 benchmark test functions.
  • Assessed hDMVO performance on four DTCTP benchmark problems, including medium (63 activities) and large-scale (630 activities) instances.

Main Results:

  • hDMVO demonstrated competitive performance against MVO, SCA, dragonfly algorithm, and ant lion optimization on benchmark functions.
  • hDMVO achieved superior solutions for time-cost optimization in large-scale and complex construction projects.
  • The algorithm proved effective in handling the discrete nature of time-cost trade-off problems.

Conclusions:

  • The proposed hDMVO is a powerful and efficient tool for discrete time-cost trade-off problems in construction.
  • hDMVO offers significant advantages for optimizing large-scale and complex construction projects.
  • This hybrid approach enhances the capability of metaheuristic algorithms in project management applications.