Solving time cost optimization problem with adaptive multi-verse optimizer

Vu Hong Son Pham () and Nghiep Trinh Nguyen Dang ()
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Vu Hong Son Pham: Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM)
Nghiep Trinh Nguyen Dang: Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM)

OPSEARCH, 2024, vol. 61, issue 2, No 7, 662-679

Abstract: Abstract The construction industry holds a central role globally, marked by its unique attributes that lead to distinct challenges. Given projects in this sector are often tailored to specific needs, operate on a grand scale, showcase complex designs, and have limited adaptability, the industry frequently confronts the dual challenges of time and cost. In construction project management, a fundamental task is to harmonize these time and cost considerations effectively. The Multi-Verse Optimizer (MVO) algorithm has been identified as a potential tool to navigate the intricate search spaces typical of construction endeavors. However, MVO is not without its challenges, specifically its slower convergence rate and propensity for entrapment in local optima. In response to these challenges, this research introduces the Adaptive Multi-Verse Optimizer (aMVO) model. By integrating the Modified Adaptive Weight Approach (MAWA) with the MVO algorithm, the aMVO aims to bolster optimization efficiency. Centering on the complex domain of time-cost optimization problems (TCP), this study embarks on an in-depth evaluation of the aMVO against benchmarks covering scenarios with 18, 90, and 180 activities. The findings compellingly underscore the superiority of aMVO in navigating the complexities of TCP, particularly when contrasted with methods such as GA, MFO, SCA, DA, and ALO. This firmly positions aMVO as an indispensable tool in the domain of construction project management.

Keywords: Time cost optimization problem; Multiverse optimizer; Decision support systems; Project management; Modified adaptive weight approach (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12597-023-00737-x

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