Optimization of Construction Duration and Schedule Robustness Based on Hybrid Grey Wolf Optimizer with Sine Cosine Algorithm

Zhao, Mengqi; Wang, Xiaoling; Yu, Jia; Bi, Lei; Xiao, Yao; Zhang, Jun

doi:10.3390/en13010215

Cited by 13 publications

(6 citation statements)

References 52 publications

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“…Given the above analysis, it can be concluded that GWO, the NBA, and the WOA can find the best solutions, and there is no statistically significant difference between the quality of their solutions. Given the relatively new advent of these three metaheuristics (i.e., GWO, NBA, and WOA), few studies have reported their efficiency in the solution of different construction planning problems such as the optimization of construction duration and schedule robustness using GWO [126], and the construction stage and zone optimization of Rockfill dams using the WOA [127]. On the other hand, the FA generates less optimal solutions than those produced by the others, while the SSA provides the worst results.…”

Section: Statistical Results Of the Si Metaheuristicsmentioning

confidence: 99%

Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

et al. 2021

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Off-site construction is a modern construction method that brings many sustainability merits to the built environment. However, the sub-optimal planning decisions (e.g., resource allocation, logistics and overtime planning decisions) of off-site construction projects can easily wipe away their sustainability merits. Therefore, simulation modelling—an efficient tool to consider the complexity and uncertainty of these projects—is integrated with metaheuristics, developing a simulation-optimization model to find the best possible planning decisions. Recent swarm intelligence metaheuristics have been used to solve various complex optimization problems. However, their potential for solving the simulation-optimization problems of construction projects has not been investigated. This research contributes by investigating the status-quo of simulation-optimization models in the construction field and comparing the performance of five recent swarm intelligence metaheuristics to solve the stochastic time–cost trade-off problem with the aid of parallel computing and a variance reduction technique to reduce the computation time. These five metaheuristics include the firefly algorithm, grey wolf optimization, the whale optimization algorithm, the salp swarm algorithm, and one improved version of the well-known bat algorithm. The literature analysis of the simulation-optimization models in the construction field shows that: (1) discrete-event simulation is the most-used simulation method in these models, (2) most studies applied genetic algorithms, and (3) very few studies used computation time reduction techniques, although the simulation-optimization models are computationally expensive. The five selected swarm intelligence metaheuristics were applied to a case study of a bridge deck construction project using the off-site construction method. The results further show that grey wolf optimization and the improved bat algorithm are superior to the firefly, whale optimization, and salp swarm algorithms in terms of the obtained solutions’ quality and convergence behaviour. Finally, the use of parallel computing and a variance reduction technique reduces the average computation time of the simulation-optimization models by about 87.0%. This study is a step towards the optimum planning of off-site construction projects in order to maintain their sustainability advantages.

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Section: Statistical Results Of the Si Metaheuristicsmentioning

confidence: 99%

Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

et al. 2021

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“…Construction duration T and schedule robustness, which is measured using the composite robustness criterion using the weighting methods proposed in this paper and the traditional weighting methods, respectively, are the two optimization objectives. A hybrid grey wolf optimizer with a sine-cosine algorithm is adopted to solve the optimization model [50]. The iteration number is set as 1000.…”

Section: Comparison Of Weighting Methodsmentioning

confidence: 99%

A Construction Schedule Robustness Measure Based on Improved Prospect Theory and the Copula-CRITIC Method

Zhao

Wang

et al. 2020

Applied Sciences

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A robustness measure is an effective tool to evaluate the anti-interference capacity of the construction schedule. However, most research focuses on solution robustness or quality robustness, and few consider a composite robustness criterion, neglecting the bounded rationality of subjective weights and inherent importance and nonlinear intercriteria correlations of objective weights. Therefore, a construction schedule robustness measure based on improved prospect theory and the Copula-criteria importance through intercriteria correlation (CRITIC) method is proposed. Firstly, a composite robustness criterion is established, including start time deviation rs and structural deviation rp for measuring solution robustness from project execution and completion probability rc for measuring quality robustness from the project result. Secondly, bounded rationality is considered, using prospect theory to calculate subjective weights, which is improved by the interval distance formula. Thirdly, the Copula-CRITIC method is proposed to determine objective weights incorporating both inherent importance and nonlinear intercriteria correlations. Finally, an information-entropy-based evidence reasoning method is applied to combine subjective and objective weights together while identifying their validity. An underground power station in China is used for a case study, whose robustness is measured using the proposed methods, single robustness criterion, and composite robustness criterion using traditional weighting methods. The comparison results verify the consistency, representativeness, and advantage of the proposed criterion and methods.

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“…To explain the influence of weather conditions on the simulation activity precedence logic and the role of construction activity sequence classification in simulations, a criterion denoted as the structural deviation (SD) is adopted to describe the consistency between the simulation activity logic relationship and the actual situation (Zhao et al, 2020). SD is the absolute value of the weighted summation of the deviations between the actual and simulation activities during each simulation cycle:

SD = {false\sum}_{i = 0}^{n} w_{i} ((||, P_{i} goodbreak- p_{i}))

where P i denotes the sequence of the actual construction activities, p i denotes the simulation activity sequence and w i denotes the time of the i‐ th activity as a percentage of the total cycle time.…”

Section: Case Studymentioning

confidence: 99%

Enhanced semi‐supervised ensemble machine learning approach for earthwork construction simulation activity sequence automatically updating driven by weather data

Zhang

Peng

et al. 2022

Geological Journal

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Construction activity sequence changes are among the most crucial considerations in establishing construction simulation models. However, conventional simulation models are designed with a fixed sequence of simulation activities, which is unable to update automatically. Existing machine learning methods require abundant time for manual labelling and are unsuitable for construction data with high‐dimensional and heterogeneous characteristics. The motivation for this work is to develop a labour‐saving activity sequence classification model for earthwork construction simulation which realizes flexibly modifying simulation activities according to different weather conditions. Three heterogeneous semi‐supervised classifiers with complementary characteristics were ensembled to reduce the workload of manual labelling and enhance the generalization ability of activity sequence classification based on weather data. Furthermore, Dempster–Shafer‐based evidence reasoning improved by a security filtering mechanism was adopted to enhance the accuracy of semi‐supervised classification. The proposed enhanced ensemble semi‐supervised activity sequence classification model was embedded in an earthwork construction simulation model, which was evaluated in a case study of rockfill dam construction. The proposed classifier outperformed four common semi‐supervised methods and two common supervised methods in terms of accuracy and generalization ability. Additionally, the proposed simulation method outperformed conventional simulation methods in terms of the construction schedule, construction intensity and consistency of the simulated activity sequence with the true values by 65.55%, 28.47% and 88.15%, respectively.

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Optimization of Construction Duration and Schedule Robustness Based on Hybrid Grey Wolf Optimizer with Sine Cosine Algorithm

Cited by 13 publications

References 52 publications

Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

A Construction Schedule Robustness Measure Based on Improved Prospect Theory and the Copula-CRITIC Method

Enhanced semi‐supervised ensemble machine learning approach for earthwork construction simulation activity sequence automatically updating driven by weather data

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