Optimization model for construction project resource leveling using a novel modified symbiotic organisms search

Prayogo, Doddy; Cheng, Min‐Yuan; Wong, Foek Tjong; Tjandra, Daniel; Tran, Duc-Hoc

doi:10.1007/s42107-018-0048-x

Cited by 37 publications

(20 citation statements)

References 32 publications

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“…Initially developed by Cheng and Prayogo [10], the SOS algorithm took its inspiration from the symbiotic interactions among a group of organisms. Its initial application was to solve continuous optimization problems [10] and it has been used to solve various problems in multiple disciplines [19][20][21][22][23][24][25][26][27]. SOS utilized nature-inspired operators -the mutualism phase, commensalism phase, and parasitism phase -to guide the organisms (solutions) to the global optima region (best solution).…”

Section: Optimization Algorithm: Sosmentioning

confidence: 99%

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Prayogo

Susanto

2019

MATEC Web Conf.

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Pile foundations usually are used when the upper soil layers are soft clay and, hence, unable to support the structures’ loads. Piles are needed to carry these loads deep into the hard soil layer. Therefore, the safety and stability of pile-supported structures depends on the behavior of the piles. Additionally, an accurate prediction of the piles’ behavior is very important to ensure satisfactory performance of the structures. Although many methods in the literature estimate the settlement of the piles both theoretically and experimentally, methods for comprehensively predicting the load-settlement of piles are very limited. This study develops a new data mining approach called self-learning support vector machine (SL-SVM) to predict the load-settlement behavior of single piles. SL-SVM performance is investigated using 446 training data points and 53 test data points of cone penetration test (CPT) data obtained from the previous literature. The actual prediction accuracy is then compared to other prediction methods using three statistical measurements, including mean absolute error (MAE), coefficient of correlation (R), and root mean square error (RMSE). The obtained results show that SL-SVM achieves better accuracy than does LS-SVM and BPNN. This confirms the capability of the proposed data mining method to model the accurate load-settlement behavior of single piles through CPT data. The paper proposes beneficial insights for geotechnical engineers involved in estimating pile behavior.

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Section: Optimization Algorithm: Sosmentioning

confidence: 99%

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Prayogo

Susanto

2019

MATEC Web Conf.

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“…However, these approaches become impractical when solving large-scale construction projects. Later, heuristics methods were used [8,9], but they still do not produce optimal results [10]. The shortcomings of both mathematical and heuristic methods encouraged many researchers to study metaheuristic approaches in order to find more reliable optimization alternatives in solving resource leveling problems.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have used some type of metaheuristic method as an alternative to resource leveling, such as genetic algorithm (GA) [2,6], ant colony organization (ACO) [11], particle swarm optimization (PSO) [12], and differential evolution (DE) [10]. However, these methods each have their own drawbacks; one of them is much relying on parameter tuning too much.…”

Section: Introductionmentioning

confidence: 99%

Optimization of resource leveling problem under multiple objective criteria using a symbiotic organisms search

Prayogo

Kusuma²

2019

ced

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Bad scheduling and resource management can cause delays or cost overruns. Optimization in solving resource leveling is necessary to avoid those problems. Several objective criteria are used to solve resource leveling. Each of them has the same objective, which is to reduce the fluctuation of resource demand of the project. This study compares the performance of particle swarm optimization (PSO) and symbiotic organisms search (SOS) in solving resource leveling problems using separate objective functions in order to find which one produces a better solution. The results show that SOS produced a better solution than PSO, and one objective function is better in solving resource leveling than the others.

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“…Construction industry plays an indelible role in the rapid economic development [1]. In the construction process, whether building materials or labor force are disposable, different types of construction projects have different plans, and plans are also disposable.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Comprehensive Optimal Management of Construction Projects Based on Particle Algorithm

Wang¹

2019

IJCAI

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Construction industry is one of the pillars of rapid economic development. The optimization of construction project management can greatly optimize the cycle, cost and quality of projects. In this paper, the multi-objective management optimization model of construction projects and the particle swarm optimization (PSO) algorithm for calculating the optimal solution of the model are briefly introduced, genetic operators are introduced into the PSO algorithm to prevent premature phenomenon, so as to improve the accuracy of the solution, and then case analysis is performed on a single-storey building project. The results show that the algorithm converges to stability and obtains the optimum solution set after 400 times of iterations and a total of 63250 seconds. The construction period of each process of the solution with the shortest total construction period in the optimum solution set is shorter than that before optimization, the total construction period reduces by 56 days, the total cost reduces by 520,000 yuan, and the total quality increases by 3.58. In summary, the improved PSO algorithm can effectively optimize the management of construction projects. Povzetek: Predstavljen je izboljšani algoritem na osnovi delcev in večkriterijske optimizacije za izboljšanje vodenja gradbeniških projektov.

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Optimization model for construction project resource leveling using a novel modified symbiotic organisms search

Cited by 37 publications

References 32 publications

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Optimization of resource leveling problem under multiple objective criteria using a symbiotic organisms search

Multi-objective Comprehensive Optimal Management of Construction Projects Based on Particle Algorithm

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