An applicable method for modifying over-allocated multi-mode resource constraint schedules in the presence of preemptive resources

Delgoshaei, Aidin; Rabczuk, Timon; Ali, Ahad; Ariffin, Mohd Khairol Anuar Bin Mohd

doi:10.1007/s10479-016-2336-8

Cited by 15 publications

(18 citation statements)

References 39 publications

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“…Delgoshaei et al [53] presented a new forward method to solve MRCPSP-PCF. In this study, the authors proposed an optimization model that maximizes the net present value (NPV) of a manufacturing project.…”

Section: Integration Of Special Solving Methods and Pmtmentioning

confidence: 99%

A Survey on Integration of Optimization and Project Management Tools for Sustainable Construction Scheduling

2020

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Construction scheduling, in practice, commonly relies on the usage of commercial project management tools (PMT) without specific optimization features. To obtain optimal schedules, planners often need to develop separate optimization models with special tools, which, however, demand further processing and editing of optimization results by PMT into forms expected for project management. In this regard, separation of optimization and PMT also requires considerable additional work for complete and harmonized updating of schedules during construction execution. Mentioned drawbacks and lack of available time may take to deficient construction scheduling during the implementation phase resulting in poor or even insufficient realization of project goals. Therefore, this paper presents an achievements survey on the integration of optimization and PMT that allow sustainable construction scheduling, particularly in terms of continuous optimal time and resource allocation throughout the project life cycle. Such work has not yet been comprehensively done up to now and the present contribution intends to fill a literature gap in the aforesaid area. Following a brief introduction, the optimization platform for construction scheduling is given in the article. Focusing on construction scheduling, an in-depth achievements survey on the integration of heuristics methods, mathematical programming and special solving methods with conventional PMT as well as optimization-based building information modeling (BIM) tools is then performed and findings are reported. The paper ends with conclusions and recommendations for further research.

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Section: Integration Of Special Solving Methods and Pmtmentioning

confidence: 99%

A Survey on Integration of Optimization and Project Management Tools for Sustainable Construction Scheduling

2020

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“…We determined the number of neurons in the hidden layer and the percentage of the training and test data using a simulated annealing algorithm to reduce the computational time. Simulated Annealing (SA) is a stochastic algorithm for estimating the optimum value of a given function [37]. This method is inspired by the slow cooling of metals.…”

Section: Yield Strengths Ofmentioning

confidence: 99%

Straightforward Prediction for Responses of the Concrete Shear Wall Buildings Subject to Ground Motions Using Machine Learning Algorithms

Barkhordari

Es-haghi

2021

IJE

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The prediction of responses of the reinforced concrete shear walls subject to strong ground motions is critical in designing, assessing, and deciding the recovery strategies. This study evaluates the ability of regression models and a hybrid technique (ANN-SA model), the Artificial Neural Network (ANN), and Simulated Annealing (SA), to predict responses of the reinforced concrete shear walls subject to strong ground motions. To this end, four buildings (15, 20, 25, and 30-story) with concrete shear walls were analyzed in OpenSees.150 seismic records are used to generate a comprehensive database of input (characteristics of records) and output (responses). The maximum acceleration, maximum velocity, and earthquake characteristics are used as predictors. Different machine learning models are used, and the accuracy of the models in identifying the responses of the shear walls is compared. The sensitivity of input variables to the seismic demand model is investigated. It has been seen from the results that the ANN-SA model has reasonable accuracy in the prediction.

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“…The results indicate that the proposed suspension can easily adopt its control parameters to obtain a better compromise of performance than passive methods. Delgoshaei et al (2017) proposed a supervisod method to rapid analyzing the different types of input information. An adaptive backstepping controller is designed by Sun et al (2012a) for active suspension method in the presence of parameter uncertainties to stabilize the attitude of vehicle and also improving ride comfort.…”

Section: Active Semi Active and Passive Suspension Systemsmentioning

confidence: 99%

A new method to improve passenger vehicle safety using intelligent functions in active suspension system

Rezanoori¹,

Ariffin²,

Delgoshaei³

et al. 2019

10.5267/j.esm

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In this research a new electronic based mechanism for vehicle suspension system is designd. The aims are to improve passengers' safety and comfort. The proposed system is developed for proactive rapid reaction of suspension system which can readjust the height of chassis while confronting with wrong conditions of driving such as unflatted road, rainy or snowy road profile. The results show that the proposed mechanism can successfully increase the stability of the car by readjusting the height of the the chassis and center of the gravity of vehicle while turning.

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An applicable method for modifying over-allocated multi-mode resource constraint schedules in the presence of preemptive resources

Cited by 15 publications

References 39 publications

A Survey on Integration of Optimization and Project Management Tools for Sustainable Construction Scheduling

A Survey on Integration of Optimization and Project Management Tools for Sustainable Construction Scheduling

Straightforward Prediction for Responses of the Concrete Shear Wall Buildings Subject to Ground Motions Using Machine Learning Algorithms

A new method to improve passenger vehicle safety using intelligent functions in active suspension system

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