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

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 215 ◽  
Author(s):  
Mengqi Zhao ◽  
Xiaoling Wang ◽  
Jia Yu ◽  
Lei Bi ◽  
Yao Xiao ◽  
...  
Keyword(s):  
Optimization Model ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Power Station ◽  
Starting Time ◽  
Proposed Model ◽  
Efficient Construction ◽  
Sine Cosine Algorithm ◽  
Critical Chain

Construction duration and schedule robustness are of great importance to ensure efficient construction. However, the current literature has neglected the importance of schedule robustness. Relatively little attention has been paid to schedule robustness via deviation of an activity’s starting time, which does not consider schedule robustness via structural deviation caused by the logical relationships among activities. This leads to a possibility of deviation between the planned schedule and the actual situation. Thus, an optimization model of construction duration and schedule robustness is proposed to solve this problem. Firstly, duration and two robustness criteria including starting time deviation and structural deviation were selected as the optimization objectives. Secondly, critical chain method and starting time criticality (STC) method were adopted to allocate buffers to the schedule in order to generate alternative schedules for optimization. Thirdly, hybrid grey wolf optimizer with sine cosine algorithm (HGWOSCA) was proposed to solve the optimization model. The movement directions and speed of grey wolf optimizer (GWO) was improved by sine cosine algorithm (SCA) so that the algorithm’s performance of convergence, diversity, accuracy, and distribution improved. Finally, an underground power station in China was used for a case study, by which the applicability and advantages of the proposed model were proved.

scholarly journals A Cost Optimization Model for Multiresource Leveling Problem without Project Duration Constraint

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qingyou Yan ◽  
Qian Zhang ◽  
Xin Zou
Keyword(s):  
Optimization Model ◽  
Cost Optimization ◽  
Resource Usage ◽  
Project Duration ◽  
Resource Demand ◽  
Resource Leveling ◽  
Proposed Model ◽  
Liquidated Damages ◽  
The Relationship

The study of traditional resource leveling problem aims at minimizing the resource usage fluctuations and obtaining sustainable resource supplement, which is accomplished by adjusting noncritical activities within their start and finish time. However, there exist limitations in terms of the traditional resource leveling problem based on the fixed project duration. This paper assumes that the duration can be changed in a certain range and then analyzes the relationship between the scarce resource usage fluctuations and project cost. This paper proposes an optimization model for the multiresource leveling problem. We take into consideration five kinds of cost: the extra hire cost when the resource demand is greater than the resource available amount, the idle cost of resource when the resource available amount is greater than the resource demand, the indirect cost related to the duration, the liquidated damages when the project duration is extended, and the incentive fee when the project duration is reduced. The optimal objective of this model is to minimize the sum of the aforementioned five kinds of cost. Finally, a case study is examined to highlight the characteristic of the proposed model at the end of this paper.

scholarly journals Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3500 ◽  
Author(s):  
Bishwajit Dey ◽  
Fausto Pedro García Márquez ◽  
Sourav Kr. Basak
Keyword(s):  
Low Voltage ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Energy Resources ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Renewable Energy Resources ◽  
Generation Cost ◽  
Sine Cosine Algorithm ◽  
The Impact

Optimal scheduling of distributed energy resources (DERs) of a low-voltage utility-connected microgrid system is studied in this paper. DERs include both dispatchable fossil-fueled generators and non-dispatchable renewable energy resources. Various real constraints associated with adjustable loads, charging/discharging limitations of battery, and the start-up/shut-down time of the dispatchable DERs are considered during the scheduling process. Adjustable loads are assumed to the residential loads which either operates throughout the day or for a particular period during the day. The impact of these loads on the generation cost of the microgrid system is studied. A novel hybrid approach considers the grey wolf optimizer (GWO), sine cosine algorithm (SCA), and crow search algorithm (CSA) to minimize the overall generation cost of the microgrid system. It has been found that the generation costs rise 50% when the residential loads were included along with the fixed loads. Active participation of the utility incurred 9–17% savings in the system generation cost compared to the cases when the microgrid was operating in islanded mode. Finally, statistical analysis has been employed to validate the proposed hybrid Modified Grey Wolf Optimization-Sine Cosine Algorithm-Crow Search Algorithm (MGWOSCACSA) over other algorithms used.

scholarly journals Collaborative Strategy for Grey Wolf Optimization Algorithm

2018 ◽  
Vol 12 (7) ◽  
pp. 73 ◽  
Author(s):  
Esra F. Alzaghoul ◽  
Sandi N. Fakhouri
Keyword(s):  
Optimization Algorithm ◽  
State Of The Art ◽  
Particle Swarm ◽  
Search Space ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Particle Swarm Optimizer ◽  
Minimal Points ◽  
Search Feature ◽  
Sine Cosine Algorithm

Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar particle swarm optimizer, sine cosine Algorithm (SCA), multi-verse optimizer (MVO), supernova optimizer as well as particle swarm optimizer (PSO). The results show that the proposed algorithm enhanced GWO behaviour for reaching the best solution and showed competitive results that outperformed the compared meta-heuristics over the tested benchmarked functions.

scholarly journals An optimization model to solve the resource constrained project scheduling problem RCPSP in new product development projects•

DYNA ◽  
2020 ◽  
Vol 87 (212) ◽  
pp. 179-188 ◽  
Author(s):  
Néstor Raúl Ortíz Pimiento ◽  
Francisco Javier Diaz Serna
Keyword(s):  
Product Development ◽  
New Product Development ◽  
Optimization Model ◽  
New Product ◽  
Development Projects ◽  
Analysis Process ◽  
Proposed Model ◽  
Project Scheduling Problem ◽  
The Impact

New product development projects (NPDP) face different risks that may affect the scheduling. In this article, the purpose was to develop an optimization model to solve the RCPSP in NPDP and obtain a robust baseline for the project. The proposed model includes three stages: the identification of the project’s risks, an estimation of activities’ duration, and the resolution of an integer linear program. Two versions of the model were designed and compared in order to select the best one. The first version uses a method to estimate the activities’ duration based on the expected value of the impact of the risks and the second version uses a method based on the judgmental risk analysis process. Finally, the two version of the model were applied to a case study and the best version of the model was identified using a robustness indicator that analyses the start times of the baselines generated.

scholarly journals Unified Framework for Optimal Routing Choice under Guidance Information

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Zhi-yuan Sun ◽  
Yue Li ◽  
Wen-cong Qu ◽  
Tanveer Muhammad
Keyword(s):  
Big Data ◽  
Optimization Model ◽  
Optimal Routing ◽  
Unified Framework ◽  
Proposed Model ◽  
Travel Mode Choice ◽  
Departure Time Choice ◽  
Method Of Successive Averages ◽  
Guidance Information

In order to satisfy the diverse demand of travel service in the context of big data, this paper puts forward a unified framework for optimal routing choice under guidance information. With consideration of the influence of big data, the scenario analysis of routing choice is implemented, and the routing choice under guidance information is discussed. The optimal routing choice problem is abstracted into the collaboration optimization model of travel route choice, departure time choice, and travel mode choice. Based on some basic assumptions, the collaboration optimization model is formulated as a variational inequality model. The method of successive averages is applied to solve the proposed model. A case study is carried out to verify the applicability and reliability of the model and algorithm.

scholarly journals A Queuing Network Based Optimization Model for Calculating Capacity of Subway Station

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Hanchuan Pan ◽  
Zhigang Liu
Keyword(s):  
Optimization Model ◽  
Subway Station ◽  
Queuing Model ◽  
Selection Probability ◽  
Evaluation Indexes ◽  
State Dependent ◽  
Decision Variables ◽  
Proposed Model ◽  
Shanghai Metro

Capacity of subway station is an important factor to ensure the safety and improve the transportation efficiency. In this paper, based on the M/G/C/C state-dependent queuing model, a probabilistic selection optimization model is proposed to assess the capacity of the station. The goal of the model is to maximize the output rate of the station, and the decision variables of the model are the selection results of the passengers. Finally, this paper takes a subway station of Shanghai Metro as a case study and calculates the optimal selection probability. The proposed model could be used to analyze the average waiting time, congestion probability, and other evaluation indexes; at the same time, it verifies the validity and practicability of the model.

scholarly journals Optimal, Reliable and Cost-Effective Framework of Photovoltaic-Wind-Battery Energy System Design Considering Outage Concept Using Grey Wolf Optimizer Algorithm—Case Study for Iran

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 182611-182623 ◽  
Author(s):  
Amirreza Naderipour ◽  
Zulkurnain Abdul-Malek ◽  
Masoud Zahedi Vahid ◽  
Zahra Mirzaei Seifabad ◽  
Mohammad Hajivand ◽  
...  
Keyword(s):  
System Design ◽  
Cost Effective ◽  
Energy System ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Battery Energy

scholarly journals Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 950 ◽  
Author(s):  
Jianguo Zhou ◽  
Xuejing Huo ◽  
Xiaolei Xu ◽  
Yushuo Li
Keyword(s):  
Extreme Point ◽  
Extreme Learning Machine ◽  
Carbon Price ◽  
Grey Wolf Optimizer ◽  
Symmetric Mode ◽  
Grey Wolf ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Learning Machine

Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

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