scholarly journals Risk Response Selection in Construction Projects

2018 ◽  
Vol 3 (12) ◽  
pp. 1208 ◽  
Author(s):  
Hafeth I. Naji ◽  
Rouwaida Hussein Ali
Keyword(s):  
Risk Management ◽  
Optimal Strategy ◽  
Construction Projects ◽  
Response Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Response Strategy ◽  
Risk Response ◽  
Gravitational Search ◽  
The Cost

Risk and its management  is  important  for the success of the project, the  risk management, which encompassed of planning, identification, analysis, and response has an important phase, which is risk response  and it should not be undermined, as its  success going to  the projects  the capability  to overcome the  uncertainty and  thus an effective  tool in project risk management, risk response used the collective information in the analysis stage and in order  to take decision how to improve the possibility to complete the project within time, cost and performance. This stage work on preparing the response to the main risks and appoint the people who are responsible for each response.  When it's needed risk response may be started in quantitative analysis stage and the repetition may be possible between the analysis and risk response stage. The aim of this research is to provide a methodology to make the plane for unexpected events and control uncertain situations and identify the reason for risk response failure and to respond to risk successfully by using the optimization method to select the best strategy. The methodology of this research divided into four parts, the first part main object is to find the projects whose risk response is failed, the second part includes the reasons for risk response Failure, the third part includes   finding   the most important risks generated from risk response that leads to increasing the cost of construction projects, the fourth part of the management system is selecting the optimal risk response strategy. An optimization model was used to select the optimal strategy to treat the risk by using Serval constraints such as the cost of the project, time of the project, Gravitational Search Algorithm and particle swarm used. The result of the risk response selection shows that The investment (contractor, bank) strategy shows a very good strategy as it saves the cost about 30%, while the Mitigate (pay for advances with interest 0. 1) Strategy show saving the cost 40%   and giving land to contractors show saving the cost 40% finally the BIM strategy show saving the cost 25%. The risk response is an important part and should give a great attention and it must be used sophisticated method to select the optimal strategy, the two techniques both show high efficiency in selecting the strategy but Gravitational Search Algorithm show better performance.

Construction Projects Problems Optimization Using PSO and GSA

2020 ◽  
Vol 897 ◽  
pp. 147-151
Author(s):  
Naji Mutar Sahib ◽  
Abtehaj Hussein ◽  
Suha Falih ◽  
Hafeth I. Naji
Keyword(s):  
Expert System ◽  
Computer Science ◽  
Construction Projects ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Solution ◽  
Severe Effect ◽  
Good Technique ◽  
Gravitational Search

Construction projects are a combination of high complicated procedures that rarely go with the plan. The greatest dangers projects are the construction since it linked with an extraordinary amount of ambiguity and threat and that because of the business activities nature, procedures, and the outside surroundings. This paper investigates the problems during the pre-construction phase and the optimal solution for this problem by using to algorithm, partial swarm and Gravitational search algorithm. The results show that the construction problems have a severe effect on both time and cost and these problems must be treated immediately and this requires sophisticated techniques by using computer science. GSA and PSO are both used and show excellent results in solving these problems, the GSA algorithm shows better results in both the velocity is taken to find the solutions and in the accuracy. PSO is still a good technique in finding the solution and their future recommendation in making an expert system to find the solution more than one project and their interdependency.

scholarly journals APPLICATION OF THE GRAVITY SEARCH METHOD TO MINIMIZE THE COST OF CONDUCTING A MULTIFACTOR EXPERIMENT

2021 ◽  
pp. 42-48
Author(s):  
N.D. Koshevoy ◽  
A.V. Malkova
Keyword(s):  
Experimental Research ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Method ◽  
Search Method ◽  
Main Task ◽  
Multifactor Experiment ◽  
Gravitational Search ◽  
The Cost

Experimental research methods are increasingly used in industry in the optimization of production processes. Experiments, as a rule, are multifactorial and are connected with optimization of quality of materials, search of optimum conditions of carrying out technological processes, development of the most rational designs of the equipment, etc. The use of experimental planning makes the behavior of the experimenter purposeful and organized, significantly increases productivity and reliability of the results. An important advantage is its versatility, suitability in the vast majority of research areas. When implementing an industrial experiment, the main task is to obtain the maximum amount of useful information about the influence of individual factors of the production process, provided that the minimum number of expensive observations in the shortest period of time. Therefore, it is important to increase the efficiency of experimental research with minimal time and cost. For this purpose, it is expedient to develop systems of automation of experiments which will allow to reduce terms of carrying out experimental researches and to reduce expenses for them. Object of research: processes of optimization of plans of multifactor experiment on cost and time expenses. Subject of research: an optimization method developed on the basis of the gravitational search algorithm, which consists in comparing the force of gravity (cost) of the first row of the planning matrix of the experiment to the next rows of the matrix. In the study of photoelectric transducers of angular displacements, the efficiency and effectiveness of the gravitational search method were analyzed in comparison with previously developed methods: analysis of line permutations, particle swarm, taboo search. The cost of carrying out the experiment plan and the efficiency for solving optimization problems in comparison with the original plan and the implementation of the above methods are shown.

scholarly journals APPLICATION OF THE GRAVITY SEARCH METHOD TO MINIMIZE THE COST OF CONDUCTING A MULTIFACTOR EXPERIMENT

2020 ◽  
pp. 51-57
Author(s):  
N.D. Koshevoy ◽  
A.V. Malkova
Keyword(s):  
Experimental Research ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Method ◽  
Search Method ◽  
Main Task ◽  
Multifactor Experiment ◽  
Gravitational Search ◽  
The Cost

Experimental research methods are increasingly used in industry in the optimization of production processes. Experiments, as a rule, are multifactorial and are connected with optimization of quality of materials, search of optimum conditions of carrying out technological processes, development of the most rational designs of the equipment, etc. The use of experimental planning makes the behavior of the experimenter purposeful and organized, significantly increases productivity and reliability of the results. An important advantage is its versatility, suitability in the vast majority of research areas. When implementing an industrial experiment, the main task is to obtain the maximum amount of useful information about the influence of individual factors of the production process, provided that the minimum number of expensive observations in the shortest period of time. Therefore, it is important to increase the efficiency of experimental research with minimal time and cost. For this purpose, it is expedient to develop systems of automation of experiments which will allow to reduce terms of carrying out experimental researches and to reduce expenses for them. Object of research: processes of optimization of plans of multifactor experiment on cost and time expenses. Subject of research: an optimization method developed on the basis of the gravitational search algorithm, which consists in comparing the force of gravity (cost) of the first row of the planning matrix of the experiment to the next rows of the matrix. In the study of photoelectric transducers of angular displacements, the efficiency and effectiveness of the gravitational search method were analyzed in comparison with previously developed methods: analysis of line permutations, particle swarm, taboo search. The cost of carrying out the experiment plan and the efficiency for solving optimization problems in comparison with the original plan and the implementation of the above methods are shown.

Analysis of Optimal Power Flow Using Gravitational Search Algorithm

2016 ◽  
Vol 3 (4) ◽  
pp. 1-11
Author(s):  
M. Lakshmikantha Reddy ◽  
◽  
M. Ramprasad Reddy ◽  
V.C. Veera Reddy ◽  
◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Power ◽  
Gravitational Search

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

Sign in / Sign up

Export Citation Format

Share Document