The Subcontractor Selection Practice using ANN-Multilayer

Fachrurrazi, Fachrurrazi; Husin, Saiful; Munirwansyah,; Husaini, Husaini

doi:10.14716/ijtech.v8i4.9490

Cited by 17 publications

(13 citation statements)

References 17 publications

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“…Trainlm syntax in MATLAB was applied to train the network, which used the Levenberg-Marquardt algorithm. This procedure was continued until reaching the minimum mean squared error (MSE), and the ANN outputs (i.e., the yields simulated by the model) and targets (i.e., the actual values) were stable (Fachrurrazi & Munirwansyah, 2017):…”

Section: Modeling Methodsmentioning

confidence: 99%

Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network

Sadighi¹,

Mohaddecy²,

Abbasi³

2018

IJTech

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In this research, based on actual data gathered from an industrial scale vacuum gas oil (VGO) hydrocracker and artificial neural network (ANN) method, a model is proposed to simulate yields of products including light gases, liquefied petroleum gas (LPG), light naphtha, heavy naphtha, kerosene, diesel and unconverted oil (off-test). The input layer of the ANN model consists of the catalyst, feed and recycle flow rates, and bed temperatures, while the output neurons are yields of those products. The results showed that the AAD% (average absolute deviation) of the developed ANN model for training, testing, and validating data are 0.445%, 1.131% and 0.755%, respectively. Then, by considering all operational constraints, the results confirmed that the decision variables (i.e., recycle rate and bed temperatures) generated by the optimization approach can enhance the gross profit of the hydrocracking process to more than $0.81 million annually, which is significant for the economy of the target refinery.

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Section: Modeling Methodsmentioning

confidence: 99%

Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network

Sadighi¹,

Mohaddecy²,

Abbasi³

2018

IJTech

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“…Although, there are many multicriteria decision analysis (MCDA) that use criteria weights as a basis for determining the ranking of alternatives (in this case, the variable rank of risk), as in the study of Fachrurrazi et al [39]. However, the study has not considered various expert judgments as for the linguistic variable and the linguistic alternative.…”

Section: Comparison To Other Researchmentioning

confidence: 99%

Implementing Fuzzy TOPSIS on Project Risk Variable Ranking

Husin

Fachrurrazi

Rizalihadi

et al. 2019

Advances in Civil Engineering

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Managing construction risks with a large number of risks with small impact can increase the additional effort and cost of inefficient construction. Therefore the variables need to be eliminated. The aim of this study is ranking the risk variable based on its frequency of occurrence by integrating time, cost, and quality criteria simultaneously and selecting the top ten variables with the order of the most significant impact. The risk variable ranking based on triple project objective of cost, time, and quality simultaneously is a challenge for particular projects or regions contributing to the risk context. A number of 127 qualitative risk variables of 14 factors occurring in a project to be eliminated require a method/technique. A fuzzy TOPSIS method involving linguistics data is proposed to capture vague conditions. Results show that the top ten rankings of risk variables based on integrating the different weights of cost, time, and quality are successfully identified by concluding that the labour factor is the most dominant variable affecting project risk in context the rehabilitation and reconstruction posttsunami disaster, especially in Aceh-Indonesia. The variables are lack of labour, unskilled labour, undisciplined labour, and low productivity of labour. This condition can differ from different risk contexts. This research is different from other studies that only review cost, time, and quality separately. We stated that to integrate all three criteria of cost, time, and quality simultaneously is more logic to analyze risk variable ranking.

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“…The potential risks that will affect the organization's objectives should be evaluated sustainable over the time and be compared mainly to the work area of the organization; this is done to optimize the output of the organization's objectives. The Contractors who have three primary goals as triple constraints (cost, quality and time) [1], need to assess the risk to maximize their goals. Risk management only needs to be addressed at the vital risk, without having to spend time and effort on less essential risks.…”

Section: Introductionmentioning

confidence: 99%

Project Risk Patterns: A Comparison Across Three Periods

Fachrurrazi¹,

Husin²,

Mahmuddin³

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Aceh has experienced the critical event in three periods (political conflict, tsunami disaster, regular period) which they are different in risk characteristics. These critical events have inflicted on the sectors of life, including the construction sector. Some risk variables have been identified from previous studies that will be applied and analysis in this area by using risk assessment theory. In this research, we focus on the project risks in the three periods that it has an impact on the loss of profit on the contractors in Aceh. The potential risk variable and its pattern will be analyzed crossover in all the three periods. Qualitative risk analysis is used to describe the project risk of contractors who have conduct project in those periods. The potential risk variable, unofficial levies charges by bullies (P11), is the most extreme than other risks variables in the 1st period, has decreased in subsequent periods (2nd period and third period). The risk variable of P11 is the most avoided by the contractor because it is very influential on the achievement of the project objectives. This P11 variable not only emerged during the political conflict period but also in the posttsunami period due to social, economic factors, although it has decreased. This P11 variable not only affects the risk singly but also could trigger the other variable, thus providing a double effect on project risks, such as increased costs for safety and security in project areas (P5) and Increased labor costs due to increased wages (P6). The result of risk analysis using qualitative risk analysis method with Risk Important Index (RII) shows that risk variable of P1 (Bureaucratic of the owner delays payment terms), P12 (Contractors shortage of funds caused by the cost overrun on the project), has experienced decreasing from 2nd period to 3rd period. However, the hypothesis results show that the decreasing is not significant. It indicates that this test is critical to include in risk analysis, especially on the method of qualitative risk analysis.

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The Subcontractor Selection Practice using ANN-Multilayer

Cited by 17 publications

References 17 publications

Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network

Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network

Implementing Fuzzy TOPSIS on Project Risk Variable Ranking

Project Risk Patterns: A Comparison Across Three Periods

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