The Weights Detection of Multi-criteria by using Solver

Fachrurrazi, Fachrurrazi; Away, Yuwaldi; Husin, Saiful

doi:10.11591/ijece.v7i2.pp858-868

Cited by 7 publications

(8 citation statements)

References 11 publications

(20 reference statements)

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“…The use of the population in evolutionary multi-objective allows the Solver to have parallel searching ability to find multiple non-dominated solutions in a single iteration [13]. The Solver algorithms are based on a goal programming using the parameters of the objective function model (as target cell), constraints model (as subject to the constraints), and finding the value of variables (as changing cells) [24]. The framework for solving the PSC problem using the proposed MOINLP is described in the framework as shown in Figure 1.…”

Section: Methods 21 Methods Characteristicmentioning

confidence: 99%

Negative Total Float to Improve a Multi-Objective Integer Non-Linear Programming for Project Scheduling Compression

Fachrurrazi

Abdullah

Away

et al. 2018

IJECE

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This paper presents Multi-Objective Integer Non-Linear Programming (MOINLP) involving Negative Total Float (NTF) for improving the basic model of Multi-Objective Programming (MOP) in case the optimization of the additional cost for Project Scheduling Compression (PSC). Using the basic MOP to solve the more complex problems is a challenging task. We suspect that Negative Total Float (NTF) having an indication to make the basic MOP to solve the more general case, both simple and complex of PSC. The purpose of this research is identifying the conflicting objectives in PSC problem using NTF and improving MOINLP by involving the NTF parameter to solve the PSC problem. The Solver Application, which is an add-in of MS Excel, is used to perform optimization process to the model developed. The results show that NTF has an important role to identify the conflicting objectives in PSC. We define NTF is an automatic maximum value of the activity duration reduction to achieve due date of PSC. Furthermore, the use of NTF as a constraint in MOINLP can solve the more general case for both simple and complex PSC problem. Base on the condition, we state that the basic MOP is still significant to solve the PSC complex problems using MOINLP as a sophisticated MOP technique.

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Section: Methods 21 Methods Characteristicmentioning

confidence: 99%

Negative Total Float to Improve a Multi-Objective Integer Non-Linear Programming for Project Scheduling Compression

Fachrurrazi

Abdullah

Away

et al. 2018

IJECE

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“…Some of the alternative cases are connected to MCDM criteria weight problem using a decision matrix as Koulinas's research [28]. In MCDM of classical methods, the rankings of variable and the weights of the criteria are known precisely in the papers [15,19,25,27,29]. Implementation for only TOPSIS (Technique for Order of Performance by Similarity to Ideal Solution) has been known as the classical MCDM method, and it was first introduced by Hwang to solve an MCDM problem [24,30].…”

Section: Multiple Criteria Decision-making (Mcdm)mentioning

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 data, totaling 40 sets used in this research, are from previous research by Fachrurrazi et al (2017b). They consist of expert's judgment data, to be used as the target of ANN, and the director judgment of the main contractor.…”

Section: Datasetmentioning

confidence: 99%

“…Special thanks go to the reviewers for their great comments for improving this paper. Table 1 Dataset of input and target for training of the ANN model (Fachrurrazi et al, 2017b) Multi …”

Section: Acknowledgementmentioning

confidence: 99%

“…The performance criteria based on Fachrurrazi et al (2017b), show that the weight criteria vary between each assessor, as indicated by the standard deviation of the criterion weights. This is in line with the subcontractor selection practice, where the main contractor usually gives a freedom to determine the criterion weight within a specified range to the assessor.…”

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confidence: 99%

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The Subcontractor Selection Practice using ANN-Multilayer

Fachrurrazi¹,

Husin²,

Munirwansyah³

et al. 2017

IJTech

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The practice of subcontracting selection emphasizes two important goals: the company's strategic goal to maximize profits by partnering with subcontractors and the project's operational goal for obtaining qualified subcontractors. Both goals are achieved by formulating the best multi-criteria weights. This is not easy to implement due to differences in subjectivity, viewpoint, and other consideration of assessors, but prioritizing the criterion weights can reduce these differences. This study presents an ANN (Artificial Neural Network) with the ability to generalize data. The purpose of the study is to develop an ANN model for subcontracting selection and to identify significant criteria related to the company's strategic goal. The initial training of the proposed ANN model utilized 40 subcontractor selection datasets containing data in the form of a subcontractor selection scheme consisting of 20 criteria and 5 major groups. Training of ANN model was successful with MSE learning at 1.37269e -7 , MSE validation at 0.07985, and epoch 600 to 800. The quotation price is the significant criterion of the selection, and it has a great outcome for the contractor strategic goal. The interaction between the subcontractor selection practice and the ANN model shows that the ANN has an important role in the subcontractor selection practice.

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The Weights Detection of Multi-criteria by using Solver

Cited by 7 publications

References 11 publications

Negative Total Float to Improve a Multi-Objective Integer Non-Linear Programming for Project Scheduling Compression

Negative Total Float to Improve a Multi-Objective Integer Non-Linear Programming for Project Scheduling Compression

Implementing Fuzzy TOPSIS on Project Risk Variable Ranking

The Subcontractor Selection Practice using ANN-Multilayer

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