Risk analysis model for construction projects using fuzzy logic

The construction industry project is more subjective and risky compared with the others industries because of the unique characteristics of construction activities such as poor working condition, the significant frequency of accidents and the occupational risky situation. Risk analysis and management on the project sites is the first key to achieve adequate level of security. However, the modern construction and the new sophisticated design have shown a significant obstacles and uncertainties to complete the project safely; thereby it is inevitable to search a new approach to deal with uncertainties. The ability of a fuzzy system to deliver its reasoning process is presented to have absolute result within the field of risk analysis. As well as, fuzzy set theory is mainly subjective and associated to deal with inexact and vague information in construction projects. This paper describes the stages of the fuzzy risk analysis model which is developed to assess the risks related with construction projects and their uncertainties based on evaluations of cost, time and quality. Ultimately, using this model we can prioritize and rank all risk factors cited in the construction project; besides that we can easily manage them in the best appropriate way

Multivariate Control Chart based on Neutrosophic Hotelling T2 Statistics and Its Application

Under classical statistics Hotelling 〖 T〗^2 control chart is applied when the observations of quality characteristics are precise, exact, or crips data. However, in reality, under uncertain conditions, the observations are not necessarily precise, exact, or indeterminacy. As a consequence, the classical Hotelling〖 T〗^2control chart is not appropriate to monitor the process for this condition. To tackle this situation, we proposed new Hotelling 〖 T〗^2 monitoring scheme based on a fuzzy neutrosophic concept. Neutrosophic is the generalization of fuzzy. It is used to handle uncertainty using indeterminacy. The combination of statistics based on neutrosophic Hotelling 〖 T〗^2 and classical Hotelling 〖 T〗^2 control chart will be proposed to tackle indeterminacy observations. The proposed Hotelling 〖 T〗^2 statistics, its call neutrosophic Hotelling 〖 T〗^2 (T_N^2 ) control chart. This chart involves the indeterminacy of observations, its call neutrosophic data and will be expressed in the indeterminacy interval. T_N^2 control charts consist T_N^2 lower chart and T_N^2 upper chart. In this paper, the neutrosophic Hotelling T^2will be applied to individual observations of glass production and will be compared by using classical Hotelling T^2 control chart. Based on T_N^2 control charts of glass production, nine points fall outside of 〖UCL〗_N of lower control chart and 24 points outside from 〖UCL〗_N  of upper control chart. Whereas using classical Hotelling T^2 control chart, just one point outside frim UCL. From the comparison, it concluded that the neutrosophic Hotelling T^2 control chart is more suitable for the indeterminacy of observations

Fuzzy logic applied to system control to enhance commercial appliance performance

The purpose of this research is to determine the usefulness of fuzzy logic and fuzzy control when applied to a commercial appliance. Fuzzy logic is a structured, model-free estimator that approximates a function through linguistic input/output associations. Fuzzy rule-based systems apply these methods to solve many types of real-world problems, especially where a system is difficult to model, is controlled by a human operator or expert, or where ambiguity or vagueness is common. This dissertation presents fuzzy sets, fuzzy systems, and fuzzy control, with an example conveying the use of fuzzy control of a consumer product and an overview of fuzzy logic in the field of artificial intelligence. Ultimately, it demonstrates that the use of fuzzy systems makes a viable addition to the field of artificial intelligence and, perhaps, more generally to the application of other consumer products to reduce energy consumption and increase the ease of operation. Topics such as classical logic, set theory, fuzzy set theory, and fuzzy mathematics are developed in this research to provide a foundation in fuzzy logic. Fuzzy logic is an excellent development of a basic home appliance to provide a powerful and user-friendly device. Fuzzy logic allows an engineer without a great knowledge of control systems and mathematical modeling a viable alternative in product creation. The fuzzy logic toolbox of the program MATLAB\sp{\rm TM} developed by The Mathworks Corporation is used to build and test the fuzzy logic systems explored by this dissertation. Again, in this dissertation the concept of fuzzy logic shall be explored in detail. Background and theoretical information shall be derived to provide a good base for applications. Classical logic, crisp sets, fuzzy sets, and operations on fuzzy sets are explained in order to cover a wide spectrum of applications. The focus or cumulating point will be to apply the fuzzy logic principle to any type of consumer appliance (such as a washing machine). The use of fuzzy logic will allow many household goods to be manufactured more quickly and with more options, and be energy efficient, user friendly, and cost effective

Fuzzy Univariate Control Chart untuk Monitoring Kualitas Ketebalan Lem Labelstock di PT "XYZ"

Perkembangan industri sektor barang dan jasa terus bertambah seiring perkembangan peradaban. Oleh karena itu perusahaan berlomba-lomba menghasilkan produk dengan kualitas yang baik. PT “XYZ” merupakan salah satu perusahaan yang bergerak dalam bidang pembuatan labelstock, release liner, dan adhesive tape. Dalam proses produksinya, PT “XYZ” berupaya untuk terus menjaga kualitas sehingga dapat meng-hasilkan produk bernilai tinggi sesuai permintaan pelanggan. Kualitas produksi labelstock dapat diukur dari daya rekat lem yang digunakan. Hal tersebut tentunya dipengaruhi oleh ketebalan lem pada labelstock. Pada proses produksinya, PT “XYZ” melakukan pengukuran ketebalan lem pada tiga titik pengamat-an, yaitu dari sisi kanan, kiri, dan tengah. Perbedaan hasil pengukuran menimbulkan adanya ambiguitas sehingga perlu dilakukan pengendalian kualitas dengan metode yang tepat yaitu peta kendali fuzzy. Peta kendali fuzzy merupakan penggabungan dari teori fuzzy dan peta kendali. Peta kendali fuzzy yang digunakan dalam penelitian adalah peta kendali Fuzzy X ̅ ̃-R ̃ dan Fuzzy Exponentially Weighted Moving Average (FEWMA). Hasil analisis didapatkan peta kendali FEWMA lebih sensitif dalam mendeteksi pergeseran proses dibandingkan peta kendali fuzzy X ̅ ̃-R ̃. Pada peta kendali FEWMA didapatkan nilai pembobot optimum yaitu = 0,1 dan didapatkan hasil bahwa proses belum terkendali secara statistik

Analysis of Traditional and Fuzzy Quality Control Charts to Improve Short-Run Production in the Manufacturing Industry

Quality control charts are limited to controlling one characteristic of a production process, and it needs a large amount of data to determine control limits to control the process. Another limitation of the traditional control chart is that it doesn’t deal with the vague data environment. The fuzzy control charts work with the uncertainty that exists in the data. Also, the fuzzy control charts investigate the random variations found between the samples. In modern industries, productivity is often of different designs and a small volume that depends on the market need for demand (short-run production) implemented in the same type of machines to the production units. In such cases, it is difficult to determine the control limits for the operations carried out on the same machines. This work aims to compare the traditional control charts and the fuzzy control charts for short-run production. In the traditional case, the data collected were processed using the (Minitab 21) software. It was found that the fuzzy control charts were more flexible and accurate in determining the control limits of the machine under study. The traditional deviation from nominal control charts showed false alarm of observation (15) as out-of-control, while the fuzzy (DNOM) showed that these observations were under control. Also, the standard deviation of the process was dropped from (σ =0.209041) to (σ =0.204401) after using the fuzzy control chart

Approximate Reasoning in Hydrogeological Modeling

The accurate determination of hydraulic conductivity is an important element of successful groundwater flow and transport modeling. However, the exhaustive measurement of this hydrogeological parameter is quite costly and, as a result, unrealistic. Alternatively, relationships between hydraulic conductivity and other hydrogeological variables less costly to measure have been used to estimate this crucial variable whenever needed. Until this point, however, the majority of these relationships have been assumed to be crisp and precise, contrary to what intuition dictates. The research presented herein addresses the imprecision inherent in hydraulic conductivity estimation, framing this process in a fuzzy logic framework. Because traditional hydrogeological practices are not suited to handle fuzzy data, various approaches to incorporating fuzzy data at different steps in the groundwater modeling process have been previously developed. Such approaches have been both redundant and contrary at times, including multiple approaches proposed for both fuzzy kriging and groundwater modeling. This research proposes a consistent rubric for the handling of fuzzy data throughout the entire groundwater modeling process. This entails the estimation of fuzzy data from alternative hydrogeological parameters, the sampling of realizations from fuzzy hydraulic conductivity data, including, most importantly, the appropriate aggregation of expert-provided fuzzy hydraulic conductivity estimates with traditionally-derived hydraulic conductivity measurements, and utilization of this information in the numerical simulation of groundwater flow and transport

Pengendalian Kualitas Produksi Film Biaxially Oriented Polypropylene (BOPP) di PT. Trias Sentosa Tbk, Menggunakan Grafik Kendali FuzzyX̅̃-R̃

Kebutuhan plastik di Indonesia masih cukup tinggi, tercatat produksi plastik di Indonesia di tahun 2018 mencapai 4,6 juta ton/tahun. PT. Trias Sentosa Tbk, sebagai salah satu produsen terbesar produk film kemasan fleksibel dengan bahan utama Biaxially Oriented Polypropylene (BOPP) telah mengaplikasikan standar internasional dalam menghasilkan produk yang bernilai tinggi bagi pelanggan, namun pada proses produksi seringkali masih ditemukan kecatatan produk. Pada penelitian ini akan dianalisis karakteristik kualitas produk BOPP menggunakan grafik kendali X-R dan grafik kendali X-R berbasis fuzzy . Selajutnya dengan berbasis fuzzy akan ditinjau proses kapabilitas  setiap karakteristik kualitas. Didapatkan metode fuzzy dengan alpha (α = 0.65) lebih sesuai digunakan jika terdapat kesamaran (pengamatan pada sisi yang berbeda) karena lebih informatif. Selanjutnya proses kapabilitas setiap karakteristik kualitas sudah berada pada batas mampu atau memuaskan, kecuali pada karakteristik stiffness yang memiliki kapabilitas yang masih kurang baik sehingga disarankan dilakukan perbaikan atau peninjauan kembal

Explainable machine learning for project management control

Project control is a crucial phase within project management aimed at ensuring —in an integrated manner— that the project objectives are met according to plan. Earned Value Management —along with its various refinements— is the most popular and widespread method for top-down project control. For project control under uncertainty, Monte Carlo simulation and statistical/machine learning models extend the earned value framework by allowing the analysis of deviations, expected times and costs during project progress. Recent advances in explainable machine learning, in particular attribution methods based on Shapley values, can be used to link project control to activity properties, facilitating the interpretation of interrelations between activity characteristics and control objectives. This work proposes a new methodology that adds an explainability layer based on SHAP —Shapley Additive exPlanations— to different machine learning models fitted to Monte Carlo simulations of the project network during tracking control points. Specifically, our method allows for both prospective and retrospective analyses, which have different utilities: forward analysis helps to identify key relationships between the different tasks and the desired outcomes, thus being useful to make execution/replanning decisions; and backward analysis serves to identify the causes of project status during project progress. Furthermore, this method is general, model-agnostic and provides quantifiable and easily interpretable information, hence constituting a valuable tool for project control in uncertain environments