scholarly journals An integrated approach based on Fuzzy Inference System for scheduling and process planning through multiple objectives

2020 ◽  
Vol 16 (3) ◽  
pp. 1235-1259
Author(s):  
Dariush Mohamadi Zanjirani ◽  
◽  
Majid Esmaelian
2015 ◽  
Vol 25 (3) ◽  
pp. 377-396
Author(s):  
N. Sozhamadevi ◽  
S. Sathiyamoorthy

Abstract A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.


2021 ◽  
pp. 1-19
Author(s):  
Majid Mardani Shahri ◽  
Abdolhamid Eshraghniaye Jahromi ◽  
Mahmoud Houshmand

The purpose of maintenance is to ensure the maximum efficiency and availability of production assets at optimal cost considering quality, safety, and environmental aspects. Assets criticality analysis is one of the main steps in many maintenance methodologies, including Reliability Centered Maintenance. The present study seeks to provide a solution for determining critical assets for more efficient maintenance management. In this regard, an integrated approach of the analytical hierarchy process and fuzzy inference system was proposed based on the concept of the risk matrix. According to the concept of the risk matrix, two main criteria of failure consequences and probability were employed to determine assets criticality. Analytic Hierarchy Process (AHP) was used to consider all sub-criteria of failure consequences and probability. Finally, using two main criteria as inputs, a fuzzy inference system was developed to determine the criticality of the assets. The proposed approach was implemented in a gas refinery; the results showed its effectiveness and applicability in the process of prioritizing assets based on criticality criteria. The proposed approach has the advantages of multi-criteria decision-making techniques, modeling ambiguity and uncertainty in real issues, modeling the process of inference in the human mind, and storing the knowledge of the organization’s expert.


2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.


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