Sustainable supplier selection under must-be criteria through Fuzzy inference system

2020 ◽  
Vol 248 ◽  
pp. 119275 ◽  
Author(s):  
Naveen Jain ◽  
A.R. Singh
Keyword(s):  
Fuzzy Inference System ◽  
Supplier Selection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Sustainable Supplier Selection

Sustainable supplier selection: A ranking model based on fuzzy inference system

2012 ◽  
Vol 12 (6) ◽  
pp. 1668-1677 ◽  
Author(s):  
Atefeh Amindoust ◽  
Shamsuddin Ahmed ◽  
Ali Saghafinia ◽  
Ardeshir Bahreininejad
Keyword(s):  
Fuzzy Inference System ◽  
Supplier Selection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Model Based ◽  
Sustainable Supplier Selection ◽  
Ranking Model

scholarly journals Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches

2021 ◽  
Vol 13 (3) ◽  
pp. 1413
Author(s):  
Seyed Amirali Hoseini ◽  
Alireza Fallahpour ◽  
Kuan Yew Wong ◽  
Amir Mahdiyar ◽  
Morteza Saberi ◽  
...  
Keyword(s):  
Decision Making ◽  
Construction Industry ◽  
Fuzzy Inference System ◽  
Supplier Selection ◽  
Fuzzy Inference ◽  
Social Issues ◽  
Second Phase ◽  
Inference System ◽  
Sustainable Supplier Selection ◽  
Best Worst Method

Due to increase in the public and stakeholders’ awareness regarding economic, environmental, and social issues, the construction industry tends to follow the sustainability policies and practices in supply chain management. Hence, one of the most crucial aspects for a construction company in this regard is sustainable supplier selection, and, to this end, an accurate and reliable model is required. In this paper a hybrid fuzzy best-worst method and fuzzy inference system model is developed for sustainable supplier selection. In the first phase of this study, after determining 19 criteria in three main aspects, the final weight of each aspect and criterion is obtained using fuzzy best-worst method approach. In the second phase, the most sustainable supplier is selected by running the weighted fuzzy inference system both in aspect and criterion level, providing more accurate results compared to the use of other available models. Finally, two different tests are employed to validate the results and evaluate the robustness of the proposed model. The novel developed model enables the decision-maker to simulate the decision-making process, reduce the calculations loads, consider a large number of criteria in decision making, and resolve the inherited uncertainties in experts’ responses.

Study a Fuzzy Inference System on the Supplier Evaluation in E-Manufacturing

2009 ◽  
Vol 16-19 ◽  
pp. 189-192 ◽  
Author(s):  
Jian Chen ◽  
Ming Hong Wu ◽  
Wen Rong Jiang ◽  
An Bao Wang ◽  
Ji Hong Yan
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Inference System ◽  
Evaluation System ◽  
Supplier Selection ◽  
Fuzzy Inference ◽  
Supplier Evaluation ◽  
Inference System ◽  
Key Factor

The supplier selection and evaluation is a key factor of the intelligent supplier selection & evaluation system in e-manufacturing. The model used for supplier selection is Fuzzy inference system which is introduced in the paper. The paper started with the brief introduction of the intelligent internet supplier selection & evaluation system. It concentrated to introduce the application of the fuzzy set model for supplier selection. This paper will introduce the design of the fuzzy sets model, and the evaluation results.

scholarly journals Decision Support System for Green Supplier Selection Using the Fuzzy Inference System Method in Abrasive Companies

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Defi Norita ◽  
Ririn Regiana Dwi Satya ◽  
Andary Asvaroza Munita ◽  
Asep Endih Nurhidayat
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference System ◽  
Supplier Selection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Green Supplier Selection ◽  
System Method ◽  
Green Suppliers

The development of information and communication technology makes it easier for users in the industrial world to make decisions in choosing environmentally friendly suppliers more easily. This study aims to determine the selection of green suppliers of all the criteria that have been determined and make a decision support system for selecting green suppliers with the Fuzzy Inference System method. The method used in making identification of green supplier selection is to create criteria based on fuzzy rules and to make digital business modeling using business process modeling notation. Decision support there are 4 criteria used, namely price, reject quality, late delivery and environmental management. Based on the results of research conducted it is known that with the fuzzy inference system method that is assisted using matlab software, the optimization results on the fuzzy inference system show that prices are 20.5%, quality is 5.5%, environment is 5.5%, and material delays are 3%, then supplier performance in selecting green suppliers with a decision making system of 55% so that green supplier selection is obtained at abrasive companies.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

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

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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