The Regional Logistics Hubs Location Problem Based on the Technique for Order Preference by Similarity to an Ideal Solution and Genetic Algorithm: A Case of Sichuan

2016 ◽  
Vol 13 (9) ◽  
pp. 6065-6075
Author(s):  
Si Chen ◽  
Dong Chen ◽  
Mi Gan
Keyword(s):  
Genetic Algorithm ◽  
Location Problem ◽  
Ideal Solution ◽  
Regional Logistics ◽  
Order Preference

A multi-objective optimization approach for simultaneously lightweighting and maximizing functional performance of vehicle body structure

2019 ◽  
Vol 234 (7) ◽  
pp. 2086-2102
Author(s):  
Dengfeng Wang ◽  
Shuang Wang ◽  
Chong Xie
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Hybrid Approach ◽  
Support Vector ◽  
Ideal Solution ◽  
Analysis Method ◽  
Multi Objective ◽  
Sensitivity Analysis Method ◽  
Order Preference ◽  
Pillar Structure

This study presents a hybrid approach to integrate the comprehensive sensitivity analysis method, support vector machine technology, modified non-dominated sorting genetic algorithm-II method and the technique for order preference by similarity to ideal solution, which have been applied to multi-objective lightweight optimization of the B-pillar structure of an automobile. First, numerical models of the static–dynamic stiffness and the crashworthiness performance of automobile are established and validated by experimental testing. Then, the comprehensive sensitivity analysis method is used to define the final optimization variables. Experimental design and support vector machine based surrogate model techniques are introduced to establish the approximate model; subsequently, the modified non-dominated sorting genetic algorithm-II algorithm is applied to the multi-objective lightweight optimization design of the B-pillar structure, and the non-dominated solution set is determined. The principal component analysis method is applied to determine the weight of each objective. Finally, the technique for order preference by similarity to ideal solution method is used to rank Pareto front from best to worst to obtain the optimal solution; furthermore, a comparison between the original model and optimized design denotes that the mass of the B-pillar being reduced by 22.55% under the other impacting indicators is well guaranteed. Therefore, the proposed hybrid approach provided promising prospects in the lightweight and crashworthiness optimization application of the B-pillar.

Optimization of roller burnishing process using Kriging model to improve surface properties

2019 ◽  
Vol 233 (12) ◽  
pp. 2264-2282 ◽  
Author(s):  
Trung-Thanh Nguyen ◽  
Xuan-Ba Le
Keyword(s):  
Genetic Algorithm ◽  
Surface Hardness ◽  
Kriging Model ◽  
Numerical Control ◽  
Maximum Height ◽  
Ideal Solution ◽  
Average Roughness ◽  
Roller Burnishing ◽  
Highly Nonlinear ◽  
Order Preference

The objective of this work is to investigate the influences of three machining factors (burnishing speed V, feed rate f, and depth of penetration a) on the improved rate of arithmetic average roughness Δ Ra, improved rate of maximum height roughness Δ Ry, and improved rates of surface hardness Δ SH. The internal roller burnishing experiments were conducted with the aid of the computer numerical control machining center and Box–Behnken experimental design. The Kriging models were used to render the highly nonlinear relationships between inputs and outputs. An integrative approach combining a Non-dominated Sorting Genetic Algorithm II and Technique for Order Preference by Similarity to Ideal Solution was adopted to generate a set of feasible optimal solutions and determine the best machining conditions. The scanning electron microscopy images were depicted to investigate the surface morphology at the different conditions. The X-ray diffraction was applied to measure the compressive stresses at the external surface. The results showed that the predicted values of the objectives have good agreement with the experimental ones. High surface quality is characterized by an improved average roughness of 95.80%, an enhancement in the maximum roughness of 91.98%, and an improvement in surface hardness of 45.44%, compared to pre-machined surfaces. The selection of optimum process parameters could help the burnishing operators to save the machining costs and time. The combination of Kriging model, Non-dominated Sorting Genetic Algorithm II, and Technique for Order Preference by Similarity to Ideal Solution is considered as an intelligent approach for modeling and optimization of burnishing processes.

TERAPAN KOMBINASI METODE TOPSIS DAN ANALYTICAL HIERARCHY PROCESS PADA PEREKOMENDASIAN PENERIMA BEASISWA PENINGKATAN PRESTASI AKADEMIK (Studi Kasus pada STMIK PPKIA Tarakanita Rahmawati)

2017 ◽  
Vol 8 (2) ◽  
pp. 663 ◽  
Author(s):  
Muhammad Fadlan ◽  
Muhammad Muhammad ◽  
Hadriansa
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
Ideal Solution ◽  
Microsoft Excel ◽  
Order Preference ◽  
Hierarchy Process

Beasiswa Peningkatan Prestasi Akademik (PPA) sebagai salah satu bentuk dukungan pemerintah Republik Indonesia terhadap dunia pendidikan. Beasiswa yang disalurkan oleh pemerintah melalui Perguruan Tinggi yang ada di Indonesia ini, penyeleksian dan penetapan penerimanya sepenuhnya diserahkan kepada pihak Perguruan Tinggi yang bersangkutan. Tahap inilah yang sangat rentan terjadinya kecurangan. Pada objek penelitian  yang diteliti hingga saat ini proses penyeleksian masih dilakukan dengan menggunakan Microsoft Excel, hal ini tentu saja kurang efektif dan efisien, serta rentan akan terjadinya kesalahan bahkan kecurangan. Untuk itu, diperlukan suatu metodologi dan aplikasi yang tepat dalam melakukan penyeleksian penerima beasiswa tersebut. Decision Support System digunakan sebagai solusi untuk melakukan perekomendasian penerima beasiswa, khususnya dengan menggunakan Metode Technique  for  Order  Preference  by  Similarity  to  Ideal  Solution  (TOPSIS)  dan  Analytical  Hierarcy Process (AHP). Penggunaan kombinasi dua metode tersebut dilakukan agar memiliki tingkat akurasi yang baik jika dibandingkan  dengan menggunakan satu metode. Hasilnya,  aplikasi  decision support system dengan penerapan kombinasi metode Topsis dan AHP berhasil di rancang dan di ujicoba, serta sukses dalam perekomendasian penerima beasiswa PPA dengan menghasilkan data alternatif mahasiswa yang terurut mulai dari nilai preferensi yang paling tinggi 0.764 hingga terendah 0.189. Hasil ini dapat menjadi rekomendasi bagi pengambil keputusan dalam mengambil keputusan yang efektik, efisien dan dapat dipertanggung jawabkan.

Radiator threat evaluation with missing data based on improved technique for order preference by similarity to ideal solution

2021 ◽  
pp. 1-10
Author(s):  
Yu-Heng Xu ◽  
Si-Yi Cheng ◽  
Hu-Biao Zhang
Keyword(s):  
Missing Data ◽  
Prior Knowledge ◽  
Attribute Reduction ◽  
Estimation Algorithm ◽  
Weighted Averaging ◽  
Ideal Solution ◽  
Attribute Weights ◽  
Order Preference ◽  
Improved Technique ◽  
Threat Evaluation

To solve the problem of the missing data of radiator during the aerial war, and to address the problem that traditional algorithms rely on prior knowledge and specialized systems too much, an algorithm for radiator threat evaluation with missing data based on improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been proposed. The null estimation algorithm based on Induced Ordered Weighted Averaging (IOWA) is adopted to calculate the aggregate value for predicting missing data. The attribute reduction is realized by using the Rough Sets (RS) theory, and the attribute weights are reasonably allocated with the theory of Shapley. Threat degrees can be achieved through quantization and ranking of radiators by constructing a TOPSIS decision space. Experiment results show that this algorithm can solve the incompleteness of radiator threat evaluation, and the ranking result is in line with the actual situation. Moreover, the proposed algorithm is highly automated and does not rely on prior knowledge and expert systems.

Sistem Pendukung Keputusan untuk Menentukan Penerima Bantuan Beasiswa dengan Metode AHP dan TOPSIS

Sainteks ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 65
Author(s):  
Rima Dwi Mainingsih ◽  
Muhammad Hamka
Keyword(s):  
Analytical Hierarchy Process ◽  
Black Box ◽  
Ideal Solution ◽  
Order Preference ◽  
Black Box Testing ◽  
Hierarchy Process

Salah satu program LAZISMU Banyumas adalah beasiswa. Beasiswa merupakan pemberian bantuan keuangan yang diberikan kepada perorangan yang bertujuan untuk meningkatkan kapasitas SDM atau Sumber Daya Manusia melalui pendidikan. Permasalahan di LAZISMU, sistem bantuan beasiswa masih bersifat manual maka diperlukan suatu sistem pendukung keputusan (SPK) untuk memperhitungkan segala kriteria yang mendukung pengambilan keputusan guna membantu, mempercepat dan mempermudah proses pengambilan keputusan. Metode yang digunakan pada sistem pendukung keputusan adalah metode Analytical Hierarchy Process (AHP) dan metode TOPSIS. Analytical Hierarchy Process (AHP) yang digunakan untuk memperbaiki nilai bobot dari kriteria. Nilai bobot yang dihasilkan dari metode AHP selanjutnya digunakan untuk menentukan proses peranking calon penerima bantuan beasiswa menggunakan metode Technique For Order Preference By Similarity To Ideal Solution (TOPSIS). Metode AHP dan TOPSIS digunakan untuk meningkatkan hasil rekomendasi calon penerima bantuan beasiswa. Hasil klasifikasi kemudian dievaluasi menggunakan Black Box Testing untuk mengetahui apakah fungsi, masukan dan keluaran dari perangkat lunak sesuai dengan spesifikasi yang dibutuhkan. Dengan demikian dapat disimpulkan bahwa sistem aplikasi telah berjalan sesuai dengan tujuan yang diharapkan, karena tidak ditemukan adanya kesalahan pada interface serta sistem yang dirancang dan dibangun mampu mengatasi kelemahan-kelemahan yang terdapat pada sistem yang lama dan memberikan hasil rekomendasi calon penerima bantuan beasiswa.Kata-kata Kunci: sistem pendukung keputusan, penerima beasiswa, AHP, TOPSIS, black box testing

Sistem Pendukung Keputusan Pemilihan Bibit Padi Berkualitas Dengan Metode AHP Dan Topsis

2021 ◽  
Vol 10 (2) ◽  
pp. 162-169
Author(s):  
Istna Mar`atul Khusna ◽  
Novita Mariana
Keyword(s):  
Analytic Hierarchy Process ◽  
Analytical Hierarchy Process ◽  
Ideal Solution ◽  
Analytic Hierarchy ◽  
Order Preference ◽  
Hierarchy Process

Abstrak— Bibit merupakan salah satu penentu dalam keberhasilan budidaya tanaman padi. Budidaya tanaman padi dimulai dari memilih bibit tanaman yang berkualitas karena bibit termasuk objek utama yang dikembangkan pada budidaya selanjutnya. Bibit sebagai pembawa gen dari induknya yang akan menentukan sifat dari tanaman setelah berproduksi dan untuk mendapatkan bibit padi yang berkualitas dapat diperoleh dari memilih dan menentukan bibit yang berasal dari induk berkualitas. Kualitas bibit merupakan kunci keberhasilan dalam budidaya padi. Bibit yang berkualitas mampu beradaptasi, memiliki pertumbuhan yang cepat serta seragam, tumbuh lebih cepat, tahan hama dan tinggi nilai produktivitasnya. Untuk mendapatkan bibit padi berkualitas, petani sering mengalami kesulitan. Berdasarkan kesulitan yang dialami petani, maka akan dibangun sebuah sistem pendukung keputusan untuk membantu petani memutuskan bibit yang akan ditanam sesuai dengan kondisi lingkungan tanam dengan mempertimbangkan beberapa aspek kriteria. Dalam mengatasi masalah pemilihan bibit padi tersebut dibuat sebuah program sistem pendukung keputusan agar memudahkan informasi dan rekomendasi kepada petani padi tentang bibit yang berkualitas. Dengan menggunakan dua metode yaitu Analytical Hierarchy Process (AHP) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Penentuan bobot kriteria dilakukan dengan menggunakan metode Analytic Hierarchy Process (AHP), sedangkan untuk tahap perankingan dikerjakan dengan menggunakan metode TOPSIS. Hasil yang didapatkan dari penelitian ini adalah padi berkualitas dari lima alternatif yang sudah ditetapkan, yaitu: Sunggal, Inpari32, Ciherang, IR64, Situbagendit. Sistem menghasilkan nilai preferensi tertinggi yaitu 0,858 pada padi Sunggal di urutan pertama dan  0,767 pada padi Inpari32 diurutan kedua. Jadi dari hasil penelitian ini, peneliti merekomendasikan bibit padi berkualitas yang cocok ditanam di di desa sambongbangi yaitu Sunggal dan Inpari32..Kata Kunci : Bibit Padi, DSS, TOPSIS, AHP, Kualitas Bibit Padi

Optimal Structural Design of a Heat Sink With Laminar Single-Phase Flow Using Computational Fluid Dynamics-Based Multi-Objective Genetic Algorithm

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Ya Ge ◽  
Feng Shan ◽  
Zhichun Liu ◽  
Wei Liu
Keyword(s):  
Genetic Algorithm ◽  
Heat Sink ◽  
Volumetric Flow Rate ◽  
Pareto Solutions ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Conflicting Objectives ◽  
Order Preference ◽  
General Method

This paper proposes a general method combining evolutionary algorithm and decision-making technique to optimize the structure of a minichannel heat sink (MCHS). Two conflicting objectives, the thermal resistance θ and the pumping power P, are simultaneously considered to assess the performance of the MCHS. In order to achieve the ultimate optimal design, multi-objective genetic algorithm is employed to obtain the nondominated solutions (Pareto solutions), while technique for order preference by similarity to an ideal solution (TOPSIS) is employed to determine which is the best compromise solution. Meanwhile, both the material cost and volumetric flow rate are fixed where this nonlinear problem is solved by applying the penalty function. The results show that θ of Pareto solutions varies from 0.03707 K W−1 to 0.10742 K W−1, while P varies from 0.00307 W to 0.05388 W, respectively. After the TOPSIS selection, it is found that P is significantly reduced without increasing too much θ. As a result, θ and P of the optimal MCHS determined by TOPSIS are 35.82% and 52.55% lower than initial one, respectively.

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