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"ABSTRACTTraditional approaches in constructing response surface models typically ignore model uncertainty. If the relationship between the input factors and output characteristics of a process is very complex, traditional model building approaches may have limited effectiveness. In this paper, we propose a multi model ensemble and then implement this ensemble model to optimize the process performance. To form a multi model ensemble, we need to determine the weights of the different models, that is, values indicating relative importance among the models. To determine the weights, a hybrid weighting method is proposed, in which both global and local weighting methods are taken into account. Based on the hybrid weights of different models, a multi model ensemble is built and optimized. An example is illustrated to verify the effectiveness of the proposed approach. The results show that the proposed model can achieve more accurate predictive capability and that a better process improvement is reached."

"This paper addresses a new robust multi objective multi period model for supply chain planning under uncertainty considering quantity discounts. The proposed model maximizes the current proht of the distributor by making a balance between the total costs of the supply chain and the distributor company s revenues of selling products and also maximizes the company s expected profit by introducing brands and taking the risk of loss on it. Considering uncertainty in the purchasing cost, selling fees, and demand fluctuations, the new robust multi objective mixed integer programming model is solved as a single objective mixed integer programming model by utilizing the LP metrics method. By settling regulatory penalty parameters and considering different economic scenarios, the robustness and effectiveness of the developed model are verified with the data from BEH PAKHSH Company, a commodities distributor in Iran. The outcomes show that the proposed model is a promising approach to run an efficient supply chain."

"Pada tahap awal desain kapal, optimasi dimensi utama memiliki dampak yang signifikan dalam menentukan kinerja kapal dan total cost of ownership. Penelitian ini berfokus pada pendekatan multi-objective optimization (MOP) dengan surrogate model untuk tahap awal desain kapal. Penelitian ini menerapkan pendekatan ensemble dari 3 surrogate model: PR (Polynomial Regression), Kriging, dan BPNN-PSO (Backpropagation Neural Networks – Particle Swarm Optimizer) dan adaptive switching metamodeling (ASM) framework pada MOP. Framework ini didapatkan dari taksonomi surrogate model berdasarkan bagaimana fungsi objective dan constraint dimodelkan secara independen atau agregat. Hasil akurasi surrogate model menunjukkan ensemble surrogate model mempunyai performa terbaik dengan Mean Absolute Error (MAE) 10.75 dan R2 0.98. Kemudian, hasil optimization menunjukkan kombinasi Kriging dengan ASM memberikan performa terbaik dengan nilai Inverted Generational Distance (IGD) paling kecil dan hypervolume paling besar dibandingkan kombinasi lainnya. Di sisi lain, framework dengan fungsi objective dan constraint dioptimalkan secara independen (framework M1-2), mendapatkan performa IGD yang paling baik untuk ensemble maupun individual surrogate model. Varian solusi desain dari kombinasi Kriging dan ASM framework memberikan nilai objective kebutuhan daya 60% lebih kecil dan berat baja 7% lebih kecil (dengan waktu desain 300 kali lebih cepat), jika dibandingkan dengan hasil desain oleh desainer kapal.

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In the early stages of ship design, optimization of main ship dimensions significantly impacts ship performance and the total cost of ownership. This research focuses on the Multi-Objective Optimization (MOP) approach with the surrogate model for the early stages of ship design. This study applies an ensemble approach of 3 surrogate models: PR (Polynomial Regression), Kriging, and BPNN-PSO (Backpropagation Neural Networks - Particle Swarm Optimizer) and Adaptive Switching Metamodeling (ASM) framework on MOP. This framework is obtained from the surrogate model taxonomy based on how the objective and constraint functions are modeled independently or in aggregate. The results of the surrogate model accuracy show that the ensemble surrogate model has the best performance with a Mean Absolute Error (MAE) of 10.75 and R2 of 0.98. Then the optimization results show that the combination of Kriging with the ASM framework has the best performance with the smallest IGD value and the largest hypervolume compared to other combinations. Meanwhile, frameworks with objective and constraint functions optimized independently (framework M1-2) have the best IGD performance for both ensemble and individual surrogate models. The design solution variant of the Kriging and ASM framework has objective values of 60% less effective power and 7% less steel weight requirements (with design time 300 times faster), when compared to the original design by the expert/ship designer."

"Integrasi sistem merupakan salah satu kunci penting dalam mengoptimalkan performa sistem supply chain secara komprehensif. Sistem terintegrasi sendiri dapat digambarkan sebagai sistem yang mengatur rangkaian proses yang melibatkan aktivitas dari hulu producer hingga hilir end customer . Selama horizon waktu tersebut, aktivitas supply chain terus berjalan ditiap entitasnya. Selama waktu tersebut pula, salah satu hal penting lainnya yang perlu diperhatikan adalah unsur time value of money. Ini pula yang mendasari pentingnya perhitungan nilai future value, dimana nilai ini juga berkontribusi terhadap total biaya yang dikeluarkan. Fokus dari penelitian ini adalah mengembangkan model optimasi sistem supply chain tiga tingkat multi entitas dengan melibatkan unsur perhitungan future value FV dalam fungsi tujuannya. Adapun variable yang menjadi perhatian utama yaitu jumlah barang/produk yang diproduksi dan distribusikan oleh produser kepada distributor, serta jumlah produk yang didistribusikan oleh distributor kepada retailer. Terdapat dua fungsi tujuan yang diharapkan dapat dicapai dari penelitian ini, yaitu meminimalkan total biaya yang dikeluarkan dalam sistem supply chain dan meningkatkan servis level pengiriman produk kepada customer. Penelitian ini menggunakan pendekatan genetic algorithm algoritma genetik untuk optimasi persamaan supply chain tiga tingkat. Adapun model algoritma yang digunakan adalah Multi Objective Genetic Algorithm MOGA dan Non Dominated Sorting Genetic Algorithm NSGAII. Hasil yang diperoleh menunjukkan konfigurasi optimal untuk jumlah produk yang diproduksi dan dikirim ditiap periodenya, sehingga total biaya yang diperoleh dan outstanding service level dapat diminimalkan.

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Integrated system are the critical key in optimizing performance of supply chain system comprehensively. Integrated system can described as regulator in arranging process flow end to end. In the certain horizon time, supply chain activity are still going and through each entity involved. Actually, the other point that need to be consider are time value of money perspective. This consideration take more specific factor that called 'future value' calculation. However, it also contribute to the total cost spend in supply chain system.

The purpose of this research are to develop and solve supply chain three echelon optimization equation. Decision variable consist of quantity of product create and distributed from producer to distributor and quantity of product delivered from distributor to retailer. There are two objective function are presented by this model, first minimization of total cost in supply chain system and second minimization of delivery tardiness delivery surplus of product in supply chain system.Genetic algorithm GA approach is applied to solve the equation model and particularly separated to MOGA and NSGAII method.

The result shown optimal configuration of product quantity delivered in each period, that impact to minimal total cost and improve service level."

"Corona Virus Disease 2019 (COVID-19) adalah penyakit yang menyerang tubuh manusia melalui virus Severe Acute Respiratory atau SARS-CoV-2. Munculnya wabah COVID-19 menimbulkan setidaknya 16,6 juta penduduk di dunia meninggal dunia serta tidak sedikit dari penderitanya mengidap Community Acquired Pneumonia (CAP). CAP adalah infeksi akut parenkim paru pada orang yang telah mendapatkan infeksi di masyarakat. Menurut World Health Organization (WHO), pneumonia menjadi penyebab utama kematian nomor tiga di negara miskin dan berkembang. Dengan adanya pendeteksian serta diagnosis lebih dini, pengidap CAP akibat terpapar oleh virus COVID-19 ini dapat ditangani lebih cepat sebelum menyebar luas. Oleh karena itu, analisis gambar medis sangat penting dalam upaya pengobatan CAP sedini mungkin. Adanya pengembangan teknologi deep learning dan computer vision dapat membantu dokter dalam melakukan pendeteksian lebih cepat serta akurat. Maka dari itu, penelitian ini mengusulkan model Convolutional Neural Network (CNN) dengan arsitektur ensemble model Xception, InceptionV3, NASNet Large, dan Inception Resnet-V2 dengan menggunakan metode pre-processing Principal Component Analysis (PCA) dalam melakukan pendeteksian COVID-19 tiga kelas pada gambar chest xray. Penggunaan metode PCA pada data pre-processing dapat membantu mengembangkan model yang lebih efisien serta akurat. Para peneliti telah mencoba pemrosesan gambar baik menggunakan gambar rontgen dada dan juga Computerized Tomography (CT scan) khususnya CNN. Penelitian sebelumnya telah membuat model CNN dengan arsitektur ensemble model yang terdiri dari Xception, Inception-V3, NASNet Large, dan Inception Resnet-V2 berbasis ensemble model. Namun, hasil akurasi dalam pendeteksiannya masih belum optimal. Oleh karena itu, penelitian ini mengusulkan penggunaan metode PCA untuk meningkatkan akurasi pendeteksian menjadi 88,95%. Akurasi pendeteksian meningkat sebesar 3,14% dari penelitian sebelumnya.

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Corona Virus Disease 2019 (COVID-19) is a disease that attacks the human body through the SARS-CoV-2 virus. The emergence of the COVID-19 outbreak has caused at least 16.6 million people worldwide to die, and many of them suffer from Community Acquired Pneumonia (CAP). CAP is an acute lung parenchyma infection in people who have been infected in the community. According to World Health Organization (WHO), pneumonia is the third leading cause of death in poor and developing countries. With earlier detection and diagnosis, CAP sufferers due to exposure to the COVID-19 virus can be treated more quickly before it spreads widely. Therefore, medical image analysis is crucial in the effort to treat CAP as early as possible. The development of deep learning and computer vision technology can help doctors to perform faster and more accurate detection. Hence, this research proposes a Convolutional Neural Network (CNN) model with ensemble architectures of Xception, InceptionV3, NASNet Large, and Inception Resnet-V2, using Principal Component Analysis (PCA) pre-processing method to perform three-class COVID-19 detection in chest x-ray images. The use of the PCA method in pre-processing data can help develop a more efficient and accurate model. Researchers have tried image processing using both chest X-ray images and also Computerized Tomography (CT scan), especially CNN. Previous research has created a CNN model with an ensemble model architecture consisting of Xception, Inception-V3, NASNet Large, and Inception Resnet-V2 based on the ensemble model. However, the results of the accuracy in the detection are still not optimal. Therefore, this study proposes the use of the PCA method to increase the detection accuracy to 88.95%. Detection accuracy increased by 3.14% from previous studies."

"Kepuasan pelanggan muncul saat perusahaan mengelola untuk menyediakan layanan logistik yang memenuhi atau melampaui harapan pelanggan. Oleh sebab itu perusahaan dituntut untuk memiliki sistem logistik yang baik, mengurangi biaya, dan mendapatkan keunggulan kompetitif. Saat ini, persaingan antar perusahaan umumnya dipengaruhi oleh hubungan mereka dengan perusahaan lain. Misalnya, transfer daya diperlukan untuk memajukan tingkat layanan pelanggan, yaitu logistik pihak ketiga (3PL). 3PL telah menjadi pendekatan utama bagi perusahaan untuk menawarkan layanan pelanggan. Dengan adanya perkembangan penelitian sebelumnya mengenai kriteria pemilihan, penelitian dengan fokus pada kriteria pemilihan 3PL memang sudah banyak, namun penelitian dengan kriteria pemilihan 3PL dengan melihat dari sudut pandang turnover masih belum ada. Dengan menggunakan metode ANP, kriteria yang digunakan adalah performa (31%), harga (25%), servis (18%), quality assurance (16%) dan turnover (10%).  Penelitian ini juga menunjukkan PT X logistics menjadi pilihan 3PL yang tepat

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Customer satisfaction arise when companies manage to provide logistics services that meet or exceed customer expectations. Therefore, the company is required to have a good logistics system, reduce costs, and gain competitive advantage. Today, the competition between companies is generally influenced by their relationships with other companies. For example, the power transfer is needed to advance the level of customer service, which is a third party logistics (3PL). 3PL has become the main approach for companies to offer customer service. With the development of previous research on the selection criteria, the research with a focus on the 3PL selection criteria is already a lot, but research with the 3PL selection criteria with a view from the perspective of turnover is still no. By using ANP, the criteria used are performance (31%), price (25%), services (18%), quality assurance (16%) and turnover (10%). The study also showed PT X logistics into the right 3PL selection."

"ABSTRAKKonsumsi energi dunia saat ini cenderung meningkat dari waktu ke waktu karena pertumbuhan industri dan transportasi. Di Indonesia, konsumsi LPG mencapai di atas 7.5 juta ton dengan 5.5 juta ton didatangkan melalui impor. Pemerintah Indonesia menggunakan energi alternatif yaitu Dimethyl Ether (DME) untuk mengurangi jumlah impor LPG nasional. Proyek pembangunan DME plant merupakan proyek yangmemerlukan dana besar dan waktu yang cukup lama, sehingga diperlukan kajian strategis yang meliputi harga bahan baku, optimalisasi transportasi produk ke titik pengiriman, analisis biaya manfaat dari penerapan DME dan perencanaan market awareness.Optimalisasi transportasi produk adalah salah satu aspek yang mempengaruhi pengurangan biaya proyek. Penelitian ini bertujuan untuk mengembangkan model dari beberapa tujuan dalam mengetahui jalur transportasi paling optimum. Pemilihan jalur dilakukan denganbeberapa aspek yang dipertimbangkan seperti aspek lingkungan, sosial, dan kekonomian. Geographical Information System (GIS) merupakan pemanfaatan teknologi dalam penentuan jalur transportasi truk. Sebuah model goal programming dikembangkan untukmenggambarkan aspek-aspek tersebut. Hasil dari penelitian ini bisa menjadi analisis tambahan bagi pemangku kebijakan dalam pemilihan jalur transportasi yang ada.

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ABSTRACTCurrent world energy consumption tends to increase from time to time due to the growth of industry and transportation. In Indonesia, Liquefied Petroleum Gas (LPG) consumption reaches above 7.5 million tons with 5.5 million tons imported through imports. The Indonesian government uses alternative energy namely Dimethyl Ether (DME) to reduce the amount of national LPG imports. The DME plant project is a project that requires a large funds and a long time, so we needed a strategic study that includes the price of raw materials, optimization of product transportation to delivery point, benefit cost analysis of the application of DME and planning "market awareness" program. Optimization of product transportation is one aspect that influences the reduction project costs. This study aims to develop models of several objectives in finding the most optimum transportation routes. Path selection is carried out with several aspects considered such as environmental, social, and economic aspects. Geographical Information System (GIS) is a technology used in determining transportation routes for trucks. A goal programming model was developed to illustrate these aspects. The results of this study can be an additional analysis for policymakers in the selection of existing transportation routes."

"Penelitian ini, bertujuan mengembangkan model penentuan due date melalui penjadwalan batch untuk melakukan pemenuhan (Model 1) dan penentuan (Model 2) due date dengan mempertimbangkan defect rate. Pada sistem produksi job shop dinamis mesin parallel yang memproduksi muti-item berstruktur multilevel. Ukuran performansi yang digunakan yaitu total actual flow time. Proses penjadwalan dilakukan dengan menggunakan teknik penyisipan (insertion technique), yaitu melakukan penyisipan operasi-operasi disemua posisi pemproses yang mungkin pada semua mesin yang tersedia. Pemilihan posisi didasarkan pada kriteria tertentu dengan memperhatikan terpenuhinya semua urutan proses (routing) dan hubungan proses pendahulu yang ada diantara setiap operasi. Permasalahan yang diselesaikan dalam penelitian mencakup kondisi statis dan dinamis.

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This research, aims to develop due date determination model trough batch scheduling to accomplish the due date (Model 1) and due date assignment (Model 2) with defect rate consideration. On dynamic job shop machines parallel that produce multi- item structured multi-level. The measurement of performance used is the total actual flow time. Scheduling process is done by using the insertion technique, perform insertion operation at all position that may be available on all machine. The selection criteria are based on a specific criteria with respect to fulfillment of all the process sequence and predecssoe existing between each operation. The problem are solved in the static and dynamic conditions."

"Penjadwalan produksi adalah hal yang penting dilakukan dalam perusahaan agar permintaan konsumen terpenuhi tepat waktu, sehingga daya saing perusahaan dan kepuasan konsumen tetap terjaga. Salah satu tipe penjadwalan berdasarkan aliran proses produksi adalah penjadwalan flowshop. Dalam permasalahan penjadwalan flowshop mempunyai beberapa konstrain yang potensial untuk dikembangkan, salah satunya release time. Penelitian ini mengembangkan model dari penjadwalan flowshop dengan mempertimbangkan release time serta mempunyai fungsi tujuan meminimasi makespan dan total lateness pada perusahaan yang bersifat mass production. Hasil penelitian menunjukan bahwa formulasi model usulan menghasilkan performansi yang baik dari model yang digunakan oleh perusahaan, baik dari nilai makespan ataupun total lateness.

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Production scheduling is an important thing done in the company so that consumer demand can be fulfilled on time, so that the company 39's competitiveness and customer satisfaction are maintained. One type of scheduling based on production process is flow shop scheduling. In flowshop scheduling problems have some potential constraints to develop, one of which is release time. This research develops a model of flowshop scheduling by considering release time and has purpose function to minimize makespan and total lateness in a mass production company. The results indicate that the proposed model formulation yielded a good performance of the model used by the company, either from the value of makespan or total lateness."

"Beberapa metode telah diajukan untuk menggabungkan beberapa hasil forecasting dalam single forecast yang diberi nama simple averaging, pemberian rata-rata dengan bobot pada tahap validasi kinerja, atau skema kombinasi non-parametrik. Metode ini menggunakan kombinasi tetap pada individual forecast untuk mendapatkan hasil final dari forecast. Dalam paper ini, pendekatan berbeda digunakan untuk memilih metode forecasting, di mana setiap titik dihitung dengan menggunakan metode terbaik yang digunakan oleh dataset pelatihan sejenis. Dengan demikian, metode yang dipilih dapat berbeda di setiap titik perkiraan. Similarity measure yang digunakan untuk membandingkan deret waktu untuk pengujian dan validasi adalah Euclidean dan Dynamic Time Warping (DTW), di mana setiap titik yang dibandingkan diberi bobot sesuai dengan keterbaruannya. Dataset yang digunakan dalam percobaan ini adalah data time series yang didesain untuk NN3 Competition dan data time series yang di-generate dari paten-paten USPTO dan publikasi ilmiah PubMed di bidang kesehatan, yaitu pada Apnea, Aritmia, dan Sleep Stages. Hasil percobaan menunjukkan bahwa pemberian kombinasi bobot dari metode yang dipilih berdasarkan kesamaan antara data pelatihan dan data pengujian, dapat menyajikan hasil yang lebih baik dibanding salah satu kombinasi metode unweighted yang dipilih berdasarkan similarity measure atau kombinasi tetap dari individual forecast terbaik.

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AbstractSeveral methods have been proposed to combine the forecasting results into single forecast namely the simple averaging, weighted average on validation performance, or non-parametric combination schemas. These methods use fixed combination of individual forecast to get the final forecast result. In this paper, quite different approach is employed to select the forecasting methods, in which every point to forecast is calculated by using the best methods used by similar training dataset. Thus, the selected methods may differ at each point to forecast. The similarity measures used to compare the time series for testing and validation are Euclidean and Dynamic Time Warping (DTW), where each point to compare is weighted according to its recentness. The dataset used in the experiment is the time series data designated for NN3 Competition and time series generated from the frequency of USPTO?s patents and PubMed?s scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. The experimental result shows that the weighted combination of methods selected based on the similarity between training and testing data may perform better compared to either the unweighted combination of methods selected based on the similarity measure or the fixed combination of best individual forecast."