4 research outputs found
SIMEX: A simulation-based expert production scheduling system.
No full textA good methodology for production scheduling can result in high efficiency in reducing manufacturing costs. SIMEX is an experimental simulation-based expert production scheduler developed by the author for applications in flexible flow shop systems in a dynamic factory environment. This study introduces the general framework of SIMEX. A prototype is developed on an IBM compatible PC in Prolog, MODSIM II, Visual Basic, and Visual C++ to generate feasible and acceptable schedules with a synchronous data exchange facility. In general, primary tasks of SIMEX are to meet due dates of the final products, to increase throughput by reducing the number of setups, and to reduce inventory cost in a flexible flow shop system in real time. SIMEX has also an ability to change its expert system's rule base interactively by means of a user interface. The expert system module of SIMEX allows to use heuristics, and production rules which are the simplifications that help limit the search for possible problem solutions and handling unexpected events. Simulation-based scheduler written in MODSIM II, is another module of SIMEX. It generates the schedules, repeatedly, to analyze and verify proposed design and alternatives. (Abstract shortened by UMI.
STRATEGI PARTNERSHIP DALAM UPAYA MEMBANGUN BRAND VALUECITILINK{STUDI KASUS : PROGRAM BOARDING PASS TRUE VALUE (BPTV)}
Get PDFThe purpose of this Project is to discuss (I) about the program of BPTV (II) explain the procedure of cooperation with merchant (III) and explain the obstacles faced by Citilink in BPTV program and its solution. The author also states which cities are available Citilink merchants, do not forget also the authors include the data of visitors who use the program BPTV. This program is a program of the Marketing Communication division, a sub-division of New Distribution Channel And Partnership (NDC). The method used in the manufacture and writing of this Final Project Task is a descriptive method through the written or oral words of the people and the behavior observed. The author also uses the method of observation is through field work practices conducted by the author at PT Citilink Indonesia for 3 months
Optimizing Career Choices in the World of Programming: A Web-Based Decision Support System with the Simple Additive Weighting (SAW) Method
Get PDFThis study proposes the development of a web-based Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to help students choose a career in programming. By integrating data from online questionnaire surveys and observations, this research highlights the complexity of career choice in the world of programming. Criteria such as salary, work location, and educational requirements were identified as key factors in decision-making. The SAW method was chosen because of its ease of understanding, flexibility, and ability to handle complex problems. The system implementation process involves data collection, observation, web-based system design, and website development. The final results show that alternative A3 (Software development) received the highest preference weight, confirming it as the best choice based on the specified criteria. The use of DSS is expected to provide effective guidance for students in making more informed career decisions.Studi ini mengusulkan pengembangan Sistem Pendukung Keputusan (SPK) berbasis web menggunakan metode Simple Additive Weighting (SAW) untuk membantu mahasiswa dalam memilih karir di bidang pemrograman. Dengan mengintegrasikan data dari survei kuesioner online dan observasi, penelitian ini menyoroti kompleksitas pemilihan karir di dunia pemrograman. Kriteria-kriteria seperti gaji, lokasi kerja, dan persyaratan pendidikan diidentifikasi sebagai faktor utama dalam pengambilan keputusan. Metode SAW dipilih karena kemudahan pemahaman, fleksibilitas, dan kemampuannya menangani masalah kompleks. Proses implementasi sistem melibatkan pengumpulan data, observasi, perancangan sistem berbasis web, dan pengembangan website. Hasil akhir menunjukkan bahwa alternatif A3 (Pengembangan perangkat lunak) mendapatkan bobot preferensi tertinggi, menegaskan sebagai pilihan terbaik berdasarkan kriteria yang ditetapkan. Penggunaan SPK diharapkan dapat memberikan panduan yang efektif bagi mahasiswa dalam membuat keputusan karir yang lebih terinformasi
Klasifikasi Tingkat Kemanisan Buah Kersen Berdasarkan Fitur Warna NTSC Menggunakan Jaringan Syaraf Tiruan Berbasis Pengolahan Citra Digital
Get PDFThe fruit of the calabura tree (Muntingia calabura) is a small red fruit originating from the Prunus genus, often found along roadsides. This fruit contains numerous nutrients beneficial for bodily health, serving as a highly potential source of nutrition. Presently, a challenge exists in determining the sweetness level of calabura fruit, relying heavily on manual human assessment. The development of classification utilizing technology is considered a crucial step. Previous research has concentrated on classifying various objects using RGB, HSV, YCbCr color feature extraction. However, it was observed that RGB, HSV, YCbCr color features are not universally suitable, particularly for calabura fruits. Hence, this study employs a method of classifying the sweetness level of calabura fruit based on NTSC color features using a Digital Image Processing-based Artificial Neural Network (ANN). This approach leverages color-based image processing features. The research involves several stages, starting from acquiring 300 calabura fruit images with 3 levels of classification to the classification process utilizing Backpropagation in the ANN. Multiple training and testing scenarios were conducted to select feature combinations with the highest accuracy and fastest computational time. Results revealed that the most effective feature used was the NTSC color feature as a skin characteristic parameter. Based on training outcomes using 210 training images, the accuracy reached 100% with a computational time of 1.66 seconds per image. Meanwhile, testing with 90 sample images showed an accuracy of 94% with a computational time of 4.23 seconds per image. Thus, it can be concluded that the employed method successfully classifies the quality of calabura fruit images based on color features and skin characteristics
