KSHV episomes: rugged individualists on the factory floor

Penjadwalan Produksi Menggunakan Metode Simulated Annealing pada Unit Produksi Daun Pintu di PT. ML

Talenta Conference Series Energy and Engineering (EE) ◽

10.32734/ee.v2i2.445 ◽

2019 ◽

Vol 2 (2) ◽

Author(s):

Rosnani Ginting ◽

Chairul Rahmadsyah Manik

Keyword(s):

Simulated Annealing ◽

Problem Solving ◽

Production Scheduling ◽

Production Control ◽

Delivery Time ◽

Production Schedule ◽

A Company ◽

Predetermined Time ◽

Factory Floor

Penjadwalan merupakan aspek yang sangat penting karena didalamnya terdapat elemen perencanaan dan pengendalian produksi bagi suatu perusahaan yang dapat mengirim barang sesuai dengan waktu yang telah ditentukan, untuk memperoleh waktu total penyelesaian yang minimum. Masalah utama yang dihadapi oleh PT. ML adalah keterlambatan penyelesaian order yang mempengaruhi delivery time ke tangan costumer karena pelaksanaan penjadwalan produksi dilantai pabrik belum menghasilkan makespan yang sesuai dengan order yang ada. Oleh kaena itu dituntut untuk mencari solusi pemecahan masalah optimal dalam penentuan jadwal produksi untuk meminimisasi total waktu penyelessaian (makespan) semua order. Dalam penelitian ini, penjadwalan menggunakan metode Simulated Annealing (SA) diharapkan dapat menghasilkan waktu total penyelesaian lebih cepat dari penjadwalan yang ada pada perusahaan. Scheduling is a very important aspect because in it there are elements of planning and production control for a company that can send goods in accordance with a predetermined time, to obtain a minimum total time of completion. The main problem faced by PT. ML is the delay in completing orders that affect delivery time to customer because the implementation of production scheduling on the factory floor has not produced the makespan that matches the existing order. Therefore, it is required to find optimal problem solving solutions in determining the production schedule to minimize the total time of elimination (makespan) of all orders. In this study, scheduling using the Simulated Annealing (SA) method is expected to produce a total time of completion faster than the existing scheduling in the company.

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Vlogs for enhancing trainee teachers’ motivational beliefs about school experience and teaching practice: They are on the factory floor for professional development

Journal of Research on Technology in Education ◽

10.1080/15391523.2021.1911721 ◽

2021 ◽

pp. 1-17

Author(s):

Murat Debbag ◽

Mustafa Fidan

Keyword(s):

Professional Development ◽

Teaching Practice ◽

School Experience ◽

Motivational Beliefs ◽

Trainee Teachers ◽

Factory Floor

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A Two-Layer Long Short-Term Memory Network for Bottleneck Prediction in Multi-Job Manufacturing Systems

Volume 3: Manufacturing Equipment and Systems ◽

10.1115/msec2018-6678 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xingjian Lai ◽

Huanyi Shui ◽

Jun Ni

Keyword(s):

Manufacturing Systems ◽

Short Term Memory ◽

Complex Dynamics ◽

State Of The Art ◽

Short Term ◽

Term Memory ◽

Future Production ◽

Effective Manner ◽

Long Short Term Memory ◽

Factory Floor

Throughput bottlenecks define and constrain the productivity of a production line. Prediction of future bottlenecks provides a great support for decision-making on the factory floor, which can help to foresee and formulate appropriate actions before production to improve the system throughput in a cost-effective manner. Bottleneck prediction remains a challenging task in literature. The difficulty lies in the complex dynamics of manufacturing systems. There are multiple factors collaboratively affecting bottleneck conditions, such as machine performance, machine degradation, line structure, operator skill level, and product release schedules. These factors impact on one another in a nonlinear manner and exhibit long-term temporal dependencies. State-of-the-art research utilizes various assumptions to simplify the modeling by reducing the input dimensionality. As a result, those models cannot accurately reflect complex dynamics of the bottleneck in a manufacturing system. To tackle this problem, this paper will propose a systematic framework to design a two-layer Long Short-Term Memory (LSTM) network tailored to the dynamic bottleneck prediction problem in multi-job manufacturing systems. This neural network based approach takes advantage of historical high dimensional factory floor data to predict system bottlenecks dynamically considering the future production planning inputs. The model is demonstrated with data from an automotive underbody assembly line. The result shows that the proposed method can achieve higher prediction accuracy compared with current state-of-the-art approaches.

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