Optimality of Chain-based Threshold Policies for Machine Maintenance under Imperfect Predictions

2020 ◽  
Author(s):  
Guanlian Xiao ◽  
Alp Akçay ◽  
Lisa Maillart ◽  
Geert-Jan van Houtum
Keyword(s):  
Machine Maintenance ◽  
Threshold Policies

Perencanaan Pengendalian Kualitas Produk Pakaian Bayi dengan Metode Six Sigma Pada CV. AGP

JEMAP ◽  
2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Albertus Reynaldo Kurniawan ◽  
Bayu Prestianto
Keyword(s):  
Quality Control ◽  
Continuous Improvement ◽  
Six Sigma ◽  
Screen Printing ◽  
Control Method ◽  
Operational Performance ◽  
Machine Maintenance ◽  
Quality Product ◽  
Break Time ◽  
And Control

Quality control becomes an important key for companies in suppressing the number of defective produced products. Six Sigma is a quality control method that aims to minimize defective products to the lowest point or achieve operational performance with a sigma value of 6 with only yielding 3.4 defective products of 1 million product. Stages of Six Sigma method starts from the DMAIC (Define, Measure, Analyze, Improve and Control) stages that help the company in improving quality and continuous improvement. Based on the results of research on baby clothes products, data in March 2018 the percentage of defective products produced reached 1.4% exceeding 1% tolerance limit, with a Sigma value of 4.14 meaning a possible defect product of 4033.39 opportunities per million products. In the pareto diagram there were 5 types of CTQ (Critical to Quality) such as oblique obras, blobor screen printing, there is a fabric / head cloth code on the final product, hollow fabric / thin fabric fiber, and dirty cloth. The factors caused quality problems such as Manpower, Materials, Environtment, and Machine. Suggestion for consideration of company improvement was continuous improvement on every existing quality problem like in Manpower factor namely improving comprehension, awareness of employees in producing quality product and improve employee's accuracy, Strength Quality Control and give break time. Materials by making the method of cutting the fabric head, the Machine by scheduling machine maintenance and the provision of needle containers at each employees desk sewing and better environtment by installing exhaust fan and renovating the production room.

scholarly journals Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Sehrish Malik ◽  
DoHyeun Kim
Keyword(s):  
Smart Factory ◽  
Prediction Algorithm ◽  
Utilization Rate ◽  
Task Management ◽  
Management Mechanism ◽  
Machine Maintenance ◽  
Machine Utilization ◽  
And Performance ◽  
Artificial Neural Network Ann ◽  
Prediction Mechanism

The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

THRESHOLD POLICIES IN THE CONTROL OF PREDATOR-PREY MODELS

2002 ◽  
Vol 35 (1) ◽  
pp. 107-112 ◽  
Author(s):  
Magno E.M. Meza ◽  
Michel I.S. Costa ◽  
Amit Bhaya ◽  
Eugenius Kaszkurewicz
Keyword(s):  
Predator Prey ◽  
Threshold Policies ◽  
Predator Prey Models

Analyzing Massive Machine Maintenance Data in a Computing Cloud

2012 ◽  
Vol 23 (10) ◽  
pp. 1831-1843 ◽  
Author(s):  
Arshdeep Bahga ◽  
Vijay K. Madisetti
Keyword(s):  
Machine Maintenance

Using Text Visualization to Aid the Analysis of Machine Maintenance Logs

2021 ◽  
Author(s):  
Senthil Chandrasegaran ◽  
Xiaoyu Zhang ◽  
Michael Brundage ◽  
Kwan-Liu Ma
Keyword(s):  
Text Visualization ◽  
Machine Maintenance

The Stackelberg Games of Water Extraction with Myopic Agents

2021 ◽  
Author(s):  
Alain Jean-Marie ◽  
Mabel Tidball ◽  
Víctor Bucarey López
Keyword(s):  
Stackelberg Game ◽  
Infinite Horizon ◽  
Dynamic Game ◽  
Water Extraction ◽  
Discount Factor ◽  
Stackelberg Games ◽  
Groundwater Extraction ◽  
Strategic Interactions ◽  
Water Users ◽  
Threshold Policies

We consider a discrete-time, infinite-horizon dynamic game of groundwater extraction. A Water Agency charges an extraction cost to water users and controls the marginal extraction cost so that it depends not only on the level of groundwater but also on total water extraction (through a parameter [Formula: see text] that represents the degree of strategic interactions between water users) and on rainfall (through parameter [Formula: see text]). The water users are selfish and myopic, and the goal of the agency is to give them incentives so as to improve their total discounted welfare. We look at this problem in several situations. In the first situation, the parameters [Formula: see text] and [Formula: see text] are considered to be fixed over time. The first result shows that when the Water Agency is patient (the discount factor tends to 1), the optimal marginal extraction cost asks for strategic interactions between agents. The contrary holds for a discount factor near 0. In a second situation, we look at the dynamic Stackelberg game where the Agency decides at each time what cost parameter they must announce. We study theoretically and numerically the solution to this problem. Simulations illustrate the possibility that threshold policies are good candidates for optimal policies.

Assessment of driving quality and its impact on changes in the technical condition of self-driving cars

2020 ◽  
pp. 24-26
Keyword(s):  
Artificial Intelligence ◽  
Life Cycle ◽  
Computer Technology ◽  
Technical Condition ◽  
Entire Life ◽  
Entire Life Cycle ◽  
Machine Maintenance ◽  
Current Task ◽  
Self Driving Cars

The article describes the current task of developing and improving existing technologies for machine maintenance throughout the entire life cycle. The use of modern achievements in the field of computer technology, digitization of information, as well as the development of artificial intelligence technologies, will allow you to get new scientific and engineering results aimed at managing the technical condition of machines in operation.

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