Machine Learning-Supported Planning of Lead Times in Job Shop Manufacturing

2020 ◽  
pp. 363-370
Author(s):  
Kathrin Julia Kramer ◽  
Carsten Wagner ◽  
Matthias Schmidt
Keyword(s):  
Machine Learning ◽  
Job Shop ◽  
Lead Times

scholarly journals Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 183
Author(s):  
Paul Muñoz ◽  
Johanna Orellana-Alvear ◽  
Jörg Bendix ◽  
Jan Feyen ◽  
Rolando Célleri
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Early Warning Systems ◽  
Data Representation ◽  
Machine Learning Techniques ◽  
Lead Times ◽  
Warning Systems ◽  
Tropical Andes ◽  
K Nearest Neighbors ◽  
Learning Techniques

Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods.

scholarly journals LEAD-TIME, INVENTORY, AND SAFETY STOCK CALCULATION IN JOB-SHOP MANUFACTURING

2018 ◽  
Vol 58 (6) ◽  
pp. 395-401 ◽  
Author(s):  
Miguel Afonso Sellitto
Keyword(s):  
Lead Time ◽  
Job Shop ◽  
Lead Times ◽  
Lot Size ◽  
Safety Stock ◽  
Safety Stocks ◽  
Body Of Knowledge ◽  
Manufacturing Management ◽  
Workload Control ◽  
Control Body

The purpose of this article is to present a method for calculating the lead-time, the inventory, and the safety stock or buffer in job shop manufacturing, which are essentially stochastic variables. The research method is quantitative modelling. The theoretical foundation of the method relies on the techniques belonging to the WLC (workload control) body of knowledge on the manufacturing management. The study includes an application of the method in a manufacturing plant of the furniture industry, whose operation strategy requires high dependability. The company operates in a supply chain and must have high a reliability in deliveries. Adequate safety stocks, lead times, and inventory levels provide the protection against the lack of reliability in the deliveries. The inventory should remain within a certain range, being as small as possible to maintain low lead times, but not too small that it could provoke a starvation, configuring an optimization problem. The study offers guidelines for a complete application in industries. Further research shall include the influence of the variability of the lot size in the stochastic variables.

scholarly journals Flood Early Warning Systems using Machine Learning Techniques. Case the Tomebamba Catchment at the Southern Andes of Ecuador

2021 ◽  
Author(s):  
Paul Muñoz ◽  
Johanna Orellana-Alvear ◽  
Jörg Bendix ◽  
Jan Feyen ◽  
Rolando Célleri
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Early Warning Systems ◽  
Data Representation ◽  
Machine Learning Techniques ◽  
Lead Times ◽  
Warning Systems ◽  
Tropical Andes ◽  
K Nearest Neighbors ◽  
Learning Techniques

Flood Early Warning Systems (FEWSs) using Machine Learning (ML) has gained worldwide popularity. However, determining the most efficient ML technique is still a bottleneck. We assessed FEWSs with three river states, No-alert, Pre-alert, and Alert for flooding, for lead times between 1 to 12 hours using the most common ML techniques, such as Multi-Layer Perceptron (MLP), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1- and 12-hour cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of the society for floods.

scholarly journals How a learning factory approach can help to increase the un- derstanding of the application of machine learning on produc- tion planning and control tasks.

2021 ◽  
pp. 125-142
Author(s):  
Alexander Rokoss ◽  
◽  
Kathrin Kramer ◽  
Matthias Schmidt
Keyword(s):  
Machine Learning ◽  
Manufacturing Sector ◽  
Real Data ◽  
Lead Times ◽  
Data Sets ◽  
Negative Consequences ◽  
Scientific Publications ◽  
Face To Face ◽  
Planning And Control ◽  
And Control

Technological progress and increasing digitalization offer many opportunities to production companies, but also continually present them with new challenges. The automation of processes is progressing in manufacturing areas and technical support systems, such as human-robot collaboration, are leading to significant changes in workflows. However, in other areas of companies large parts of the work are still done by humans. This is partly the case with the use of production data. Although much data is already collected and sorted automatically, the final evaluation of this data and especially decision-making is often done by humans. In particular, this is the case for decisions that cannot clearly be made based on conditional programming. The use of machine learning (ML) represents a promising approach to make such complex decisions automatically. A sharp increase in scientific publications in the recent years demonstrates the trend that more and more companies and institutions are looking into the use of machine learning in production. Since ML is beeing applied across several industries, the resulting massive shortage of skilled workers in the field of ML has to be addressed in short and medium terms by training and educating existing employees in production companies. A contemporary approach to building competencies in dealing with problems in the manufacturing sector is the use of learning factories as a knowledge transfer enabler. They offer learners the opportunity to try out methods in a realistic environment without having to fear negative consequences for the company. The results of actions performed by participants can be experienced directly without any time delay, resulting in better learning results compared to conventional face-to-face teaching. This chapter shows how learning factories can support teaching machine learning methods in the field of PPC. For this purpose, the determination of lead times using real data sets is addressed with ML-based methods. Parallelly, the competencies required for the respective tasks were extracted. Based on this, elements of a learning factory were designed that simplifies the considered processes, so that the problem can be easily understood by learners. The last part of the chapter describes several learning factory game phases aiming on teaching the identified competencies. The described learning factory enables participants to setup ML-based projects in the context of manufacturing.

scholarly journals Flood Stage Forecasting Using Machine-Learning Methods: A Case Study on the Parma River (Italy)

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1612
Author(s):  
Susanna Dazzi ◽  
Renato Vacondio ◽  
Paolo Mignosa
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Computational Time ◽  
Support Vector ◽  
Lead Times ◽  
Efficiency Coefficient ◽  
Forecast Horizon ◽  
Training Time ◽  
Forecasting System

Real-time river flood forecasting models can be useful for issuing flood alerts and reducing or preventing inundations. To this end, machine-learning (ML) methods are becoming increasingly popular thanks to their low computational requirements and to their reliance on observed data only. This work aimed to evaluate the ML models’ capability of predicting flood stages at a critical gauge station, using mainly upstream stage observations, though downstream levels should also be included to consider backwater, if present. The case study selected for this analysis was the lower stretch of the Parma River (Italy), and the forecast horizon was extended up to 9 h. The performances of three ML algorithms, namely Support Vector Regression (SVR), MultiLayer Perceptron (MLP), and Long Short-term Memory (LSTM), were compared herein in terms of accuracy and computational time. Up to 6 h ahead, all models provided sufficiently accurate predictions for practical purposes (e.g., Root Mean Square Error < 15 cm, and Nash-Sutcliffe Efficiency coefficient > 0.99), while peak levels were poorly predicted for longer lead times. Moreover, the results suggest that the LSTM model, despite requiring the longest training time, is the most robust and accurate in predicting peak values, and it should be preferred for setting up an operational forecasting system.

scholarly journals Daily streamflow forecasting for Paraíba do Sul river using machine learning methods with hydrologic inputs

2018 ◽  
Author(s):  
Yulia Gorodetskaya ◽  
Leonardo Goliatt Da Fonseca ◽  
Gisele Goulart Tavares ◽  
Celso Bandeira de Melo Ribeiro
Keyword(s):  
Machine Learning ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Learning Methods ◽  
Daily Streamflow ◽  
Machine Learning Methods ◽  
Strategic Value ◽  
The Impact ◽  
Hydrologic Inputs ◽  
Paraíba Do Sul River

The Paraíba do Sul river flows through the most important industrial region of Brazil and its basin is characterized by conflicts of multiple uses of its water resources. The prediction of its natural flow has strategic value for water management in this basin. This research investigates the applicability of the two machine learning methods (Random Forest and Artificial Neural Networks) for daily streamflow forecasting of the Paraíba do Sul River at lead times of 1-7 days. The impact of fluviometric and pluviometric data from other basin sites on the quality of the forecast is also evaluated.

Comparison of wavelet and machine learning methods for regional drought prediction

2021 ◽  
Author(s):  
Gilbert Hinge ◽  
Ashutosh Sharma
Keyword(s):  
Machine Learning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Support Vector ◽  
Lead Times ◽  
Model Parameters ◽  
Coupled Models ◽  
Drought Prediction ◽  
Forecasting Performance ◽  
Grid Points

&lt;p&gt;Droughts are considered as one of the most catastrophic natural disasters that affect humans and their surroundings at a larger spatial scale compared to other disasters. Rajasthan, one of India's semiarid states, is drought inclined and has experienced many drought events in the past. In this study, we evaluated different preprocessing and Machine Learning (ML) approaches for drought predictions in Rajasthan for a lead-time of up to 6 months. The Standardized Precipitation Index (SPI) was used as the drought quantifying measure to identify the drought events. SPI was calculated for 3, 6, and 12-month timescales over the last 115-year using monthly rainfall data at 119 grid stations. &amp;#160;ML techniques, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Linear Regression (LR), were used to evaluate their accuracy in drought forecasting over different lead times. Furthermore, two data processing methods, namely the Wavelet Packet Transform (WPT) and Discrete Wavelet Transform (DWT), have also been used to enhance the aforementioned ML models' predictability. At the outset, the preprocessed SPI data from both the methods were used as inputs for LR, SVR, and ANN to form a hybrid model. The hybrid models' drought predictability for a different lead-time was evaluated and compared with the standalone ML models. The forecasting performance of all the models for all 119 grid points was assessed with three statistical indices: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE). RMSE was used to select the optimal model parameters, such as the number of hidden neurons and the number of inputs in ANN, and the level of decomposition and mother wavelet in wavelet analysis. &amp;#160;Based on these measures, the coupled model showed better forecasting performance than the standalone ML models. The coupled WPT-ANN model shows superior predictability for most of the grid points than other coupled models and standalone models. &amp;#160;All models' performance improved as the timescale increased from 3 to 12 months for all the lead times. However, the model performance decreased as the lead time increased.&amp;#160; These findings indicate the necessity of processing the data before the application of any machine learning technique. The hybrid model's prediction performance also shows that it can be used for drought early warning systems in the state.&lt;/p&gt;

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