scholarly journals Applying Clustering Methods to Develop an Optimal Storage Location Planning-Based Consolidated Picking Methodology for Driving the Smart Manufacturing of Wireless Modules

2021 ◽  
Vol 11 (21) ◽  
pp. 9895
Author(s):  
Tzu-An Chiang ◽  
Zhen-Hua Che ◽  
Ching-Hung Lee ◽  
Wei-Chi Liang
Keyword(s):  
Travel Time ◽  
Hierarchical Clustering ◽  
Average Duration ◽  
Smart Manufacturing ◽  
Storage Space ◽  
Clustering Methods ◽  
Location Planning ◽  
Duration Of Stay ◽  
Storage Location ◽  
Manufacturing Methods

Picking operations is the most time-consuming and laborious warehousing activity. Managers have been seeking smart manufacturing methods to increase picking efficiency. Because storage location planning profoundly affects the efficiency of picking operations, this study uses clustering methods to propose an optimal storage location planning-based consolidated picking methodology for driving the smart manufacturing of wireless modules. Firstly, based on the requirements of components derived by the customer orders, this research analyzes the storage space demands for these components. Next, this research uses the data of the received dates and the pick-up dates for these components to calculate the average duration of stay (DoS) values. Using the DoS values and the storage space demands, this paper executes the analysis of optimal storage location planning to decide the optimal storage location of each component. In accordance with the optimal storage location, this research can evaluate the similarity among the picking lists and then separately applies hierarchical clustering and K-means clustering to formulate the optimal consolidated picking strategy. Finally, the proposed method was verified by using the real case of company H. The result shows that the travel time and the distance for the picking operation can be diminished drastically.

scholarly journals Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregoire Preud’homme ◽  
Kevin Duarte ◽  
Kevin Dalleau ◽  
Claire Lacomblez ◽  
Emmanuel Bresso ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Latent Class ◽  
Latent Class Model ◽  
Real Life ◽  
Heterogeneous Data ◽  
Mixed Data ◽  
Categorical Variables ◽  
Clustering Methods ◽  
Model Based ◽  
Partitioning Around Medoids

AbstractThe choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of “ready-to-use” tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

OMPARATIVE ASSESSMENT OF THE TIME-BASED PARAMETERS OF COWS’ LACTATION PERFORMANCE WITH THE USE OF AN AUTOMATIC DEVICE

2021 ◽  
pp. 5-8
Author(s):  
В.П. МЕЩЕРЯКОВ
Keyword(s):  
Production Process ◽  
Milk Production ◽  
Average Duration ◽  
Automatic Device ◽  
Comparative Assessment ◽  
Lactation Performance ◽  
Duration Of Stay ◽  
Close Relationship

Проведена сравнительная оценка времени пребывания коров-первотелок в доильном боксе с минимальной, максимальной и средней продолжительностью молоковыведения из всех четвертей вымени высоко- и низкопродуктивных при доении на автоматической установке «Astronaut A». У высокопродуктивных животных по сравнению с низкопродуктивными установлено увеличение длительности исследуемых четырех периодов молоковыведения на 34,5—60,6%. Между изученными временными параметрами молоковыведения выявлена тесная взаимосвязь (r=0,69-0,98; P<0,001). Выведено уравнение регрессии между периодом доения, рассчитанного путем определения максимальной продолжительности выдаивания одной из четвертей вымени, и средней продолжительностью молоковыведения из всех четвертей вымени (r=0,96; P<0,001). Установлено, что доля технологических операций, не связанных с процессом молоковыведения, составляет 24,1—31,6% от длительности периода пребывания в доильном боксе. Показана возможность использования для оценки интенсивности молоковыведения на автоматической установке «Astronaut A4» наряду с продолжительностью пребывания коровы в доильном боксе показателей максимальной и минимальной продолжительности молоковыведения из одной четверти вымени, а также средней продолжительности молоковыведения из всех четвертей вымени у высоко- и низкопродуктивных коров. Comparative assessment of the time of stay of first-calf cows in a milking box with minimum, maximum, and average duration of milk production from all udder quarters with high and low productivity when milked using the Astronaut A automatic device was carried out. When compared to low-productive animals, high-productive ones showed the increase in duration of the studied milk production periods by 34.5—60.6%. Close relationship between the time-based parameters of milk production was established (r=0.69-0.98; P<0.001). Equation of regression between the milking period, calculated by determining the maximum milking duration of one of the four udder quarters, and the average duration of milk production from all four udder quarters (r=0.96; P<0.001) was derived. It was established that the share of technological operations not connected to milk production process amounts to 24.1—31.6% from the duration of stay in the milking box. The possibility for using the parameters of maximum and minimal duration of milk production from one of udder quarters, as well as the average duration of milk production from all four quarters of the udder in high- and low-productive cows along with the length of cow’s stay in the milking box for the evaluation of milk production intensity using the Astronaut A4 automatic device was shown.

Application of smart manufacturing methods to steam methane reformers

2020 ◽  
pp. 309-332
Author(s):  
Ankur Kumar ◽  
Apratim Bhattacharya ◽  
Michael Baldea ◽  
Thomas F. Edgar
Keyword(s):  
Smart Manufacturing ◽  
Steam Methane ◽  
Manufacturing Methods

scholarly journals Machine-learning identification of asteroid groups

2019 ◽  
Vol 488 (1) ◽  
pp. 1377-1386 ◽  
Author(s):  
V Carruba ◽  
S Aljbaae ◽  
A Lucchini
Keyword(s):  
Machine Learning ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Family Members ◽  
Parent Body ◽  
Clustering Methods ◽  
New Family ◽  
Hierarchical Clustering Methods ◽  
Rotational Failure ◽  
Asteroid Families

ABSTRACT Asteroid families are groups of asteroids that share a common origin. They can be the outcome of a collision or be the result of the rotational failure of a parent body or its satellites. Collisional asteroid families have been identified for several decades using hierarchical clustering methods (HCMs) in proper elements domains. In this method, the distance of an asteroid from a reference body is computed, and, if it is less than a critical value, the asteroid is added to the family list. The process is then repeated with the new object as a reference, until no new family members are found. Recently, new machine-learning clustering algorithms have been introduced for the purpose of cluster classification. Here, we apply supervised-learning hierarchical clustering algorithms for the purpose of asteroid families identification. The accuracy, precision, and recall values of results obtained with the new method, when compared with classical HCM, show that this approach is able to found family members with an accuracy above 89.5 per cent, and that all asteroid previously identified as family members by traditional methods are consistently retrieved. Values of the areas under the curve coefficients below Receiver Operating Characteristic curves are also optimal, with values consistently above 85 per cent. Overall, we identify 6 new families and 13 new clumps in regions where the method can be applied that appear to be consistent and homogeneous in terms of physical and taxonomic properties. Machine-learning clustering algorithms can, therefore, be very efficient and fast tools for the problem of asteroid family identification.

Efficient Document Clustering for Web Search Result

2018 ◽  
Vol 7 (3.3) ◽  
pp. 90
Author(s):  
Sumathi Rani Manukonda ◽  
Asst.Prof Kmit ◽  
Narayanguda . ◽  
Hyderabad . ◽  
Nomula Divya ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Web Search ◽  
Traditional Approach ◽  
Document Clustering ◽  
Nearest Neighbors ◽  
Clustering Methods ◽  
Search Query ◽  
Semantic Approach ◽  
Search Result ◽  
Distinct Method

Clustering the document in data mining is one of the traditional approach in which the same documents that are more relevant are grouped together. Document clustering take part in achieving accuracy that retrieve information for systems that identifies the nearest neighbors of the document. Day to day the massive quantity of data is being generated and it is clustered. According to particular sequence to improve the cluster qualityeven though different clustering methods have been introduced, still many challenges exist for the improvement of document clustering. For web search purposea document in group is efficiently arranged for the result retrieval.The users accordingly search query in an organized way. Hierarchical clustering is attained by document clustering.To the greatest algorithms for groupingdo not concentrate on the semantic approach, hence resulting to the unsatisfactory output clustering. The involuntary approach of organizing documents of web like Google, Yahoo is often considered as a reference. A distinct method to identify the existing group of similar things in the previously organized documents and retrieves effective document classifier for new documents. In this paper the main concentration is on hierarchical clustering and k-means algorithms, hence prove that k-means and its variant are efficient than hierarchical clustering along with this by implementing greedy fast k-means algorithm (GFA) for cluster document in efficient way is considered.  

scholarly journals A Study on the Behavior of Clustering Techniques for Modeling Travel Time in Road-Based Mass Transit Systems

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 18
Author(s):  
Cristóbal ◽  
Padrón ◽  
Quesada-Arencibia ◽  
Alayón ◽  
Blasio ◽  
...  
Keyword(s):  
Travel Time ◽  
Behavior Pattern ◽  
Large Data ◽  
Mass Transit ◽  
Clustering Methods ◽  
Knowledge Modeling ◽  
Activity Data ◽  
Transit Systems ◽  
Clustering Techniques ◽  
Key Factor

In road-based mass transit systems, the travel time is a key factor affecting quality of service. For this reason, to know the behavior of this time is a relevant challenge. Clustering methods are interesting tools for knowledge modeling because these are unsupervised techniques, allowing hidden behavior patterns in large data sets to be found. In this contribution, a study on the utility of different clustering techniques to obtain behavior pattern of travel time is presented. The study analyzed three clustering techniques: K-medoid, Diana, and Hclust, studying how two key factors of these techniques (distance metric and clusters number) affect the results obtained. The study was conducted using transport activity data provided by a public transport operator.

Layouts of Unit-Load Warehouses with Multiple Slot Heights

2020 ◽  
Vol 54 (5) ◽  
pp. 1332-1350
Author(s):  
Luis F. Cardona ◽  
Kevin R. Gue
Keyword(s):  
Simulation Model ◽  
Travel Time ◽  
Operating Costs ◽  
Unit Load ◽  
Space Utilization ◽  
Duration Of Stay

We describe a method to generate layouts for unit-load warehouses that use multiple slot heights as a way to maximize warehouse space utilization. The problem has two parts: slots must be arranged into rack-bays, and rack-bays must be arranged into a layout. We describe two methods for the first subproblem, depending on whether the warehouse has directed picking and put-away. For the second, we describe a simulation model and a greedy procedure based on the duration-of-stay storage policy. We observe significant benefits of using multiple slot heights in unit-load warehouses with respect to footprint, expected travel time, and cost of racking. For a typical warehouse, we expect space savings between 25% and 35%, depending on the number of slot types, and savings of between 15% and 25% in annual operating costs.

scholarly journals Using hierarchical clustering to explore patterns of deprivation among English local authorities

2019 ◽  
Vol 42 (4) ◽  
pp. 772-777
Author(s):  
Steven L Senior
Keyword(s):  
Hierarchical Clustering ◽  
Demographic Characteristics ◽  
Homogeneous Group ◽  
Statistical Testing ◽  
Clustering Methods ◽  
Local Authorities ◽  
Multiple Deprivation ◽  
Hierarchical Clustering Methods ◽  
Different Levels ◽  
Indices Of Multiple Deprivation

Abstract Background The English Indices of Multiple Deprivation (IMD) is widely used as a measure of deprivation. However, similarly ranked areas can differ substantially in the underlying domains of deprivation. These domains contain a richer set of data that might be useful for classifying local authorities. Clustering methods offer a set of techniques to identify groups of areas with similar patterns of deprivation. Methods Hierarchical agglomerative (i.e. bottom-up) clustering methods were applied to domain scores for 152 upper tier local authorities. Advances in statistical testing allow clusters to be identified that are unlikely to have arisen from random partitioning of a homogeneous group. The resulting clusters are described in terms of their subdomain scores and basic geographic and demographic characteristics. Results Five statistically significant clusters of local authorities were identified. These clusters only partially reflect different levels of overall deprivation. In particular, two clusters share similar overall IMD scores but have contrasting patterns of deprivation. Conclusion Hierarchical clustering methods identify five distinct clusters that do not correspond closely to quintiles of deprivation. This approach may help to distinguish between places that face similar underlying challenges, and places that appear similar in terms of overall deprivation scores, but that face different challenges.

scholarly journals Kernel Treelets

2019 ◽  
Vol 11 (03n04) ◽  
pp. 1950006
Author(s):  
Hedi Xia ◽  
Hector D. Ceniceros
Keyword(s):  
Hierarchical Clustering ◽  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Space ◽  
Feature Space ◽  
Coefficient Matrix ◽  
Data Sets ◽  
Clustering Methods ◽  
Orthonormal Bases ◽  
Data Points ◽  
General Data

A new method for hierarchical clustering of data points is presented. It combines treelets, a particular multiresolution decomposition of data, with a mapping on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), uses this mapping to go from a hierarchical clustering over attributes (the natural output of treelets) to a hierarchical clustering over data. KT effectively substitutes the correlation coefficient matrix used in treelets with a symmetric and positive semi-definite matrix efficiently constructed from a symmetric and positive semi-definite kernel function. Unlike most clustering methods, which require data sets to be numeric, KT can be applied to more general data and yields a multiresolution sequence of orthonormal bases on the data directly in feature space. The effectiveness and potential of KT in clustering analysis are illustrated with some examples.

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