OPTIMIZATION PROCESSES IN WAREHOUSE MANAGEMENT

This article aims to show the essence and importance of optimizing warehouse processes. Efficiency itself is an inseparable element of development, it drives the industry and allows for constant development. The idea behind the optimization of warehouse processes is an important element of business development, thanks to optimization you can save both time and money, and the pursuit of self-improvement should guide every organization striving for excellence in terms of services offered and the manner and time of order execution.

Factors Affecting Apparel Pattern Grading Accuracy: Existing Software Solutions Comparison and Development of New Solution

Every so often, grading is not 100% accurate due to the conventional system for calculating the grading incre¬ment. The aim of this study was to develop a new calculation system of grading increment provided by different software, e.g. Lectra, Gerber, Optitex, Boke CAD etc., and to develop a new mathematical solution that enhances grading precision. For this experiment, three different spec sheets of different buyers were collected, and then combined and drawn to a solitary sketch for both front and back including all points of measures (POM) for a more easy comparison. The solutions for the presence of diagonal and curve measurements were provided with examples using various tools and techniques of different professional garment CAD software. The benefit of the new approach is not only reduced errors of grading but also guaranteed garment fit without distorting style features. However, the drawbacks of the measurement method are complicated and time-consuming. They revolve around the fact that iterative fitting and adjustments are mandatory to improve the fit before bulk production. The study revealed that this new system slightly increases calculation time, whereas the sample approval time for order execution reduces considerably.

A heijunka study for the production of standard parts included in a customized finished product

Efficient order execution plays a crucial role in the activity of every company. In production planning it is important to find a balance between the fluctuations of orders and stability of production flow regarding the company. One of the methods of achieving this goal is heijunka (production leveling). This paper presents a study of choosing the best variant of the production planning and control system for the production of standard parts. Three variants are investigated regarding delays in order delivery. The analysis of variants was conducted using a simulation method. The method of choosing the best variant for the production system being investigated is also proposed. The results show that the best variant is a mix of production leveling and production "for stock".

Implementation of Meltdown Attack Simulation for Cybersecurity Awareness Material

One of the rising risk in cybersecurity is an attack on cyber physical system. Today’s computer systems has evolve through the development of processor technology, namely by the use of optimization techniques such as out-of-order execution. Using this technique, processors can improve computing system performance without sacrificing manufacture processes. However, the use of these optimization techniques has vulnerabilities, especially on Intel processors. The vulnerability is in the form of data exfiltration in the cache memory that can be exploit by an attack. Meltdown is an exploit attack that takes advantage of such vulnerabilities in modern Intel processors. This vulnerability can be used to extract data that is processed on that specific computer device using said processors, such as passwords, messages, or other credentials. In this paper, we use qualitative research which aims to describe a simulation approach with experience meltdown attack in a safe environment with applied a known meltdown attack scheme and source code to simulate the attack on an Intel Core i7 platform running Linux OS. Then we modified the source code to prove the concept that the Meltdown attack can extract data on devices using Intel processors without consent from the authorized user.

An Efficient Data Mining Technique for Structural Strength Monitoring System

A fundamental target of strength monitoring frameworks for different structures is to analyze the condition of the structure and to assess its conceivable danger and furthermore to investigation, identification, and characterization of danger in complex structures is a critical part of auxiliary strength checking. The capacities are browsed as lexicon of time-recurrence movement and scaled variants of a basic Gaussian hypothesis work. This word reference is likewise adjusted to utilize genuine estimated information. Characterization is then accomplished by coordinating the removed damage includes in the time-frequency. In this paper, we utilize our model to assess our information mining approach for the fault checking. The balanced scratch-off and high-pass sifting strategies are consolidated adequately to take care of basic issues in numerical reconciliation signs gathered from sensors are disintegrated into direct blends of very confined Gaussian capacities utilizing the coordinating significance decay calculation. The combination exactness is enhanced and contrasted with former numerical integrators. Rough set analysis uses only internal knowledge and does not rely on prior model assumption as fuzzy set methods or probabilistic models do. In this manuscript a novel hybrid algorithm combining the features of Rough set Support vector machine (Rs-SVM) classified structures and Rough set Artificial Neural Network (Rs-ANN) classified structures are used. At long last the vertices of the structure of different types are connected and analysed by the Hybrid algorithm and furthermore to additionally enhance order execution, the data gathered from numerous sensors is incorporated utilizing a Bayesian sensor combination approach.

A multi-feature hybrid classification data mining technique for human-emotion

Background and objectives The ideal treatment of illnesses is the interest of every era. Data innovation in medical care has become extremely quick to analyze diverse diseases from the most recent twenty years. In such a finding, past and current information assume an essential job is utilizing and information mining strategies. We are inadequate in diagnosing the enthusiastic mental unsettling influence precisely in the beginning phases. In this manner, the underlying conclusion of misery expressively positions an extraordinary clinical and Scientific research issue. This work is dedicated to tackling the same issue utilizing the AI strategy. Individuals’ dependence on passionate stages has been successfully characterized into various gatherings in the data innovation climate. Methods A notable AI multi-include cross breed classifier is utilized to execute half and half order by having the passionate incitement as pessimistic or positive individuals. A troupe learning calculation helps to pick the more appropriate highlights from the accessible classes feeling information on online media to improve order. We split the Dataset into preparing and testing sets for the best proactive model. Results The execution assessment is applied to check the proposed framework through measurements of execution assessment. This exploration is done on the Class Labels MovieLens dataset. The exploratory outcomes show that the used group technique gives ideal order execution by picking the highlights’ greatest separation. The supposed results demonstrated the projected framework’s distinction, which originates from the picking-related highlights chosen by the incorporated learning calculation. Conclusion The proposed approach is utilized to precisely and successfully analyze the downturn in its beginning phase. It will assist in the recovery and action of discouraged individuals. We presume that the future strategy’s utilization is exceptionally appropriate in all data innovation-based E-medical services for discouraging incitement.

Negative selection—a new performance measure for automated order execution

Automated Order Execution is the dominant way of trading at stock markets. Performance of numerous execution algorithms is measured through slippage from some benchmark. But measuring true slippage in algorithmic execution is a difficult task since the execution as well as benchmarks are function of market activity. In this paper, we propose a new performance measure for execution algorithms. The measure, named Negative Selection, takes a posterior look at the trading window and allows us to determine what would have been the optimal order placement if we knew in advance, before the actual trading, the complete market information during the trading window. We define the performance measure as the difference between the hypothetical optimal trading position and the actual execution. This difference is calculated taking into account all prices and traded quantities within the considered time window. Thus, we are capturing the impact caused by our own trading as a cost that affects all trades. Properties of Negative Selection, which make it well defined and objective are discussed. Some empirical results on real trade data are presented.