Study on Master Production Schedule in Manufacturing System for Medium and Small Manufacture Enterprises

2012 ◽  
Vol 252 ◽  
pp. 349-353
Author(s):  
Xiao Hong Lu ◽  
Wen Yi Wu ◽  
Peng Zhuo Han ◽  
Guang Jun Li ◽  
Jie Wei
Keyword(s):  
Database Management System ◽  
Prototype System ◽  
Production Schedule ◽  
Process Data ◽  
Sql Server 2000 ◽  
Operation Process ◽  
Schedule System ◽  
Master Production Schedule ◽  
Production Planning Management ◽  
Manufacture Enterprise

According to the existing problems in production planning management mode and management processes of Chinese medium and small manufacture enterprise, referring to the advanced management concept of MRPII/ERP, a master production schedule system which is suitable for management state of Chinese medium and small manufacture enterprise is researched and developed. The operation process, data flow and ER model of the master production schedule system are discussed and researched. Finally, Microsoft vb.net is selected as the development tool of the master production schedule system and Microsoft SQL Server 2000 is used as the database management system, a master production schedule prototype system is designed and implemented.

Study on Parameter Optimization about NC Cutting of Marine Ship Diesel Engine and Cutting Database System

2008 ◽  
Vol 392-394 ◽  
pp. 821-825 ◽  
Author(s):  
Xi Feng Fang ◽  
Sheng Wen Zhang ◽  
Chan Yuan Gong ◽  
T.X. Lan
Keyword(s):  
Diesel Engine ◽  
Parameter Optimization ◽  
Cutting Parameters ◽  
Database Management System ◽  
Physical Models ◽  
Sql Server ◽  
Prototype System ◽  
Sql Server 2000 ◽  
Optimization Function ◽  
Set Up

In the manufacturing of diesel engine, cutting machining is the main processing method. In order to improve efficiency and increase benefit of machining, the cutting database and parameter optimization system is built for the five key parts of marine ship diesel engine, which can provide optimal and rational cutting parameters. Microsoft Visual Basic and Microsoft SQL Server 2000 are used as the database management system, combining the modeling function of Deform 5.03 software. The logical and physical models are designed, and the functions of basic information management and query are realized. It can commend reasonable cutting parameters according to machining condition input. The Weilenmann algorithm method is adopted to calculate the cutting force and motor power, and user can be convenient to judge whether the parameter input is safe and reasonable. The objective function and restrictive conditions are set up by using the simplex method and linear programming, and the optimization function of cutting parameters is realized. A developed prototype system and an example have verified some presented techniques and the research results are the basis of the future development.

scholarly journals A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3953 ◽  
Author(s):  
Bruno Abade ◽  
David Perez Abreu ◽  
Marilia Curado
Keyword(s):  
Machine Learning ◽  
Environmental Data ◽  
Machine Learning Techniques ◽  
Smart Environments ◽  
Prototype System ◽  
Indoor Environments ◽  
Process Data ◽  
Indoor Space ◽  
Occupancy Detection ◽  
Real Scenario

Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user’s experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments.

scholarly journals Safety Measures and Auto Detection against SQL Injection Attacks

2019 ◽  
Vol 8 (4) ◽  
pp. 2827-2833
Keyword(s):  
Web Application ◽  
Application Programming Interface ◽  
Database Management System ◽  
Transport Layer ◽  
Prototype System ◽  
Large Network ◽  
Security Measures ◽  
Sql Injection ◽  
Sql Injection Attack ◽  
The Web

The SQL injection attack (SQLIA) occurred when the attacker integrating a code of a malicious SQL query into a valid query statement via a non-valid input. As a result the relational database management system will trigger these malicious query that cause to SQL injection attack. After successful execution, it may interrupts the CIA (confidentiality, integrity and availability) of web API. The vulnerability of Web Application Programming Interface (API) is the prior concern for any programming. The Web API is mainly based of Simple Object Access Protocol (SOAP) protocol which provide its own security and Representational State Transfer (REST) is provide the architectural style to security measures form transport layer. Most of the time developers or newly programmers does not follow the standards of safe programming and forget to validate their input fields in the form. This vulnerability in the web API opens the door for the threats and it’s become a cake walk for the attacker to exploit the database associated with the web API. The objective of paper is to automate the detection of SQL injection attack and secure the poorly coded web API access through large network traffic. The Snort and Moloch approaches are used to develop the hybrid model for auto detection as well as analyze the SQL injection attack for the prototype system

scholarly journals An improved Genetic Algorithm For Fuzzy Production PlanningProblems with Application

2015 ◽  
Vol 1 (3) ◽  
pp. 390
Author(s):  
Jalal Abdulkareem Sultan ◽  
Omar Ramzi Jasim ◽  
Sarmad Abdulkhaleq Salih
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Fundamental Problem ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Main Idea ◽  
Production Schedule ◽  
Improved Genetic Algorithm ◽  
Customer Service Level ◽  
Master Production Schedule

Production Planning or Master Production Schedule (MPS) is a key interface between marketing and manufacturing, since it links customer service directly to efficient use of production resources. Mismanagement of the MPS is considered as one of fundamental problem in operation and it can potentially lead to poor customer satisfaction.  In this paper, an improved Genetic Algorithm (IGA) is used to solving fuzzy multi-objective master production schedule (FMOMPS). The main idea is to integrate GA with local search operator. The FMOMPS was applied in the Cotton and medical gauzes plant in Mosul city. The application involves determine the gross requirements by demand forecasting using artificial neural networks. The IGA proved its efficiency in solving MPS problems compared with the genetic algorithm for fuzzy and non-fuzzy model, as the results clearly showed the ability of IGA to determine intelligently how much, when, and where the additional capacities (overtimes) are required such that the inventory can be reduced without affecting customer service level.

scholarly journals ANALISIS PERENCANAAN BAHAN BAKU DI UD. AA DENGAN MENERAPKAN METODE MATERIAL REQUIREMENT PLANNING (MRP)

2020 ◽  
Vol 5 (1) ◽  
pp. 1-12
Author(s):  
Rudi Abdika Saputra ◽  
Inna Kholidasari ◽  
Susanti Sundari ◽  
Lestari Setiawati
Keyword(s):  
Lot Sizing ◽  
Raw Materials ◽  
Raw Material ◽  
Production Schedule ◽  
Material Requirements Planning ◽  
Forecasting Method ◽  
Material Requirement Planning ◽  
Material Requirement ◽  
Material Procurement ◽  
Master Production Schedule

This study discusses the application of the material requirements planning (MRP) method in the planning of raw materials in a furniture company. The purpose of this research is to know the planning of raw materials for furniture products in UD. AA, determine the most suitable inventory model to be applied to material inventory planning and analyze the role of the MRP system in raw material procurement planning. The forecasting method used is the quantitative method of time series analysis, determining the master production schedule, calculating lot sizing (LFL, EOQ, POQ methods). From determining the Master Production Schedule, it is found that the cabinet production plan for the next three months is 4 units per period or week, and based on the calculation of Material Requirement Planning (MRP) it can be seen what components are needed for the manufacture of cabinets, how many and when each component is required. Therefore it is obtained that the total raw material requirement for wood for the next three months is 11.34 m³.

The Intelligent Monitoring System

1990 ◽  
Vol 80 (6B) ◽  
pp. 1833-1851 ◽  
Author(s):  
Thomas C. Bache ◽  
Steven R. Bratt ◽  
James Wang ◽  
Robert M. Fung ◽  
Cris Kobryn ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Monitoring System ◽  
Database Management System ◽  
Automated System ◽  
Seismic Events ◽  
Process Data ◽  
Intelligent Monitoring ◽  
Seismic Arrays ◽  
Interactive Analysis ◽  
Knowledge Based

Abstract The Intelligent Monitoring System (IMS) is a computer system for processing data from seismic arrays and simpler stations to detect, locate, and identify seismic events. The first operational version processes data from two high-frequency arrays (NORESS and ARCESS) in Norway. The IMS computers and functions are distributed between the NORSAR Data Analysis Center (NDAC) near Oslo and the Center for Seismic Studies (Center) in Arlington, Virginia. The IMS modules at NDAC automatically retrieve data from a disk buffer, detect signals, compute signal attributes (amplitude, slowness, azimuth, polarization, etc.), and store them in a commercial relational database management system (DBMS). IMS makes scheduled (e.g., hourly) transfers of the data to a separate DBMS at the Center. Arrival of new data automatically initiates a “knowledge-based system (KBS)” that interprets these data to locate and identify (earthquake, mine blast, etc.) seismic events. This KBS uses general and area-specific seismological knowledge represented in rules and procedures. For each event, unprocessed data segments (e.g., 7 min for regional events) are retrieved from NDAC for subsequent display and analyst review. The interactive analysis modules include integrated waveform and map display/manipulation tools for efficient analyst validation or correction of the solutions produced by the automated system. Another KBS compares the analyst and automatic solutions to mark overruled elements of the knowledge base. Performance analysis statistics guide subsequent changes to the knowledge base so it improves with experience. The IMS is implemented on networked Sun workstations, with a 56 kbps satellite link bridging the NDAC and Center computer networks. The software architecture is modular and distributed, with processes communicating by messages and sharing data via the DBMS. The IMS processing requirements are easily met with major processes (i.e., signal processing, KBS, and DBMS) on separate Sun 4/2xx workstations. This architecture facilitates expansion in functionality and number of stations. The first version was operated continuously for 8 weeks in late-1989. The Center functions were then transferred to NDAC for subsequent operation. Later versions will be distributed among NDAC, Scripps/IGPP (San Diego), and the Center to process data from many stations and arrays. The IMS design is ambitious in its integration of many new computer technologies, but the operational performance of the first version demonstrates its validity. Thus, IMS provides a new generation of automated seismic event monitoring capability.

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