A Feature Based Method for Product-Oriented Representation to Manufacturing Resources in Cloud Manufacturing

2014 ◽  
Author(s):  
Xu Liu ◽  
Yingguang Li ◽  
Wei Wang ◽  
Lihui Wang
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Cloud Manufacturing ◽  
Current Status ◽  
Machining Features ◽  
Feature Based ◽  
Manufacturing Resources ◽  
Traditional Representation ◽  
Part Model ◽  
Selection Of

In cloud manufacturing, resources are encapsulated into manufacturing services to be provided in the manufacturing cloud. Resources representation is the basis for resources encapsulation. However, traditional representation methods to manufacturing resources mainly focus on the static description and/or current status of equipment. Research in product-oriented representation to manufacturing capabilities is limited. As a result, the evaluation to resources in the manufacturing cloud is indirect which will complicate the decision making in service determination. This paper presents a feature based method for manufacturing resources representation. Machining features will be first extracted from the part model based on a predefined feature category. Then capabilities of resources linked by the manufacturing cloud to machine the part will be generated by computing the capabilities to machine the features based on a knowledge base composed of the rules to define resource capabilities. With this method, capabilities of manufacturing resources will be associated with certain product and the selection of service from the manufacturing cloud will be greatly facilitated.

Feature-Based Adaptive Manufacturing Equipment Control for Cloud Environments

2016 ◽  
Author(s):  
Göran Adamson ◽  
Lihui Wang ◽  
Magnus Holm ◽  
Philip Moore
Keyword(s):  
Resource Sharing ◽  
Service Management ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Manufacturing Equipment ◽  
Control Approach ◽  
Function Block ◽  
Feature Based ◽  
Cloud Environments ◽  
Manufacturing Resources

The ideas of on-demand, scalable and pay-for-usage resource-sharing in Cloud Manufacturing are steadily attracting more interest. For implementing the concept of Manufacturing-as-a-Service in a cloud environment, description models and implementation language for resources and their capabilities are required. A standardized approach for systemized virtualization, servisilisation, retrieval, selection and composition into higher levels of functionality is necessary. For the collaborative sharing and use of networked manufacturing resources there is also a need for a control approach for distributed manufacturing equipment. In this paper, the technological perspective for an adaptive cloud service-based control approach is described, and a supporting information model for its implementation. The control is realized through the use of a network of intelligent and distributable Function Block decision modules, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system’s integration to the cloud service management functionality is described, as well as a feature-level capability model and the use of ontologies and the Semantic Web.

scholarly journals EFEKTYVIŲ PROJEKTŲ KŪRIMO DEMONSTRACINĖ SISTEMA/DEMONSTRATION SYSTEM FOR DEVELOPMENT OF EFFICIENT PROJECTS

1996 ◽  
Vol 2 (8) ◽  
pp. 84-100 ◽  
Author(s):  
Gintautas Ambrasas ◽  
Artūras Kaklauskas ◽  
Edmundas K. Zavadskas
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Rational Design ◽  
Multicriteria Analysis ◽  
Life Time ◽  
Time Process ◽  
Construction Site ◽  
Multicriteria Assessment ◽  
Selection Of

The paper describes the Demonstration System suggested by the authors. This Demonstration System enables efficient performance of alternative life-time process planning, multicriteria assessment, utility degree determination and selection of the most efficient versions of various projects and their constituent parts: Determination of rational design solutions for walls, windows, roof structures, basement floor, heating system. Selection of rational constituent parts of a project (selection of construction site and location, multipurpose complex assessment of buildings, determination of rational types of contracts). Selection of efficient interested parties (contractors, suppliers, neighbours). Alternative designing of life-time process of a project (one-family dwelling houses; agricultural, cast-in-place, prefabricated panel, and thermal renovation of buildings), its multicriteria assessment, determination of utility degree and selection of the most efficient version: Determination of efficient investment projects. Preparation of recommendations on efficiency increase of projects. Alternative roof-to-basement designing of a building (one-family dwelling houses; agricultural, cast-in-place, prefabricated panel, and thermal renovation of buildings) and its multicriteria analysis. This Demonstration System enables to perform alternative designing of projects and their constituent parts, multicriteria analysis, determination of utility degree and priority and preparation of recommendations on further improvement of projects. The Demonstration System is composed of two main parts: knowledge and decisionmaking subsystem. The knowledge base contains information (system and subsystems of criteria, values and significance of criteria, etc.) fully characterizing life-time processes of various projects (investments, buildings and so forth) and their constituent parts. For instance, knowledge base of life-time process of constituent parts of a building consists of information on alternative construction sites, buildings, designers, contractors, suppliers and so on. A construction site can be described by the following system of criteria: cost, assessment of existing services (water supply, sewerage, gas, electric power supply), air contamination level, living expenses, shopping possibilities, assessment of possibilities for recreation, sports and medical care, possibility to find a job, development outlooks of the district, transport conveniences, etc. The composed knowledge base is processed in various sections by decision-making subsystem. The multicriteria analysis of received results is performed by decision-making subsystem two. The Demonstration System is used by Bachelors, Engineers and Masters while working on their term and final projects. The paper gives a more detailed analysis of the proposed System. There is presented one of works performed by means of the Demonstration System.

scholarly journals EXPERT SYSTEMS TO SOLVE INFORMATIONAL ISSUES IN APPAREL DESIGN

2018 ◽  
Vol 6 (3) ◽  
pp. 237-244
Author(s):  
Oksana Zakharkevich ◽  
Svetlana Kuleshova ◽  
Tetyana Zhylenko ◽  
Iryna Shuda
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Knowledge Base ◽  
Multifractal Analysis ◽  
Rapid Change ◽  
Apparel Design ◽  
Selection Of

This research is devoted to developing of the expert systems for rapid change in production of clothing. The main objective of the study was achieved through the formation of rules of decision-making that intent to solve subtasks of rapid change in production of women's outerwear, including selection of fashion fabric and parameters of the pattern blocks for the specific garments types, and choosing the models of readymade garments. Multifractal analysis was used to evaluate roughness of fabrics. Knowledge base was developed in the shell “Rapana”.

Cloud Manufacturing: Drivers, Current Status, and Future Trends

2013 ◽  
Author(s):  
Dazhong Wu ◽  
Matthew J. Greer ◽  
David W. Rosen ◽  
Dirk Schaefer
Keyword(s):  
Cloud Manufacturing ◽  
Current Status ◽  
Future Trends ◽  
Production Lines ◽  
Short Term ◽  
Resource Loading ◽  
Optimal Resource ◽  
Manufacturing Resources ◽  
Enhance Efficiency

Cloud Manufacturing (CM) refers to a customer-centric manufacturing model that exploits on-demand access to a shared collection of diversified and distributed manufacturing resources to form temporary, reconfigurable production lines which enhance efficiency, reduce product lifecycle costs, and allow for optimal resource loading in response to variable-demand customer generated tasking. Our objective is to present the drivers, current status of research and development, and future trends of CM. We also discuss the potential short term and long term impacts of CM on various sectors.

Selection of Contractor Using Multi-Criteria Decision Making Tools

2011 ◽  
Vol 4 (4) ◽  
pp. 139-142
Author(s):  
S.PUSHPARANI S.PUSHPARANI ◽  
◽  
Dr.S.SENTHAMILKUMAR Dr.S.SENTHAMILKUMAR
Keyword(s):  
Decision Making ◽  
Multi Criteria Decision Making ◽  
Selection Of

Sistem Pendukung Keputusan SNMPTN Jalur Undangan Dengan Metode Electre

2018 ◽  
Vol 3 ◽  
pp. 14
Author(s):  
Lidia K Simanjuntak ◽  
Tessa Y M Sihite ◽  
Mesran Mesran ◽  
Nuning Kurniasih ◽  
Yuhandri Yuhandri
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Action Selection ◽  
Multi Criteria Decision Making ◽  
Prospective Students ◽  
College Entrance ◽  
Electre Method ◽  
Selection Of

All colleges each year organize the selection of new admissions. Acceptance of prospective students in universities as education providers is done by selecting prospective students based on achievement in school and college entrance selection. To select the best student candidates based on predetermined criteria, then use Multi-Criteria Decision Making (MCDM) or commonly called decision support system. One method in MCDM is the Elimination Et Choix Traduisant la Reality (ELECTRE). The ELECTRE method is the best method of action selection. The ELECTRE method to obtain the best alternative by eliminating alternative that do not fit the criteria and can be applied to the decision SNMPTN invitation path.

EXPERT MANAGEMENT SYSTEMS OF AGROTECHNOLOGIES IN CLOUD INFORMATION TECHNOLOGY

1970 ◽  
pp. 12-17
Author(s):  
I. М. Mikhaylenko ◽  
V. N. Timoshin
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Information System ◽  
Knowledge Base ◽  
Information Technologies ◽  
Agricultural Sector ◽  
Perennial Grasses ◽  
Management Decision ◽  
Agricultural Technologies ◽  
Automation Systems

The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.

PEMILIHAN KEPLING TELADAN MENERAPKAN METODE RANK ORDER CENTROID (ROC) DAN METODE ADDITIVE RATIO ASSESSMENT (ARAS) DI KECAMATAN MEDAN AREA

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Liza Handayani ◽  
Muhammad Syahrizal ◽  
Kennedi Tampubolon
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Rank Order ◽  
Priority Level ◽  
The Village ◽  
Field Area ◽  
Selection Of

The head of the environment is an extension of the head of the village head in assisting or providing services to the community both in the administration of administration in the village and to other problems. It is natural for a kepling to be appreciated for their performance during their special tenure in the kecamatan field area. Previously, the selection of a dipling in a sub-district was very inefficient and seemed unfair for this exemplary selection to use a system to produce an accurate value, and no intentional element. To overcome the process of selecting an exemplary kepling that experiences these obstacles by using an application called a Decision Support System. Decision Support System (SPK) is a system that can solve a problem, and this system is also assisted with several methods, namely the Rank Order Centroid (ROC) method that can assign weight values to each of the criteria based on their priority level. And to do the ranking or determine an exemplary set using the Additive Ratio Assessment (ARAS) method, this method provides decision making that takes decisions based on ranking or the highest value.Keywords: Head of Medan Area Subdistrict, SPK, Centroid Rank Order, Additive Ratio Assessment (ARAS).

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU BERPRESTASI KOTA MEDAN MENERAPKAN METODE PREFERENCES SELECTION INDEX (STUDI KASUS : DINAS PENDIDIKAN KOTA MEDAN)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fajar Syahputra ◽  
Mesran Mesran ◽  
Ikhwan Lubis ◽  
Agus Perdana Windarto
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Formal Education ◽  
Basic Education ◽  
Selection Index ◽  
Main Task ◽  
Selection Of

The teacher is a major milestone in the world of education, the ability and achievement of students cannot be separated from the role of a teacher in teaching and guiding students. Based on the Law of the Republic of Indonesia No. 14 of 2005 concerning Teachers and Lecturers, in Article 1 explained that teachers are professional educators with the main task of educating, teaching, guiding, directing, training, evaluating, and evaluating students in early childhood education through formal education, basic education and education medium. Whereas in Article 4 of the Act, it is explained that the position of teachers as professionals serves to enhance the dignity and role of teachers as learning agents to function to improve the quality of national education.Decision making is an election process, among various alternatives that aim to meet one or several targets. The decision-making system has 4 phases, namely intelligence, design, choice and implementation. These phases are the basis for decision making, which ends with a recommendation.The Preferences Selection Index (PSI) method is a rarely used decision support system method. This method is a method developed by stevanie and Bhatt (2010) to solve the Multi Criteria Decision Making (MCDM). With the right consideration, this method can be one of the tools to determine policies in decision-making systems, especially the selection of outstanding teachers. Determination of policies taken as a basis for decision making, must use criteria that can be defined clearly and objectively.Keywords: Decision Support System, PSI, Selection of Achieving Teachers

Artikel Strategi tentang Pengambilan Keputusan

2019 ◽  
Author(s):  
Winda Safitri Caniago ◽  
Hade Afriansyah
Keyword(s):  
Decision Making ◽  
Optimization Model ◽  
Heuristic Model ◽  
Selection Of

Decision making is an action with determine the result in solving problem with choose a rule action between alternative through a mental of process, logic of process and etc. This purpose article is to help make it easier to solve a problem. This article explain some strategy decision making such as optimization model, satisfying model, mixed scanning model, heuristic model, and last the selection of certain model.

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