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Author Tzu-Yu, Lin ♦ Kuo-Cheng, Ting ♦ Shao-Chen, Liu ♦ Don-Lin, Yang ♦ Yu-Ren, Chen ♦ Hsi-Min, Chen ♦ Yi-Chung, Chen ♦ Cheng-Ju, Kuo
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher EDP Sciences
File Format PDF
Date Created 2018-08-13
Copyright Year ©2018
Language English ♦ French
Subject Domain (in LCC) TA1-2040
Subject Keyword Engineering (General) ♦ Technology ♦ Civil engineering (General)
Abstract In recent years, many major manufacturers have been incorporating Industry 4.0 technologies such as preventive fault detection, automated scheduling algorithms, and component management to increase productivity and reduce production costs. Achieving this objective requires a substantial amount of working capital to acquire large quantities of new machinery, equipment to extract data from the machinery, and high-priced big data analysis software. However, most factories in the world are small-or medium-sized companies and have not enough capital to replace their machinery or purchase big data analysis software. It is therefore almost impossible for these factories to reach the goal of Industry 4.0. Furthermore, most of the conventional automated production scheduling methods only consider a single criterion in scheduling, which is not applicable for actual situations. This study therefore proposed a multi-tasking multi-machine scheduling system for multi-stage multi-criteria production to address various shortcomings in existing methods. To achieve this goal, we proposed a novel concept based on skyline queries to assist in the scheduling process. Also, a data structure of "heap" is applied in this work to accelerate the scheduling process. The experimental results demonstrated the validity of the proposed approach.
ISSN 2261236X
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2018-01-01
e-ISSN 2261236X
Journal MATEC Web of Conferences
Volume Number 185
Starting Page 00023

Source: Directory of Open Access Journals (DOAJ)