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A Study And Application Of Wallpaper Industry Production Scheduling And Statistics System

Posted on:2005-07-19Degree:MasterType:Thesis
Country:ChinaCandidate:F M LiuFull Text:PDF
GTID:2156360122992459Subject:Machinery and electronics manufacturing
Abstract/Summary:PDF Full Text Request
Multi-varieties and small batch production is becoming the dominate mode for the wall-paper industry. So, it is very important for the wall-paper industry to establish effective production scheduling and statistical system. On the base of studying the characteristics of make-to-order wall-paper industry, this paper utilizes the whole situation search for very capable genetic algorithm to develop a production scheduling and statistical system. The thesis includes the following main content:1) The production mode of the wall-paper industry based on make-to-order are studied, then the characteristics of wall-paper industry's production scheduling are pointed out: there is priority order in succession in the work procedure, but can be halted again between the procedure, And can reach the production finishing this work procedure on different machine, The main target of production scheduling is to make full use of production equipments.2) The research status and methods are summarized. The fundamentals and basic flow of genetic algorithm are discussed in detail. Associated with the production mode and characteristics of wall-paper industry, its production scheduling belongs to the hybrid flow shop's problem. Applied based-on permutation coding genetic algorithm and used minimal production time as target function, it is solved the problem of low efficiency of wall-paper production equipments' utility that is caused by manpower scheduling and improved the production efficiency.According to the above study, a production scheduling and statistic system is developed, which is the organic part of the ERP system of wall-paper factory in Guangdong Province , And the application results show that the system is efficiency.
Keywords/Search Tags:Make-to-Order, semi-process industry, Production scheduling, Genetic algorithm, Hybrid flow shop problem
PDF Full Text Request
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