Production scheduling optimization method for textile machinery manufacturing enterprise based on improved bee algorithm

详细信息    查看官网全文

• 英文论文题名：Production scheduling optimization method for textile machinery manufacturing enterprise based on improved bee algorithm
• 论文作者：LI Xixing ; Du Baigang ; GUO Shunsheng ; XU Renhe
• 英文论文作者：LI Xixing ; Du Baigang ; GUO Shunsheng ; XU Renhe ; School of Mechanical Engineering ; Hubei University of Technology ; School of Mechanical and Electronic Engineering ; Wuhan University of Technology
• 年：2017
• 作者机构：School of Mechanical Engineering,Hubei University of Technology;School of Mechanical and Electronic Engineering,Wuhan University of Technology;
• 英文论文关键词：Improved bee algorithm ; production scheduling ; optimization ; textile machinery manufacturing
• 会议召开时间：2017-07-26
• 会议录名称：第36届中国控制会议论文集（B）
• 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
• 语种：英文
• 分类号：TP18;TS103
• 学会代码：KZLL
• 会议名称：第36届中国控制会议
• 会议地点：中国辽宁大连
• 主办单位：中国自动化学会控制理论专业委员会
• 学会名称：中国自动化学会控制理论专业委员会
• 页数：7
• 文件大小：521k
• 原文格式：D
• 会议级别：全国

摘要

Production scheduling is the key one of the basic means of production management, and the production scheduling optimization is one of the core technologies of modern management technology. According to the characteristics of quick response and order driven of production in textile machinery manufacturing enterprise, an optimal production scheduling mode is proposed which is based on improved bee algorithm. It enhances the diversity of the initial population by improving the quality of initial population and introducing the "cataclysm" operator which can avoid the precocious problem of algorithm. For some dynamic events in the actual production management of textile machinery manufacturing enterprise, a case study has been carried out, and the results show that the proposed approach is more preferably in solving the problem of workshop scheduling by comparing with the traditional bee algorithm.
Production scheduling is the key one of the basic means of production management, and the production scheduling optimization is one of the core technologies of modern management technology. According to the characteristics of quick response and order driven of production in textile machinery manufacturing enterprise, an optimal production scheduling mode is proposed which is based on improved bee algorithm. It enhances the diversity of the initial population by improving the quality of initial population and introducing the "cataclysm" operator which can avoid the precocious problem of algorithm. For some dynamic events in the actual production management of textile machinery manufacturing enterprise, a case study has been carried out, and the results show that the proposed approach is more preferably in solving the problem of workshop scheduling by comparing with the traditional bee algorithm.

引文

[1]Wang GuiRong,Li QiQiang,Yang Fan.Improved cross entropy algorithm for steelmaking continuous casting production scheduling with minimum power consumption[J].Journal of Dong Hua University(English Edition),2015,32(1):23-29.
    [2]Du Zhao Fang,Han JiangHong2,Liu Xiao-Ping.A method for solving textile production scheduling problem[J].Journal of Dong Hua University(English Edition),2011,28(2):182-186.
    [3]Wang Teng,Zhou BingH ai.Scheduling multiple orders per job with multiple constraints on identical parallel machines[J].Journal of Dong Hua University(English Edition),2013,30(6):466-471.
    [4]Li BeiZ hi,Yang Jian Guo,Zhou YaQ in,et al.Event-driven dynamic and intelligent scheduling for agile manufacturing based on immune mechanism and expert system[J].Journal of Dong Hua University(English Edition),2003,20(3):5-10.
    [5]Cheng R,Gen M,Tsujimura W.A tutorial survey of job-shop scheduling problems using genetic algorithms-I,representation[J].Computers&Industrial Engineering,1996,30(4):983-997.
    [6]Pezzella F,Merelli E.A tabu search method guided by shifting bottleneck for the job-shop scheduling problem[J].European Journal of Operational Research,2000,120(2):297-310.
    [7]Pezzella R,Morganti G.Ciaschetti G.A genetic algorithm for the Flexible Job-shop Scheduling Problem[J].Computers&Operations Research,2008,35(10):3202-3212.
    [8]Zhang C Y,Li P G,Rao Y Q.A very fast TS/SA algorithm for the job-shop scheduling problem[J].Computers and Operations Research,2008,35(1):282-294.
    [9]Heinonen J,Pettersson R.Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem[J].Applied Matematics and Computation,2010,187(2):989-998.
    [10]Kamrul H,Sarker R,Essam D.Memetic algorithms for solving job-shop scheduling problems[J].Memetic Computing,2009,1(1):69-83.
    [11]Sun L,Liang Y C,Qian F.An effective PSO and AIS-Based hybrid intelligent algorithm for Job-Shop scheduling[J].Journals&Magazines,2008,38(2):358-368.
    [12]Sha D Y,Lin H H.A multi-objective PSO for job-shop scheduling problems[J].Expert Systems with Applications,2010,37(2):1065-1070.
    [13]Wang Bing,Li Qiao yun,Yang Xiao fei,et al.Robust and Satisfactory Job shop Scheduling under Fuzzy Processing Times and Flexible Due Dates[J].International Conference on Automation and Logistics,2010:575-580.
    [14]Jiao Bin.Job Shop Scheduling Based on an Improved Cooperative Particle Swarm Optimization[J].2010International Conference on Measuring Technology and Mechatronics Automation,2010:532-536.
    [15]Yang Jian-Guo1,Li Bei-Zhi.Multi-objective scheduling using an artificial immune system[J].Journal of Dong Hua University(English Edition),2003,20(2):22-27.
    [16]Annapurani,K.;Sadiq,M.A.K.;Malathy,C.Fusion of shape of the ear and tragus-A unique feature extraction method for ear authentication system[J].Expert Systems with Applications,2015:42(1):649-656
    [17]Poggiolini,Mario;Engelbrecht,Andries.Application of the feature-detection rule to the Negative Selection Algorithm[J].Expert Systems with Applications,2013:40(8):3001-3014
    [18]Ventura,Jose A.;Yoon,Suk-Hun.A new genetic algorithm for lot-streaming flow shop scheduling with limited capacity buffers[J].JOURNAL OF INTELLIGENT MANUFACTURING,2013:24(6):1185-1196
    [19]Hongtao Tang,Xixing Li,Shunsheng Guo,et al.An optimizing model to solve the nesting problem of rectangle pieces based on genetic algorithm[J].Journal of Intelligent Manufacturing,2015,78:1865-1878
    [20]Du,B.,Guo,S.,Huang,X.,et al.A Pareto supplier selection algorithm for minimum the life cycle cost of complex product system[J].Expert Systems with Applications,2015.