Intelligent Sequencing Problem of Multi-Variety Mixed-Model Production Line Based on Mean Residence Time

doi:10.11908/j.issn.0253-374x.19441

Home > Archive>Volume 48, Issue 11, 2020 >1676-1686. DOI:10.11908/j.issn.0253-374x.19441

Intelligent Sequencing Problem of Multi-Variety Mixed-Model Production Line Based on Mean Residence Time
DOI:
                        10.11908/j.issn.0253-374x.19441
                    
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                        LIU Jinfei1LIU Jinfei
Sino-German College of Applied Sciences， Tongji University， Shanghai 201804， China
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LI Jielin2LI Jielin
Institute of China Aeronautical Radio Electronics， Shanghai 200241， China
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MA Xueming3MA Xueming
School of Mechanical Engineering， Tongji University， Shanghai 201804， China
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Lin Hao4Lin Hao
Tesla （Shanghai） Co.， Ltd.， Shanghai 201306，China
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Affiliation:1.Sino-German College of Applied Sciences， Tongji University， Shanghai 201804， China;2.Institute of China Aeronautical Radio Electronics， Shanghai 200241， China;3.School of Mechanical Engineering， Tongji University， Shanghai 201804， China;4.Tesla (Shanghai) Co.， Ltd.， Shanghai 201306，China
Clc Number:TP391
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Abstract:

The frequent switching of tooling/fixture， tool/gauge and system utilizationse cases in the multi-variety mixed-model production line under the mode of "industrial 4.0" intelligent manufacturing can easily lead to abnormal production and short-term stopping risk in workstations. First， the mean residence time（MRT） was proposed by analyzing the uncertainties caused by product polymorphism in the multi-variety mixed-model production line. Next， this study by considerings the minimum production cycle of the MRT multi-variety mixed-model production line， establishes the product operation schedule based on the sequence of products entering the production line， and establishes an intelligent sequencing mathematical model by introducing the key variables of MRT to the objective of minimal cycle optimization. After that， based on the Palmer principle， an improved genetic algorithm was designed to solve the intelligent sequencing problem of the multi product mixed-model production line. Finally， the feasibility and validity of this method are verified by an example.

Key words:intelligent manufacturing;multi-variety mixed-model production;intelligent production sequencing;modified genetic algorithm;production residence time

Reference

[1] CARDIN O， OUNNAR F， THOMAS A， et al. Future industrial systems： Best practices of the intelligent manufacturing and services systems[J]. IEEE Transactions on Industrial Informatics， 2017， 13（4）： 704.

[2] 刘晋飞，陈明，姚远，等. 基于多 Agent 的产品模块化协同设计策略[J]. 计算机集成制造系统，2011， 17（3）：560.

[3] 陈明，周云龙，刘晋飞. 基于MDP的多Agent生产线动态调度策略[J]. 机电一体化，2017，23（11）：15.

[4] NODA A， PABLO D， GONZALEZ C. A network flow-based method to solve performance cost and make span open-shop scheduling problems with time-windows[J]. European Journal of Operational Research， 2009， 196（1）： 140.

[5] ALLAHVERDI A， NG C， CHENG T E， et al. A survey of scheduling problems with setup times or costs[J]. European Journal of Operational Research， 2008， 187（3）： 985.

[6] RABIEE M， RAD R S， MAZINANI M， et al. An intelligent hybrid meta-heuristic for solving a case of no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines[J]. International Journal of Advanced Manufacturing Technology， 2014， 71（5）： 1229.

[7] 李爱平，张燕红，郭海涛，等. 混流生产线车间内物料调度工位组划分优化法[J]. 同济大学学报（自然科学版），2017，45（8）：1191.

[8] SHAO X， WANG B， RAO Y， et al. Metaheuristic approaches to sequencing mixed-model fabrication/assembly systems with two objectives[J]. International Journal of Advanced Manufacturining， 2010 ， 48 （9/12）：1159.

[9] 王炳刚. 混流加工/装配系统集成优化研究[J].机械工程学报， 2010，46（17）：114.

[10] 陈明，刘江山，李杰林，等. 基于信息物理系统新特性的智能工厂部署策略研究[J]. 中国工程机械学报，2017，15（5）：383.

[11] SIALA M， HEBRARD E， HUGUET M. A study of constraint programming heuristics for the car-sequencing problem[J]. Engineering Applications of Artificial Intelligence， 2015， 38： 34.

[12] DERVIS K， BEYZA G， CELAL O， et al. A comprehensive survey： Artificial bee colony （ABC） algorithm and applications[J]. Artificial Intelligence Review， 2014， 42（1）： 21.

[13] PAN Q K， SUGANHAN P N， TASGETIREN M F， et al. A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem[J]. Information Sciences， 2011 ， 181（12）：2455.

[14] SAIF U ， GUAN Z， LIU W，et al. Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line[J]. International Journal of Advanced Manufacturing Technology， 2014 ， 75 （9/12）：1809.

[15] XU Y， WANG L， WANG S， et al. An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time[J]. Neurocomputing， 2015， 148： 260.

[16] CHANG H， CHEN Y， LIU T， et al. Solving the flexible job shop scheduling problem with makespan optimization by using a hybrid taguchi-genetic algorithm[J]. IEEE Access， 2015， 3： 1740.

[17] THAMMANO A， PHU-ANG A. A hybrid artificial bee colony algorithm with local search for flexible job-shop scheduling problem[J]. Procedia Computer Science， 2013， 20： 96.

[18] YUAN Y， XU H， YANG J. A hybrid harmony search algorithm for the flexible job shop scheduling problem[J]. Applied Soft Computing， 2013， 13（7）： 3259.

[19] ZHANG R， CHANG P， WU C. A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs： Investigations motivated by vehicle production[J]. International Journal of Production Economics， 2013， 145（1）： 38.

[20] KARTHIKEYAN S， ASOKAN P， NICKOLAS S， et al. A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems[J]. International Journal of Bio-Inspired Computation， 2015， 7（6）： 386.

[21] SUN L， LIN L， WANG Y， et al. A bayesian optimization-based evolutionary algorithm for flexible job shop scheduling[J]. Procedia Computer Science， 2015， 61： 521.

[22] LI J， PAN Q， TASGETIREN M F. A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities[J]. Applied Mathematical Modeling， 2014， 38（3）： 1111.

[23] GUO Weian， WANG Lei， CHEN Ming， et al. Design for a novel framework of hyper-heuristic algorithm[J]. Journal of Donghua University （English edition）， 2014， 31（2）：109.

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LIU Jinfei, LI Jielin, MA Xueming, Lin Hao. Intelligent Sequencing Problem of Multi-Variety Mixed-Model Production Line Based on Mean Residence Time[J].同济大学学报(自然科学版),2020,48(11):1676~1686

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Received:October 25,2019
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Online: December 01,2020
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