Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach

This article investigates metamodeling opportunities in buffer allocation and performance modeling in asynchronous assembly systems (AAS). Practical challenges to properly design these complex systems are emphasized. A critical review of various approaches in modeling and evaluation of assembly systems reported in the recently published literature, with a special emphasis on the buffer allocation problems, is given. Various applications of artificial intelligence techniques on manufacturing systems problems, particularly those related to artificial neural networks, are also reviewed. Advantages and the drawbacks of the metamodeling approach are discussed. In this context, a metamodeling application on AAS buffer design/performance modeling problems in an attempt to extend the application domain of metamodeling approach to manufacturing/assembly systems is presented. An artificial neural network (ANN) metamodel is developed for a simulation model of an AAS. The ANN and regression metamodels for each AAS are compared with respect to their deviations from the simulation results. The analysis shows that the ANN metamodels can successfully be used to model of AASs. Consequently, one concludes that practising engineers involved in assembly system design can potentially benefit from the advantages of the metamodeling approach.

[1]  R. H. Hollier,et al.  Inter-stage stock control in a series production system with different numbers of parallel machines at each stage , 1982 .

[2]  Yannis A. Phillis,et al.  A CONTINUOUS-FLOW MODEL FOR PRODUCTION NETWORKS WITH FINITE BUFFERS, UNRELIABLE MACHINES, AND MULTIPLE PRODUCTS , 1997 .

[3]  Michael J. Shaw,et al.  A neural-net approach to real time flow-shop sequencing , 2000 .

[4]  Akif Asil Bulgak,et al.  Modeling and design optimization of asynchronous flexible assembly systems with statistical process control and repair , 1991 .

[5]  Berna Dengiz,et al.  Computer simulation of a PCB production line: metamodeling approach , 2000 .

[6]  A. T. C. Goh,et al.  Back-propagation neural networks for modeling complex systems , 1995, Artif. Intell. Eng..

[7]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[8]  Young Hoon Lee,et al.  Scheduling jobs on parallel machines applying neural network and heuristic rules , 2000 .

[9]  Joseph Masso,et al.  Interstage Storages for Three Stage Lines Subject to Stochastic Failures , 1974 .

[10]  Kai Yang,et al.  Application of artificial neural network to identify non-random variation patterns on the run chart in automotive assembly process , 2003 .

[11]  Janet M. Twomey,et al.  Validation and Verification , 1997 .

[12]  Ashu Jain,et al.  A comparative analysis of training methods for artificial neural network rainfall-runoff models , 2006, Appl. Soft Comput..

[13]  Taho Yang,et al.  Design of manufacturing systems by a hybrid approach with neural network metamodelling and stochastic local search , 2002 .

[14]  Y. C. Ho,et al.  A New Approach to Determine Parameter Sensitivities of Transfer Lines , 1983 .

[15]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[16]  Yoon-Hyun Kim,et al.  Heuristics for selecting machines and determining buffer capacities in assembly systems , 2000 .

[17]  J. A. Buzacott,et al.  Models of automatic transfer lines with inventory banks a review and comparison , 1978 .

[18]  A. A. Bulgak,et al.  Robust design of asynchronous flexible assembly systems , 1999 .

[19]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[20]  Surendra M. Gupta,et al.  A methodology for analyzing finite buffer tandem manufacturing systems with N -policy , 1998 .

[21]  Hajime Yamashina,et al.  Analysis of in-process buffers for multi-stage transfer line systems , 1983 .

[22]  Larry J. Shuman,et al.  Computing confidence intervals for stochastic simulation using neural network metamodels , 1999 .

[23]  Charles R. Standridge,et al.  Modeling and Analysis of Manufacturing Systems , 1993 .

[24]  Dong-Jo Park,et al.  Optimal buffer allocation of serial production lines with quality inspection machines , 2002 .

[25]  Ihsan Sabuncuoglu,et al.  Simulation metamodelling with neural networks: An experimental investigation , 2002 .

[26]  H. T. Papadopoulos,et al.  Optimal buffer allocation in short m -balanced unreliable production lines , 1999 .

[27]  R. D. Hurrion,et al.  A comparison of factorial and random experimental design methods for the development of regression and neural network simulation metamodels , 1999, J. Oper. Res. Soc..

[28]  William L. Maxwell,et al.  The Role of Work-in-Process Inventory in Serial Production Lines , 1988, Oper. Res..

[29]  Jerry Y. H. Fuh,et al.  A neural network approach for early cost estimation of packaging products , 1998 .

[30]  Harry G. Perros,et al.  Open Networks of Queues with Blocking: Split and Merge Configurations: , 1986 .

[31]  Hajime Yamashina,et al.  Justification for Installing Buffer Stocks in Unbalanced Two Stage Automatic Transfer Lines , 1979 .

[32]  George Chryssolouris,et al.  Manufacturing Systems: Theory and Practice , 1992 .

[33]  김호균,et al.  불완전한 기계 및 랜덤가공시간을 갖는 폐쇄형 생산시스템의 성능분석에 관한 연구 ( Performance Analysis for Closed-Loop Production Systems with Unreliable Machines and Random Processing Times ) , 1999 .

[34]  K. K. Chan,et al.  On-line optimization of quality in a manufacturing system , 2001 .

[35]  Noureddine Zerhouni,et al.  Control of Manufacturing Systems Using Neural Networks , 1995, EUROSIM.