Article

Effective genetic approach for optimizing advanced planning and scheduling in flexible manufacturing system

Authors:

Mitsuo GenAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 1841 - 1848

https://doi.org/10.1145/1143997.1144293

Published: 08 July 2006 Publication History

Abstract

In this paper, a novel approach for designing chromosome has been proposed to improve the effectiveness, which called multistage operation-based genetic algorithm (moGA). The objective is to find the optimal resource selection for assignments, operations sequences, and allocation of variable transfer batches, in order to minimize the total makespan, considering the setup time, transportation time, and operations processing time. The plans and schedules are designed considering flexible flows, resources status, capacities of plants, precedence constraints, and workload balance in Flexible Manufacturing System (FMS). The experimental results of various Advanced Planning and Scheduling (APS) problems have offered to demonstrate the efficiency of moGA by comparing with the previous methods.

References

[1]

Kusiak, A. Computational Integrated in Design & Manufacturing. John Wiley & Sons, New York, 2000.

Digital Library

[2]

Turbide, D. Advanced Planning and Scheduling (APS) Systems. Midrange ERP Magazine, 1998.

[3]

Eck, M. Advanced Planning and Scheduling: Is logistic everything? A research on the use (fullness) of advanced planning and scheduling systems. Technique report, Vrije University, Amsterdam, Holland. 2003.

[4]

Tan, W. Integration of process planning and scheduling - a review. Journal of Intelligent Manufacturing, 11,1, 2000, 51--63.

[5]

Tan, W. A linearized polynomial mixed integer programming model for the integration of process planning and scheduling. Journal of Intelligent Manufacturing, 15, 5, 2004, 593--605.

[6]

Kolisch, R., and Hess, K. Efficient methods for scheduling make-to-order assemblies under resource, assembly area and part availability constraints. International Journal of Production Research, 38, 2000, 207--228.

[7]

Dellaert, N., Jeunet, J., and Jornard, N. A genetic algorithm to solve the general multi-level lot-sizing problem with time-varying costs. International Journal of Production Economy, 68, 2000, 241--257.

[8]

Chung, D., Lee, K., Shin, K., and Park, J. An algorithm for job shop scheduling problem with due date constraints considering operation subcontract. In Proc. of 15th International Conference on Production Research, 2000.

[9]

Gen, M., and Syalif, A. Hybrid genetic algorithm for multi-time period production /distribution planning. Computers & Industrial Engineering, 48, 4, 2005, 799--809.

Digital Library

[10]

Kacem, I., Hammadi, S., and Borne, P. Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man and Cybernetics, Part C, 32, 1, 2002, 408--419.

Digital Library

[11]

Najid, N.M., Dauzere-Peres, S., and Zaidat, A. A modified simulated annealing method for flexible job shop scheduling problem. In Proc. of IEEE International Conference on Systems, Man and Cybernetics, 5, 2002, Pages 6.

[12]

Lopez, O., and Ramirez, M. A STEP-based manufacturing information system to share flexible manufacturing resources data. Journal of Intelligent Manufacturing, 16, 3, 2005, 287--301.

[13]

Moon, C., Kim, J. S., and Gen, M. Advanced planning and scheduling based on precedence and resource constraints for e-plant chains. International Journal of Production Research, 42, 15, 2004, 2941--2955.

[14]

Moon, C., and Seo, Y. Evolutionary Algorithm for Advanced Process Planning and Scheduling in a Multi-plant. Computer and Industrial Engineering, 48, 2, 2005, 311--325.

Digital Library

[15]

Moon, C. Evolutionary System Approach for Advanced Planning in Multi-Plant Chain. PhD thesis, Waseda University, Japan, 2004.

[16]

Gen, M., and Cheng R. Genetic Algorithms and Engineering Design, John Wiley & Sons, New York, 1997.

Digital Library

[17]

Ulusoy, G., Sivrikaya-Serifoglu, F., and Bilge, U. A genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles. Computers & Operations Research, 24, 4, 1997, 335--351.

Digital Library

[18]

Gonçalves, J. F., Magalhães Mendes J. J., and Resende, M. G. C. A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operational Research, 167, 1, 2005, 77--95.

[19]

Gen, M., and Cheng, R. Genetic Algorithms & Engineering Optimization. John Wiley & Sons, New York, 2000.

Digital Library

[20]

Zhang, H., and Gen, M. Multistage-based genetic algorithm for flexible job-shop scheduling problem. Journal of Complexity International, 11, 2005, 223--232.

Cited By

Sukkerd WWuttipornpun T(2016)Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operationsComputers and Industrial Engineering10.1016/j.cie.2016.05.00697:C(157-169)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.cie.2016.05.006
Mejía GGutiérrez E(2016)Scheduling Complex Manufacturing Systems Using a Genetic AlgorithmMetaheuristics for Production Systems10.1007/978-3-319-23350-5_10(223-240)Online publication date: 2016
https://doi.org/10.1007/978-3-319-23350-5_10
Gen MLin L(2014)Multiobjective evolutionary algorithm for manufacturing scheduling problemsJournal of Intelligent Manufacturing10.1007/s10845-013-0804-425:5(849-866)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1007/s10845-013-0804-4
Show More Cited By

Index Terms

Effective genetic approach for optimizing advanced planning and scheduling in flexible manufacturing system
1. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
2. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2006

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GECCO06: Genetic and Evolutionary Computation Conference

July 8 - 12, 2006

Washington, Seattle, USA

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GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Sukkerd WWuttipornpun T(2016)Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operationsComputers and Industrial Engineering10.1016/j.cie.2016.05.00697:C(157-169)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.cie.2016.05.006
Mejía GGutiérrez E(2016)Scheduling Complex Manufacturing Systems Using a Genetic AlgorithmMetaheuristics for Production Systems10.1007/978-3-319-23350-5_10(223-240)Online publication date: 2016
https://doi.org/10.1007/978-3-319-23350-5_10
Gen MLin L(2014)Multiobjective evolutionary algorithm for manufacturing scheduling problemsJournal of Intelligent Manufacturing10.1007/s10845-013-0804-425:5(849-866)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1007/s10845-013-0804-4
Zhou XFu Y(2011)Optimized E-Cash Scheme and its Formal AnalysisAdvanced Materials Research10.4028/www.scientific.net/AMR.204-210.1322204-210(1322-1325)Online publication date: Feb-2011
https://doi.org/10.4028/www.scientific.net/AMR.204-210.1322
Zhou XFu Y(2011)Blind Signature Based on Discrete Logarithm Type CryptosystemAdvanced Materials Research10.4028/www.scientific.net/AMR.204-210.1318204-210(1318-1321)Online publication date: Feb-2011
https://doi.org/10.4028/www.scientific.net/AMR.204-210.1318
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https://doi.org/10.4028/www.scientific.net/AMM.20-23.706
Zhou X(2010)Improved Signcryption Schemes Based on Hyper-Elliptic Curves CryptosystemApplied Mechanics and Materials10.4028/www.scientific.net/AMM.20-23.54620-23(546-552)Online publication date: Jan-2010
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Zhou X(2010)Scheme Optimization in Key Management with Cryptograph MethodsApplied Mechanics and Materials10.4028/www.scientific.net/AMM.20-23.53920-23(539-545)Online publication date: Jan-2010
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Zhou X(2010)Optimized Electronic Cash Scheme with Fast Public Key AlgorithmApplied Mechanics and Materials10.4028/www.scientific.net/AMM.20-23.53220-23(532-538)Online publication date: Jan-2010
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Zhou X(2010)Further Study on System Optimization in Proxy SignatureApplied Mechanics and Materials10.4028/www.scientific.net/AMM.20-23.51820-23(518-524)Online publication date: Jan-2010
https://doi.org/10.4028/www.scientific.net/AMM.20-23.518
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