PENYELESAIAN MULTI-OBJECTIVE FLEXIBLE JOB SHOP SCHEDULING PROBLEM MENGGUNAKAN HYBRID ALGORITMA IMUN

https://doi.org/10.22146/teknosains.22901

Yabunayya Habibi(1*), Galandaru Swalaganata(2), Aprilia Divi Yustita(3)

(1) Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Teknologi Sepuluh Nopember
(2) Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Teknologi Sepuluh Nopember
(3) Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Teknologi Sepuluh Nopember
(*) Corresponding Author

Abstract


Flexible Job shop scheduling problem (FJSSP) is one of scheduling problems with specification: there is a job to be done in a certain order, each job contains a number of operations and each operation is processed on a machine of some available machine. The purpose of this paper is to solve Multi-objective Flexible Job Shop scheduling problem with minimizing the makespan, the biggest workload and the total workload of all machines. Because of complexity these problem, a integrated approach Immune Algorithm (IA) and Simulated Annealing (SA) algorithm are combined to solve the multi-objective FJSSP. A clonal selection is a strategy for generating new antibody based on selecting the antibody for reproduction. SA is used as a local search search algorithm for enhancing the local ability with certain probability to avoid becoming trapped in a local optimum. The simulation result have proved that this hybrid immune algorithm is an efficient and effective approach to solve the multi-objective FJSSP


Keywords


Flexible Job Shop Scheduling; Immune Algorithm; Multi-objective optimization; Simulated Annealing

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References

Bagheri, A. Zandieh, M. Mahdavi, I. Yazdani, M. 2010. An Artificial Immune Algorithm for The Flexible Job Shop Scheduling Problem. Future Generation Computer Systems 26. 533–541.

Baykasoglu, A. Ozbakir, L. dan Sonmez, A.I. 2004. Using multiple objective tabu search and grammars to model and solve multi-objective flexible job-shopscheduling problems. Journal of Intelligent Manufacturing. 15(6): 777–785.

Bondal, A. 2008. Artificial Immune System Applied to Job Shop Scheduling. Master Thesis. Russ College of Engineering and Technology of Ohio University.

Brandimarte, P. 1993. Routing and Scheduling in a Flexible Job Shop by Taboo Search. Annals of Operation Research 41. 157–183.

Bruker, P. dan Schlie, R. 1990. Job Shop Scheduling with multi-purpose machine. Computing 45. 369–375

Chaudhry, I.A. Khan, A.M. dan Khan, A.A. 2013. A Genetic Algorithm for Felxible Job Shop Scheduling. Proceeding of the World Congress on Engineering I. 1–6.

Chen, L. Ihlow, J. Lehmann, C. 1999. A Genetic Algorithm for Flexible Job Shop Scheduling. IEEE Internasional Conference on Robotics and Automation. Detroit. 1120–1125.

Chibante, R. 2010. Simulated Annealing Theory with Application. Sciyo. Rijeka. Croatia.

Chu, C.W. Lin, M.D. Liu, G.F. dan Sung, Y.H. 2008, Application of Immune Algorithms on Solving Minimum-Cost Problem of Water Distribution Network. Mathematical and Computer Modelling 48(11-12). 1888–1900.

Fattahi, P. Saidi, M.M. Jolai, F. 2007. Mathematical Modeling and Heuristic Approach to Flexible Job Shop Scheduling Problem. Kournal of Intelligent Manufacturing 18(3). 331–342.

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

Jia, H.Z. Nee, A.Y.C. Fuh, J.Y.H. Zhang, Y.F. 2003. A Modified Genetic Algorithm for Distributed Scheduling Problems. Internasional Journal of Intelligent Manufacturing 14. 351–362.

Kacem, I, Hammadi, S. Borne, P. 2002. Approach by Localization and Multi-Objetive Evolutionary Optimization for Flexible Job Shop Scheduling Problem. IEEE Transaction on Systems, Man, and Cybernetics, Part C 32 (1). 1–13.

Larijani, A.M. Laghaie, K.S. Heydari, M. 2010. Solving Flexible Job Shop Scheduling with Multi Objective Approach. Internasional Journal of Industrial Engineering & Production Research 21(4). 197–209.

Wang, X. Gao, X.Z. Ovasca S.J. 2008. A Simulated Annealing-Based Immune Optimization Method. Proceedings of the 2nd International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning. 41–47.

Xia, W. J. dan Wu, Z.M. 2005. An effective hybrid optimization approach for multiobjective flexible job-shop scheduling problems. Computers and Industrial Engineering 48(2). 409–425.

Zhang, G. Shao, X. Li, P. Gao, L. 2009. An Effective Hybrid Particle Swarm Optimization Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problem. Computers & Industrial Engineering. 56(4). 1309-1318.



DOI: https://doi.org/10.22146/teknosains.22901

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