Development of Both the AIS and PSO for Solving the Flexible Job Shop Scheduling Problem

The flexible job shop scheduling problem (FJSP) is to assign each operation to an appropriate machine and to sequence the operations on the machines. The paper describes the development and the application of the artificial immune system (AIS) and the particle swarm optimization (PSO) for solving the flexible job shop scheduling problem with sequence-dependent setup times (SDST-FJSP). A series of the experiments have been designed using the analysis of variance to recognize best settings of parameters. Finally, 30 examples of the different sizes in the SDST-FJSP with the objective of minimizing makespan and mean tardiness have been used to verify the performance of the proposed algorithms, and to compare them with the existing meta-heuristic algorithms in the literature, such as the genetic algorithm (GA), the parallel variable neighborhood search (PVNS), and the variable neighborhood search (VNS). The obtained results show that the proposed PSO outperforms the GA and the PVNS approaches. It is found that the average best-so-far solutions obtained from the proposed AIS are better than those produced by the GA, the PVNS, the VNS, and the PSO algorithms for all the examples.

[1]  Mostafa Zandieh,et al.  A robust genetic algorithm for scheduling realistic hybrid flexible flow line problems , 2010, J. Intell. Manuf..

[2]  Yih-Long Chang,et al.  A scatter search approach to sequence-dependent setup times job shop scheduling , 2009 .

[3]  Riccardo Poli,et al.  Particle Swarm Optimisation , 2011 .

[4]  Berna Haktanirlar Ulutas,et al.  A clonal selection algorithm for dynamic facility layout problems , 2009 .

[5]  F. Burnet.  The clonal selection theory of acquired immunity , 1959 .

[6]  Zhiming Wu,et al.  An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems , 2005, Comput. Ind. Eng..

[7]  Min Du,et al.  A three-fold approach for job shop problems: A divide-and-integrate strategy with immune algorithm , 2012 .

[8]  E Bonabeau,et al.  Swarm Intelligence: A Whole New Way to Think about Business , 2001 .

[9]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[10]  Jun Liu,et al.  A Modified Particle Swarm Optimization Algorithm and its Application For Solving Traveling Salesman Problem , 2005, 2005 International Conference on Neural Networks and Brain.

[11]  G L Ada,et al.  The clonal-selection theory. , 1987, Scientific American.

[12]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[13]  Hong Zhang,et al.  Particle swarm optimization for resource-constrained project scheduling , 2006 .

[14]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[15]  Chunguang Zhou,et al.  Particle swarm optimization for traveling salesman problem , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[16]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[17]  Orhan Engin,et al.  ARTIFICIAL IMMUNE SYSTEMS AND APPLICATIONS IN INDUSTRIAL PROBLEMS , 2004 .

[18]  Pierre Borne,et al.  Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[19]  Alper Döyen,et al.  A new approach to solve hybrid flow shop scheduling problems by artificial immune system , 2004, Future Gener. Comput. Syst..

[20]  Haixiang Guo,et al.  Intelligent optimization for project scheduling of the first mining face in coal mining , 2010, Expert Syst. Appl..

[21]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[22]  K. P. Murphy,et al.  Janeway's immunobiology , 2007 .

[23]  Chien-Ho Ko,et al.  Precast production scheduling using multi-objective genetic algorithms , 2011, Expert Syst. Appl..

[24]  Zuwairie Ibrahim,et al.  Data Clustering for the DNA Computing Readout Method Implemented on LightCycler and Based on Particle Swarm Optimization , 2012 .

[25]  Li Pheng Khoo,et al.  Solving the assembly configuration problem for modular products using an immune algorithm approach , 2003 .

[26]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[27]  N. Imanipour,et al.  Modeling & Solving Flexible Job Shop Problem With Sequence Dependent Setup Times , 2006, 2006 International Conference on Service Systems and Service Management.

[28]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[29]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[30]  Ata Allah Taleizadeh,et al.  Multi-product multi-chance-constraint stochastic inventory control problem with dynamic demand and partial back-ordering: A harmony search algorithm , 2012 .

[31]  D. Dasgupta,et al.  Advances in artificial immune systems , 2006, IEEE Computational Intelligence Magazine.

[32]  Dipankar Dasgupta,et al.  An Overview of Artificial Immune Systems and Their Applications , 1993 .

[33]  Mostafa Zandieh,et al.  Flexible job-shop scheduling with parallel variable neighborhood search algorithm , 2010, Expert Syst. Appl..

[34]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[35]  M. Zandieh,et al.  Bi-criteria flexible job-shop scheduling with sequence-dependent setup times—Variable neighborhood search approach , 2011 .

[36]  F. Pezzella,et al.  A genetic algorithm for the Flexible Job-shop Scheduling Problem , 2008, Comput. Oper. Res..

[37]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[38]  Mostafa Zandieh,et al.  An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times , 2006, Appl. Math. Comput..

[39]  Reza Tavakkoli-Moghaddam,et al.  A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: Weighted mean completion time and weighted mean tardiness , 2007, Inf. Sci..

[40]  P. Matzinger.  The Danger Model: A Renewed Sense of Self , 2002, Science.

[41]  I-Tung Yang,et al.  Performing complex project crashing analysis with aid of particle swarm optimization algorithm , 2007 .

[42]  Ronald G. Askin,et al.  Comparing scheduling rules for flexible flow lines , 2003 .

[43]  Petr Musílek,et al.  Immune programming , 2006, Inf. Sci..

[44]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[45]  R. J. Kuo,et al.  Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering , 2010, Decis. Support Syst..

[46]  Peter B. Luh,et al.  Job shop scheduling with group-dependent setups, finite buffers, and long time horizon , 1998, Ann. Oper. Res..

[47]  H. Al-Duwaish.  Identification of Hammerstein Models with Known Nonlinearity Structure Using Particle Swarm Optimization , 2011 .

[48]  Mohammad Saidi-Mehrabad,et al.  Flexible job shop scheduling with tabu search algorithms , 2007 .

[49]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[50]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[51]  H. Fan.  A modification to particle swarm optimization algorithm , 2002 .

[52]  Pupong Pongcharoen,et al.  Development of a stochastic optimisation tool for solving the multiple container packing problems. , 2012 .

[53]  Amir Sadrzadeh,et al.  A genetic algorithm with the heuristic procedure to solve the multi-line layout problem , 2012, Comput. Ind. Eng..

[54]  M. Kök,et al.  Modeling and Assessment of Some Factors that Influence Surface Roughness for the Machining of Particle Reinforced Metal Matrix Composites , 2011 .

[55]  Wufan Chen,et al.  A particle swarm optimization algorithm for beam angle selection in intensity-modulated radiotherapy planning , 2005, Physics in medicine and biology.