A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems

A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the KBACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then applies the existing knowledge to guide the current heuristic searching. The performance of KBACO was evaluated by a large range of benchmark instances taken from literature and some generated by ourselves. Final experimental results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules.

[1]  Ling Wang,et al.  A hybrid genetic algorithm-neural network strategy for simulation optimization , 2005, Appl. Math. Comput..

[2]  J. A. Spim,et al.  Mathematical modeling and optimization strategies (genetic algorithm and knowledge base) applied to the continuous casting of steel , 2003 .

[3]  Luca Maria Gambardella,et al.  Effective Neighborhood Functions for the Flexible Job Shop Problem , 1998 .

[4]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[5]  Emanuela Merelli,et al.  A tabu search method guided by shifting bottleneck for the job shop scheduling problem , 2000, Eur. J. Oper. Res..

[6]  Robert G. Reynolds,et al.  A Testbed for Solving Optimization Problems Using Cultural Algorithms , 1996, Evolutionary Programming.

[7]  Klaus Jansen,et al.  Approximation schemes for job shop scheduling problems with controllable processing times , 2005, Eur. J. Oper. Res..

[8]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[9]  Pierre Borne,et al.  Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic , 2002, Math. Comput. Simul..

[10]  Dong Guo Shao,et al.  A novel evolution strategy for multiobjective optimization problem , 2005 .

[11]  Yin Ai-hua,et al.  An improved shifting bottleneck procedure for the job shop scheduling problem , 2004 .

[12]  Daniel J. Fonseca,et al.  Artificial neural networks for job shop simulation , 2002, Adv. Eng. Informatics.

[13]  Ivan Tanev,et al.  Hybrid evolutionary algorithm-based real-world flexible job shop scheduling problem: application service provider approach , 2004, Appl. Soft Comput..

[14]  T. M. English.  Proceedings of the third annual conference on evolutionary programming: A.V. Sebald and L.J. Fogel, River Edge, NJ: World Scientific, ISBN 981-02-1810-9, 371 pages, hardbound, $78 , 1995 .

[15]  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.

[16]  Sushil J. Louis,et al.  Learning with case-injected genetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[17]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[18]  Mauricio G. C. Resende,et al.  A hybrid genetic algorithm for the job shop scheduling problem , 2005, Eur. J. Oper. Res..

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

[20]  Weixiong Zhang,et al.  Cut-and-solve: An iterative search strategy for combinatorial optimization problems , 2006, Artif. Intell..

[21]  Ryszard S. Michalski,et al.  LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning , 2004, Machine Learning.

[22]  Nhu Binh Ho,et al.  An effective architecture for learning and evolving flexible job-shop schedules , 2007, Eur. J. Oper. Res..

[23]  Robert G. Reynolds,et al.  Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[24]  Li Lin,et al.  Multiple-Objective Scheduling for the Hierarchical Control of Flexible Manufacturing Systems , 1999 .

[25]  Paolo Brandimarte,et al.  Routing and scheduling in a flexible job shop by tabu search , 1993, Ann. Oper. Res..

[26]  Johann L. Hurink,et al.  Tabu search for the job-shop scheduling problem with multi-purpose machines , 1994 .

[27]  Yuping Wang,et al.  An orthogonal genetic algorithm with quantization for global numerical optimization , 2001, IEEE Trans. Evol. Comput..

[28]  Ling Wang,et al.  An effective hybrid optimization strategy for job-shop scheduling problems , 2001, Comput. Oper. Res..

[29]  Amitava Dutta,et al.  Integrating Heuristic Knowledge and Optimization Models for Communications Network Design , 1993, IEEE Trans. Knowl. Data Eng..

[30]  Peter B. Luh,et al.  An alternative framework to Lagrangian relaxation approach for job shop scheduling , 2003, Eur. J. Oper. Res..

[31]  Andrew Kusiak.  Process planning: a knowledge-based and optimization perspective , 1991, IEEE Trans. Robotics Autom..

[32]  Ching-Jong Liao,et al.  Ant colony optimization combined with taboo search for the job shop scheduling problem , 2008, Comput. Oper. Res..

[33]  Raghunathan Rengaswamy,et al.  A framework for integrating diagnostic knowledge with nonlinear optimization for data reconciliation and parameter estimation in dynamic systems , 2001 .

[34]  Stéphane Dauzère-Pérès,et al.  An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search , 1997, Ann. Oper. Res..

[35]  Peter Brucker,et al.  Job-shop scheduling with multi-purpose machines , 1991, Computing.

[36]  Michael Kolonko.  Some new results on simulated annealing applied to the job shop scheduling problem , 1999, Eur. J. Oper. Res..