Applying Ant Colony Optimisation (ACO) algorithm to dynamic job shop scheduling problems

Ant Colony Optimization (ACO) is applied to two dynamic job scheduling problems, which have the same mean total workload but different dynamic levels and disturbing severity. Its performances are statistically analysed and the effects of its adaptation mechanism and parameters such as the minimal number of iterations and the size of searching ants are studied. The results show that ACO can perform effectively in both cases; the adaptation mechanism can significantly improve the performance of ACO when disturbances are not severe; increasing the size of iterations and ants per iteration does not necessarily improve the overall performance of ACO.

[1]  Narayan Raman,et al.  The job shop tardiness problem: A decomposition approach , 1993 .

[2]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .

[3]  Martin Middendorf,et al.  Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP , 2001, EvoWorkshops.

[4]  Michael Guntsch,et al.  Applying Population Based ACO to Dynamic Optimization Problems , 2002, Ant Algorithms.

[5]  Hartmut Schmeck,et al.  An Ant Colony Optimization approach to dynamic TSP , 2001 .

[6]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[7]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[8]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[9]  Erik D. Goodman,et al.  A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problem , 1997, ICGA.

[10]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[11]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[12]  Marco Dorigo,et al.  Ant colony optimization , 2006, IEEE Computational Intelligence Magazine.

[13]  M. Dorigo,et al.  Aco Algorithms for the Traveling Salesman Problem , 1999 .

[14]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[15]  Marco Dorigo,et al.  Ant system for Job-shop Scheduling , 1994 .

[16]  Tim Hendtlass,et al.  Ant Colony Optimisation Applied to a Dynamically Changing Problem , 2002, IEA/AIE.

[17]  Andrew Y. C. Nee,et al.  Simulating the generic job shop as a multi-agent system , 2008, Int. J. Intell. Syst. Technol. Appl..

[18]  Tobias Teich,et al.  Real-world Shop Floor Scheduling By Ant Colony Optimization , 2002, GECCO.

[19]  W. Punch,et al.  A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problems , 1997 .

[20]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[21]  Corso Elvezia,et al.  Ant colonies for the traveling salesman problem , 1997 .