Bacterial memetic algorithm for simultaneous optimization of path planning and flow shop scheduling problems

The paper deals with simultaneous optimization of path planning of mobile robots and flow shop scheduling problem. The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. The objective is to minimize the path length without colliding with an obstacle. On the other hand, shop scheduling problems deal with processing a given set of jobs on a given number of machines. Each operation has an associated machine on which it has to be processed for a given length of time. The problem is to minimize the overall time demand of the whole process. In this paper, we deal with two robots carrying items between the machines. Bacterial memetic algorithm is proposed for solving this combined problem. The algorithm is verified by experimental simulations and compared to classical techniques.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  M. Mirabi,et al.  Application of Hybrid Meta-Heuristic Approach to Solve Flow-Shop Scheduling Problem , 2011 .

[3]  J. Grabowski,et al.  The permutation flow shop problem with blocking. A tabu search approach , 2007 .

[4]  Naoyuki Kubota,et al.  Bacterial memetic algorithm for offline path planning of mobile robots , 2012, Memetic Comput..

[5]  László T. Kóczy,et al.  Eugenic bacterial memetic algorithm for fuzzy road transport traveling salesman problem , 2011 .

[6]  Zbigniew Michalewicz,et al.  Adaptive evolutionary planner/navigator for mobile robots , 1997, IEEE Trans. Evol. Comput..

[7]  Colin R. Reeves,et al.  A genetic algorithm for flowshop sequencing , 1995, Comput. Oper. Res..

[8]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[9]  Habib Izadkhah,et al.  Evolutionary Approach for Mobile Robot Path Planning in Complex environment , 2010 .

[10]  László T. Kóczy,et al.  Fuzzy rule extraction by bacterial memetic algorithms , 2009, Int. J. Intell. Syst..

[11]  Takeshi Furuhashi,et al.  Fuzzy system parameters discovery by bacterial evolutionary algorithm , 1999, IEEE Trans. Fuzzy Syst..

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .