This paper focused on the theme of AVS/RS with multi-RGV and multi-lift scheduling optimization. Firstly, we establish the corresponding mathematical model. Then, an encoding and decoding method is designed, which contains RGV task allocation and elevator selection information. Dynamic range vision and discrete adaptive step are introduced to accelerate the convergence speed and optimization direction. The mechanism of survival of the fittest groups appropriate is introduced to update the population so as to enhance the diversity of the population. Finally, we complete simulation based on the concrete living example of AVS/RS in a provincial verification center. The results obtained by the proposed algorithm are compared with another two optimization algorithm. Analysis shows that the proposed algorithm has the characteristics of fast convergence and could get the best solution.
[1] Luo, J., Wu, C.-Q. and Li, B., “Modeling and Optimization of RGV System Based on Improved QPSO,” Computer Integrated Manufacturing Systems, Vol. 17, No. 2, pp. 321328 (2011).
[2] Wu, C.-Q., He, S.-J. and Luo, J., “Cycle-deadlock Control of RGVs in Autonomous Vehicle Storage and Retrieval Systems,” Computer Integrated Manufacturing Systems, Vol. 14, No. 9, pp. 17671773 (2008).
[3] Fukunari, M. and Almborg, C. J., “A Network Queuing Approach for Evaluation of Performance Measures in Autonomous Vehicle Storage and Retrieval Systems,” European Journal of Operational Research, Vol. 193, No. 2, 152167 (2009). doi: 10.1016/j.ejor.2007.10.049
[4] Naderi, B., Ahmadi, A. and Javid, F., “Permutation Flow-shops with Transportation Times: Mathematical Models and Solution Methods,” International Journal of Advanced Manufacturing Technology, Vol. 46, pp. 631647 (2010). doi: 10.1007/s00170-009-2122-8
[5] Wang, S.-Y., Wang, L. and Xu, Y., “Estimation of Distribution Algorithm for Solving Hybrid Flow-shop Scheduling Problem with Identical Parallel Machine,” Computer Integrated Manufacturing Systems, Vol. 12, No. 6, pp. 1551 (2013). doi: 10.1007/s00170-013- 4819-y
[6] Wang, X. and Tang, L., “A Tabu Search Heuristic for the Hybrid Flow-shop Scheduling with Finite Intermediate Buffers,” Computers & Operations Research, Vol. 36, No. 3, pp. 907918 (2008). doi: 10.1016/j.cor. 2007.11.004
[7] Duan, Q.-C., et al., “Simulation Analysis of the Fish Swarm Algorithm Optimized by PSO,” Control and Decision, Vol. 28, No. 9, pp. 14361440 (2013).
[8] Gao, J., Chen, R. and Wu, D., “An Efficient Tabu Search Algorithm for the Distributed Permutation Flow-shop Scheduling Problem,” International Journal of Production Research, Vol. 51, No. 3, pp. 641 651 (2013). doi: 10.1080/00207543.2011.644819
[9] Sun, D.-H. and Song, X.-X., “Scheduling Model of Motorcycle Parallel Conveyor Belt Flow Shop and its Solution,” Computer Integrated Manufacturing Systems, Vol. 17, No. 2, pp. 294300 (2011).
[10] Xu, H. R. and Li, R., “An Adaptive Meta-cognitive Artificial Fish School Algorithm,” International Forum on Information Technology and Applications, pp. 156165 (2009). doi: 10.1109/IFITA.2009.352
[11] Yang, C. G., Tu, X. Y. and Chen, J., “Algorithm of Marriage in Honey Optimization Based on the Wolf Pack Search,” International Conference on Intelligent Pervasive Computing, pp. 462467 (2007). doi: 10. 1109/IPC.2007.104
We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.
