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Paper Title : Flower Pollination Algorithm for Scheduling Optimization of Flexible Manufacturing System

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.6630708

Authors: Gayathri Devi K, RS Mishra, AK Madan

         


Abstract
Flexible manufacturing system (FMS) is a part of smart manufacturing and their implementation is on the rise in manufacturing industrial scenario. The need of FMS arises in order to meet the customized demands of consumers. It is highly complicated due to its flexibility, tools and AGVs which makes it unpredictable dynamic manufacturing environment. Finding an optimal schedule is relatively easy for small instance problems, whereas large realistic problems are difficult to solve. Recently, many swarm intelligence techniques have been developed and implemented to achieve an optimal schedule for FMS. Flower Pollination Algorithm (FPA) is one of the swarm intelligence techniques which mimics the behaviour of pollination of flowering plants. This paper proposes a Hybrid Flower Pollination Algorithm (HFPA) which is an amalgamation of classic FPA with Simulated Annealing (SA) to solve two objectives-1) to minimise the machine idle time and 2) to minimise the total penalty cost. The algorithm’s efficiency is demonstrated through comparison of HFPA with other meta heuristics found in literature and it is concluded that HFPA gives better results in less computational time.

Reference
[1] J. Liu and B. L. Maccarth, “The classification of FMS scheduling problems,” International Journal of Production Research, vol. 34, no. 3, pp. 647–656, 1996, doi: 10.1080/00207549608904925. [2] R. Sharma, “Implementation Issues in FMS : A Literature Review,” International Journal of Innovations in Engineering and Technology, vol. 2, no. 2, 2013. [3] C. C. Wu, P. H. Hsu, and K. Lai, “Simulated-annealing heuristics for the single-machine scheduling problem with learning and unequal job release times,” Journal of Manufacturing Systems, vol. 30, no. 1, pp. 54–62, 2011, doi: 10.1016/j.jmsy.2011.03.004. [4] A. Noorul Haq, T. Karthikeyan, and M. Dinesh, “Scheduling decisions in FMS using a heuristic approach,” International Journal of Advanced Manufacturing Technology, vol. 22, no. 5–6, pp. 374–379, 2003, doi: 10.1007/s00170-002-1474-0. [5] J. Jerald, P. Asokan, G. Prabaharan, and R. Saravanan, “Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm,” International Journal of Advanced Manufacturing Technology, vol. 25, no. 9–10, pp. 964–971, 2005, doi: 10.1007/s00170-003-1933-2. [6] V. K. Chawla, A. K. Chanda, and S. Angra, “Simultaneous Dispatching and Scheduling of Multi-Load AGVs in FMS-A Simulation Study,” in Materials Today: Proceedings, 2018, vol. 5, no. 11, pp. 25358–25367. doi: 10.1016/j.matpr.2018.10.339. [7] C. Zhao and Z. Wu, “A genetic algorithm approach to the scheduling of FMSs with multiple routes,” International Journal of Flexible Manufacturing Systems, vol. 13, no. 1, pp. 71–88, 2001, doi: 10.1023/A:1008148313360. [8] R. Saravanan, “Fms Scheduling Optimization Using Modified Nsga-Ii,” International Journal of Mechanical and Production Engineering, vol. 2, no. 2, pp. 1–6, 2014. [9] R. Pandey and A. Singh, “Utilization of AGVs and Machines in FMS Environment,” Journal of Material Science & Engineering, vol. 5, no. 4, 2016, doi: 10.4172/2169-0022.1000263. [10] K. Prakash Babu, V. Vijaya Babu, and N. R. Medikondu, “Implementation of heuristic algorithms to synchronized planning of machines and AGVs in FMS,” Management Science Letters, vol. 8, pp. 543–554, 2018, doi: 10.5267/j.msl.2018.5.001. [11] M. Abdel-Baset and I. Hezam, “A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems,” International Journal of Computer Applications, vol. 140, no. 12, pp. 10–23, 2016, doi: 10.5120/ijca2016909119. [12] M. U. N. Khursheed et al., “Review of Flower Pollination Algorithm: Applications and Variants,” 2020 International Conference on Engineering and Emerging Technologies, ICEET 2020, pp. 1–6, 2020, doi: 10.1109/ICEET48479.2020.9048215. [13] A. Phuang, “The flower pollination algorithm with disparity count process for scheduling problem,” 2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017, vol. 2018-Janua, pp. 1–5, 2017, doi: 10.1109/ICITEED.2017.8250497. [14] W. Xu, Z. Ji, and Y. Wang, “A flower pollination algorithm for flexible job shop scheduling with fuzzy processing time,” Modern Physics Letters B, vol. 32, no. 34–36, 2018, doi: 10.1142/S0217984918401139. [15] Dr. R. D. and Dr. P. R. K. Sivarami Reddy N, “Simultaneous Scheduling of Machines and Agvs Using Flower Pollination Algorithm: a New Nature-Inspired Meta-Heuristic,” Manufacturing Technology Today, vol. 17, no. 7, pp. 19–30, 2018. [16] Dr. R. S. Nidhish Mathew Nidhiry, “A COMBINED OBJECTIVE GENETIC ALGORITHM FOR SCHEDULING OF MACHINES IN FMS,” International Journal of Engineering Research and Applications (IJERA) ISSN:, vol. 3, no. 2, 2012. [17] K. Gayathri Devi, R. S. Mishra, and A. K. Madan, “Combined Objective Optimization of FMS Scheduling by a Hybrid Genetic Algorithm,” in Advances in Mechanical and Materials Technology, 2022, pp. 125–139.
Keywords
- Flexible manufacturing system, Hybrid Flower Pollination algorithm, scheduling, multi-objective.