Abstract
This paper focuses on implementing differential evolution (DE) to optimize the robotic assembly line balancing (RALB) problems with an objective of minimizing energy consumption in a straight robotic assembly line and thereby help to reduce energy costs. Few contributions are reported in literature addressing this problem. Assembly line balancing problems are classified as NP-hard, implying the need of using metaheuristics to solve realistic sized problems. In this paper, a well-known metaheuristic algorithm differential evolution is utilized to solve the problem. The proposed algorithm is tested on benchmark problems and the obtained results are compared with current state. It can be seen that the proposed DE algorithm is able to find a better solution for the considered objective function. Comparison of the computational time along with the cycle time is presented in detail.
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References
1. Liu, Y., Dong, H., Lohse, N., Petrovic, S., Gindy, N.: An investigation into minimising total energy consumption and total weighted tardiness in job shops. Journal of Cleaner Production 65, 87–96 (2014)
2. Mouzon, G., Yildirim, M.B.: A framework to minimise total energy consumption and total tardiness on a single machine. International Journal of Sustainable Engineering 1, 105–116 (2008)
3. Levitin, G., Rubinovitz, J., Shnits, B.: A genetic algorithm for robotic assembly line balancing. European Journal of Operational Research 168, 811–825 (2006)
4. Relich, M., Pawlewski, P.: A multi-agent system for selecting portfolio of new product development projects. International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 102–114. Springer (2015)
5. Vincent, L.W.H., Ponnambalam, S.: Scheduling flexible assembly lines using differential evolution. International Conference on Swarm, Evolutionary, and Memetic Computing, pp. 43–50. Springer (2011)
6. Nilakantan, J.M., Huang, G.Q., Ponnambalam, S.: An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems. Journal of Cleaner Production 90, 311–325 (2015)
7. Nilakantan, J.M., Nielsen, I., Ponnambalam, S., Venkataramanaiah, S.: Differential evolution algorithm for solving RALB problem using cost-and time-based models. The International Journal of Advanced Manufacturing Technology 1–22 (2016)
8. Janardhanan, M.N., Nielsen, P., Ponnambalam, S.: Application of Particle Swarm Optimization to Maximize Efficiency of Straight and U-Shaped Robotic Assembly Lines. Distributed Computing and Artificial Intelligence, 13th International Conference, pp. 525–533. Springer (2016)
9. Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11, 341–359 (1997)
10. Wang, G.-G., Hossein Gandomi, A., Yang, X.-S., Hossein Alavi, A.: A novel improved accelerated particle swarm optimization algorithm for global numerical optimization. Engineering Computations 31, 1198–1220 (2014)
11. Davis, L.: Applying adaptive algorithms to epistatic domains. IJCAI, vol. 85, pp. 162–164 (1985)
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Janardhanan, M.N., Nielsen, P., Li, Z., Ponnambalam, S.G. (2018). Minimizing energy consumption in a straight robotic assembly line using differential evolution algorithm. In: Omatu, S., Rodríguez, S., Villarrubia, G., Faria, P., Sitek, P., Prieto, J. (eds) Distributed Computing and Artificial Intelligence, 14th International Conference. DCAI 2017. Advances in Intelligent Systems and Computing, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-62410-5_6
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DOI: https://doi.org/10.1007/978-3-319-62410-5_6
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