Abstract
Assembly line task scheduling (ALTS) or simple task scheduling (TS) is a process in which the cost of operations like time and resources is minimized by scheduling the required task amongst the units that are available at any given instance. In TS problem, problem formulation is carried out as a nonlinear constrained optimization and the facets of inequality and equality constraints are engaged to test the problem. In our paper, we consider the cuckoo search algorithm (CSA) and firefly algorithm (FA) for the implementation on a system of ALTS. By using the CS and FA, we try to obtain simulation results on the preliminary level, and then, they are compared to see their veracity of effectiveness on a group of tasks. Our paper focuses mainly on the preliminary investigations of usage of these two notable algorithms to gain an insight of their convention on scheduling activities effectively that are NP-hard normally. We also touch the issue of optimality required for exploration and exploitation in these metaheuristic algorithms. Our results indicate the efficacy of the cuckoo search algorithm and firefly algorithm and prove their potential to solve the ALTS problem in terms of multi-dimensional optimization problem.
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Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, London (2008)
Yang, X.S.: Firefly algorithms for multimodal optimization. Stochastic algorithms: foundations and applications. In: SAGA, Lecture Notes in Computer Science, pp. 169–178 (2009)
Fister, I., Jr., Yang, X.S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. arXiv:1307.4186v1 (CS.NE) (2013)
Cui, H., Liu, X., Yu, T., Zhang, H., Fang, Y., Xia, Z.: Cloud service scheduling algorithm. Res. Optim. Secur. Commun. Netw. 2017, 7 (2017). Article ID 2503153
Yang, D., Liu, Z., Zhou, J.: Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization. Commun. Nonlinear Sci. Numer. Simul. 19(4), 1229–1246 (2014)
Dolgui, A., Gafarov, E.: Some new ideas for assembly line balancing research. In: IFAC (International Federation of Automatic Control) PapersOnLine 50(1), 2255–2259 (2017). Elsevier
Chabria, S.A., Dharaskar, R.V.: Multimodal interface for disabled persons. Int. J. Comput. Sci. Commun., 02–08 (2012)
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press (2010)
Yang, X.S., Deb, S.: Cuckoo search via levy flights in nature & biologically inspired computing. In: World Congress on NaBIC, pp. 210–214. IEEE (2009)
Sur, C., Sharma, S., Shukla, A.: Egyptian vulture optimization algorithm—a new nature Inspired metaheuristics for knapsack problem. In: The 9th International Conference on Computing and Information Technology (IC2IT2013), pp. 227–237. Springer (2013)
Shi, Y.: An optimization algorithm based on Brainstorming process. Int. J. Swarm Intell. Res. (IJSIR) 2(4), 35–62 (2011)
Ting, T., Man, K.L., Guan, S.U., Nayel, M., Wan, K.: Weightless swarm algorithm (WSA) for dynamic optimization problems. In: Network and Parallel Computing, pp. 508–515. Springer (2012)
Fister, I., Fister Jr., I., Yang, X.S., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13, 34–46 (2013)
Yang, X.S., Deb, S.: Eagle strategy using levy walk and firefly algorithms for stochastic optimization. In: Nature Inspired Cooperative Strategies for Optimization (NICSO2010), pp. 101–111. Springer (2010)
Yang, X.S., Karamanoglu, M., He, X.: Multiobjective flower algorithm for optimization. Proc. Comput. Sci. 18, 861–868 (2013)
Chen, H., Zhu, Y., Hu, K., He, X.: Hierarchical swarm model: a new approach to optimization. Discr. Dyn. Nat. Soc. (2010)
Civicioglu, P.: Artificial cooperative search algorithm for numerical optimization problems. Inf. Sci. 229, 58–76 (2013)
de Paula Comellas Padro, F., Martınez Navarro, J., et al.: Bumblebees: a multiagent combinatorial optimization algorithm inspired by social insect behavior (2011)
Yang, X.S.: Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)
de Lima Neto, F.B., Lins, A.J.C.C., Nascimento, A.I.S., Lima, M.P., et al.: A novel search algorithm based on fish school behavior. In: IEEE International Conference on Systems, Man and Cybernetics, (SMC), pp. 2646–2651 (2008)
Eskandar, H., Sadollah, A., Bahreininejad, A., Hamdi, M.: Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. (2012)
Kaveh, A., Farhoudi, N.: A new optimization method: dolphin echolocation. Adv. Eng. Softw. 59, 53–70 (2013)
Tang, R., Fong, S., Yang, X.S., Deb, S.: Wolf search algorithm with ephemeral memory. In: Seventh International Conference on Digital Information Management (ICDIM), pp. 165–172 (2012)
Tamura, K., Yasuda, K.: Spiral dynamics inspired optimization. J. Adv. Comput. Intell. Intell. Inf. 15(8), 1116–1122 (2011)
Teodorovic, D., Dellorco, M.: Bee colony optimization—a cooperative learning approach to complex transportation problems. In: Advanced OR and AI Methods in Transportation, Proceedings of 16th Mini–EURO Conference and 10th Meeting of EWGT—Poznan: Publishing House of the Polish Operational and System Research, pp. 51–60 (2005)
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Lohi, S.A., Jaiswal, A.A., Gorewar, H.V., Lohi, S.A. (2020). Analytical Assessment of Nature-Inspired Metaheuristic Algorithms to Elucidate Assembly Line Task Scheduling Problem. In: Tuba, M., Akashe, S., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Advances in Intelligent Systems and Computing, vol 933. Springer, Singapore. https://doi.org/10.1007/978-981-13-7166-0_21
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