Abstract
Environment, resources and energy have garnered global attention in several countries of major societal concern. Manufacturers must be mindful of the environmental impact by monitoring their products throughout their life cycle in order to manage the pollution problem. Nowadays, the disassembly operation plays a fundamental role in component remanufacturing considering their importance in product recovery by recover value and conserving energy from end-of-life products. Reducing the energy consumption of disassembly sequences has been an important subject. This paper establishes a dual-objective disassembly sequencing problem that aims to maximize disassembly profit and minimize energy consumption. This approach is based on the adaptation of the Petri net (PNs) as modeling tool that allows representing all possible disassembly sequences using the extended process graph, the disassembly priority, and the incidence matrices. Then, the particle swarm optimization (PSO) algorithm is applied to determine the optimal disassembly sequence that ensure the least energy consumption and the maximum profit. To evaluate the efficient of the proposed approach, a case study of a radio set is proposed. Simulation results demonstrate the efficacy of the proposed methods to resolve this type of problem by determining the optimal or near optimal disassembly sequence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ben Jdidia, A., Hentati, T., Bellacicco, A., Khabou, M.T., Rivier, A., Haddar, M.: Optimizing cutting conditions in single pass face milling for minimum cutting energy, time, cost, and surface roughness. In: Chaari, F., Barkallah, M., Bouguecha, A., Zouari, B., Khabou, M.T., Kchaou, M., Haddar, M. (eds.) Advances in Materials, Mechanics and Manufacturing. LNME, pp. 214–222. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-24247-3_24
ElSayed, A., Kongar, E., Gupta, S.M.: An evolutionary algorithm for selective disassembly of end-of-life products. Int. J. Swarm Intell. Evol. Comput. 22(1), 1–7 (2012)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, 39–43 (1995)
Fu, Y., Zhou, M., Guo, X., Qi, L.: Stochastic disassembly sequence optimization for profit and energy consumption. IEEE Int. Conf. Syst. Man, Cybern. (SMC), Miyazaki, Japan, 1410–1415 (2018). https://doi.org/10.1109/SMC.2018.00246
Guo, X., Liu, S., Zhou, M., Tian, G.: Disassembly Sequence Optimization for Large-Scale products with multiresource constraints using scatter search and petri nets. IEEE Trans. Cybern. 46(11), 2435–2446 (2015)
Guo, X., Liu, S., Zhou, M., Tian, G.: Dual objective program and scatter search for the optimization of disassembly sequences subject to multiresource constraints. IEEE Trans. Autom. Sci. Eng. 1–13 (2018).https://doi.org/10.1109/TASE.2017.2731981
Gao, Y., et al.: An energy-saving optimization method of dynamic scheduling for disassembly line. Energies 11(5), 1261–1279 (2018)
Guo, X., Zhou, M., Liu, S., Qi, L.: Lexicographic multiobjective scatter search for the optimization of sequence-dependent selective disassembly subject to multiresource constraints. IEEE Trans. Cybern. 50(7), 3307–3317 (2020). https://doi.org/10.1109/TCYB.2019.2901834
Guo, X., Zhou, M., Liu, S., Qi, L.: Multiresource-constrained selective disassembly With maximal profit and minimal energy consumption. IEEE Trans. Autom. Sci. Eng. (2020). https://doi.org/10.1109/TASE.2020.2992220
Jeunet, J., Della, F., Fabio Salassa, C.F.: Heuristic Solution Methods for the Selective Disassembly Sequencing Problem under SequenceDependent Costs. IFAC-Papers Online 52(13), 1908–1913 (2019)
Luo, Y., Peng, Q., Gu, P.: Integrated multi-layer representation and ant colony search for product selective disassembly planning. Comput. Ind. 75, 13–26 (2016)
Lu, Q., Ren, Y., Jin, H., Meng, L., Li, L., Zhang, C., Sutherland, J.W.: A hybrid metaheuristic algorithm for a profit-oriented and energy efficient disassembly sequencing problem. Robot. Comput. Integr. Manufact. 61, 101828 (2020)
Pornsing, C., Watanasungsuit, A.: Discrete particle swarm optimization for disassembly sequence planning, IEEE International Conference on Management of Innovation and Technology., Singapore, 480–485 (2014)
Tang, Y., Zhou, M.C., Zussman, E., Caudill, R.J.: Disassembly modeling, planning and applications. J. Manuf. Syst. 21(2), 200–217 (2002)
Tseng, H.-E., Chang, C., Lee, S., Huang, Y.-M.: A Block-based genetic algorithm for disassembly sequence planning. Expert Syst. Appl. 96, 492–505 (2018)
Yeh, w.: Optimization of the disassembly sequencing problem on the basis of self-adaptive simplified swarm optimization. IEEE Trans. Syst. Man, Cybern. A, Syst. Hum. 42, 250–261 (2012)
Zhao, S., Li, Y., Fu, F., Yuan, W.: Fuzzy reasoning Petri nets and its application to disassembly sequence decision-making for the end-of life product recycling and remanufacturing. Int. J. Comput. Integr. Manuf. 27(5), 415–421 (2014)
Zhang, L., Liu, Z., Yang, M.: Disassembly sequence planning based on interpretative structural model. J. CAD Comput. Graph. 23(5), 667–675 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bouazza, S., Hassine, H., Barkallah, M., Amari, S., Haddar, M. (2022). Disassembly Sequence Optimization for Profit and Energy Consumption Using Petri Nets and Particle Swarm Optimization. In: Ben Amar, M., Bouguecha, A., Ghorbel, E., El Mahi, A., Chaari, F., Haddar, M. (eds) Advances in Materials, Mechanics and Manufacturing II. A3M 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-84958-0_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-84958-0_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84957-3
Online ISBN: 978-3-030-84958-0
eBook Packages: EngineeringEngineering (R0)