Abstract
Increased awareness of resource scarcity and man-made pollution has driven consumers and manufacturers to reflect ways how to deal with end-of-life products and exploit their remaining value. The options of repair, remanufacturing or recycling each require at least partial disassembly of the structure with the variety of feasible process plans and large number of emerging parts and sub-assemblies generally making for a challenging optimization problem. Its complexity is further accentuated by considering divergent process flows which result from multiple parts or sub-assemblies that are released in the course of disassembly. In a previous study, it was shown that exact solution using an and/or graph based mixed integer linear program (MILP) was only practical for smaller problem instances. Consequently, a meta-heuristic approach is now taken to enable solution of large size problems. This study presents a genetic algorithm (GA) along with a problem specific representation to address both the scheduling and process planning aspect while allowing for parallel execution of certain disassembly tasks. Performance analysis with artificial test data shows that the proposed GA is capable of producing good quality solutions in reasonable time and bridging the gap regarding application to large scale problems as compared to the existing MILP formulation.
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
References
Zhang, R., Ong, S.K, Nee, A.Y.: A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling. In: Applied Soft Computing, vol. 37, pp. 521–532 (2015)
Gong, G., Deng, Q., Chiong, R., Gong, X., Huang, H., Han, W.: Remanufacturing-oriented process planning and scheduling: mathematical modelling and evolutionary optimisation. Int. J. Prod. Res. 58(12), 3781–3799 (2020)
Özgüven, C., Özbakır, L., Yavuz, Y.: Mathematical models for job-shop scheduling problems with routing and process plan flexibility. Appl. Math. Model. 34(6), 1539–1548 (2010)
Amin-Naseri, M.R., Afshari, A.J.: A hybrid genetic algorithm for integrated process planning and scheduling problem with precedence constraints. Int. J. Adv. Manuf. Technol. 59(1–4), 273–287 (2012)
Gaudreault, J., Frayret, J.M., Rousseau, A., D’Amours, S.: Combined planning and scheduling in a divergent production system with co-production: a case study in the lumber industry. Comput. Oper. Res. 38(9), 1238–1250 (2011)
Ehm, F.: Machine scheduling for multi-product disassembly. In: Operations Research Proceedings 2016, pp. 507–513. Springer, Cham (2018)
Ehm, F.: A data-driven modeling approach for integrated disassembly planning and scheduling. J. Remanuf. 9(2), 89–107 (2019)
Acknowledgements
The author would like to thank Benedikt Zipfel who contributed to the design and implementation of the GA as part of his diploma thesis.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ehm, F. (2020). A Problem Specific Genetic Algorithm for Disassembly Planning and Scheduling Considering Process Plan Flexibility and Parallel Operations. In: Neufeld, J.S., Buscher, U., Lasch, R., Möst, D., Schönberger, J. (eds) Operations Research Proceedings 2019. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-48439-2_73
Download citation
DOI: https://doi.org/10.1007/978-3-030-48439-2_73
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-48438-5
Online ISBN: 978-3-030-48439-2
eBook Packages: Business and ManagementBusiness and Management (R0)