Abstract
The topic of production planning not only includes capacity planning or production sequence planning, but also topics that overlap with product engineering such as assembly sequence planning. In this paper, we implement assembly sequence optimization for one of the products in a multiproduct flow shop system, specifically by searching for an assembly sequence that involves a minimum number of reorientations and tool changes. As assembly sequence optimization is an NP-Hard combinatorial problem, we use the Genetic Algorithm metaheuristic technique to find the best solution. We also combine the Taguchi Method to find the best meta-parameters for the Genetic Algorithm, such that the resulting solution is better than a solution with arbitrary meta-parameters assigned. The assembly sequence generated reduced the reorientations and equipment changes time by 14.4% (5.74 min from 6.74 min) and reducing the total assembly time by 3.3% (29 min from 30 min).
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
Rashid MFF, Hutabarat W, Tiwari A (2011) A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches. Int J Adv Manuf Technol 59:335–349
Mishra A, Deb S (2016) An intelligent methodology for assembly tools selection and assembly sequence optimization. In: CAD/CAM, robotics and factories of the future. Springer, India, pp 323–333
Kucukkoc I, Karaoglan AD, Yaman R (2013) Using response surface design to determine the optimal parameters of genetic algorithm and a case study. Int J Prod Res 51(17):5039–5054
Alharbi F, Wang Q (2018) Solving an assembly sequence optimisation problem using the genetic algorithm. In: Proceeding of 2018 international conference on electronics, control, optimization and computer science (ICECOCS), pp 1–5
Chen WC, Hsu YY, Hsieh LF, Tai PH (2010) A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN. Expert Syst Appl 37(1):716–726
Haupt RL, Haupt SE (2004) Practical genetic algoritms, 2nd edn. Wiley, New Jersey
Tseng YJ, Kao HT, Huang FY (2010) Integrated assembly and disassembly sequence planning using a GA approach. Int J Prod Res 48(20):5991–6013
Choi YK, Lee DM, Cho YB (2009) An approach to multi-criteria assembly sequence planning using genetic algorithms. Int J Adv Manuf 42:180–188
Marian RM, Luong LH, Abhary K (2006) A genetic algorithm for the optimisation of assembly sequences. Comput Industr Eng 50(4):503–527
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
William, Hasby, F.M., Aribowo, W., Sukoyo (2023). Assembly Sequence Optimization to Minimize Reorientation and Tool Changes Using Genetic Algorithm. In: Rosyidi, C.N., Laksono, P.W., Jauhari, W.A., Hisjam, M. (eds) Proceedings of the 6th Asia Pacific Conference on Manufacturing Systems and 4th International Manufacturing Engineering Conference. iMEC-APCOMS 2022. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-1245-2_5
Download citation
DOI: https://doi.org/10.1007/978-981-99-1245-2_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1244-5
Online ISBN: 978-981-99-1245-2
eBook Packages: EngineeringEngineering (R0)