IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i12p2865-d1412704.html
   My bibliography  Save this article

Optimizing Mixed-Model Synchronous Assembly Lines with Bipartite Sequence-Dependent Setup Times in Advanced Manufacturing

Author

Listed:
  • Asieh Varyani

    (Department of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, SW Monroe Avenue, Corvallis, OR 97331, USA)

  • Mohsen Salehi

    (Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan 6517838623, Iran)

  • Meysam Heydari Gharahcheshmeh

    (Department of Mechanical Engineering, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA)

Abstract

In advanced manufacturing, optimizing mixed-model synchronous assembly lines (MMALs) is crucial for enhancing productivity and adhering to sustainability principles, particularly in terms of energy consumption and energy-efficient sequencing. This paper introduces a novel approach by categorizing sequence-dependent setup times into bipartite categories: workpiece-independent and workpiece-dependent. This strategic division streamlines assembly processes, reduces idle times, and decreases energy consumption through more efficient machine usage. A new mathematical model is proposed to minimize the intervals at which workpieces are launched on an MMAL, aiming to reduce operational downtime that typically leads to excessive energy use. Given the Non-deterministic Polynomial-time hard (NP-hard) nature of this problem, a genetic algorithm (GA) is developed to efficiently find solutions, with performance compared against the traditional branch and bound technique (B&B). This method enhances the responsiveness of MMALs to variable production demands and contributes to energy conservation by optimizing the sequence of operations to align with energy-saving objectives. Computational experiments conducted on small and large-sized problems demonstrate that the proposed GA outperforms the conventional B&B method regarding solution quality, diversity level, and computational time, leading to energy reductions and enhanced cost-effectiveness in manufacturing settings.

Suggested Citation

  • Asieh Varyani & Mohsen Salehi & Meysam Heydari Gharahcheshmeh, 2024. "Optimizing Mixed-Model Synchronous Assembly Lines with Bipartite Sequence-Dependent Setup Times in Advanced Manufacturing," Energies, MDPI, vol. 17(12), pages 1-20, June.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2865-:d:1412704
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/12/2865/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/12/2865/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mosadegh, H. & Fatemi Ghomi, S.M.T. & Süer, G.A., 2020. "Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 282(2), pages 530-544.
    2. Candace Arai Yano & Ram Rachamadugu, 1991. "Sequencing to Minimize Work Overload in Assembly Lines with Product Options," Management Science, INFORMS, vol. 37(5), pages 572-586, May.
    3. McMullen, P.R. & Tarasewich, Peter, 2005. "A beam search heuristic method for mixed-model scheduling with setups," International Journal of Production Economics, Elsevier, vol. 96(2), pages 273-283, May.
    4. Emde, Simon & Polten, Lukas, 2019. "Sequencing assembly lines to facilitate synchronized just-in-time part supply," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 112075, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. repec:dau:papers:123456789/2861 is not listed on IDEAS
    6. Thiago Cantos Lopes & Adalberto Sato Michels & Nadia Brauner & Leandro Magatão, 2023. "Balancing-sequencing paced assembly lines: a multi-objective mixed-integer linear case study," International Journal of Production Research, Taylor & Francis Journals, vol. 61(17), pages 5901-5917, September.
    7. Wucheng Yang & Wenming Cheng, 2020. "Modelling and solving mixed-model two-sided assembly line balancing problem with sequence-dependent setup time," International Journal of Production Research, Taylor & Francis Journals, vol. 58(21), pages 6638-6659, November.
    8. Andreas Drexl & Alf Kimms, 2001. "Sequencing JIT Mixed-Model Assembly Lines Under Station-Load and Part-Usage Constraints," Management Science, INFORMS, vol. 47(3), pages 480-491, March.
    9. Karim Aroui & Gülgün Alpan & Yannick Frein, 2017. "Minimising work overload in mixed-model assembly lines with different types of operators: a case study from the truck industry," International Journal of Production Research, Taylor & Francis Journals, vol. 55(21), pages 6305-6326, November.
    10. H. Mosadegh & S.M.T. Fatemi Ghomi & G.A. Süer, 2017. "Heuristic approaches for mixed-model sequencing problem with stochastic processing times," International Journal of Production Research, Taylor & Francis Journals, vol. 55(10), pages 2857-2880, May.
    11. Simon Emde & Lukas Polten, 2019. "Sequencing assembly lines to facilitate synchronized just-in-time part supply," Journal of Scheduling, Springer, vol. 22(6), pages 607-621, December.
    12. Mansouri, S. Afshin, 2005. "A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines," European Journal of Operational Research, Elsevier, vol. 167(3), pages 696-716, December.
    13. Meysam Heydari Gharahcheshmeh & Karen K. Gleason, 2022. "Recent Progress in Conjugated Conducting and Semiconducting Polymers for Energy Devices," Energies, MDPI, vol. 15(10), pages 1-28, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boysen, Nils & Fliedner, Malte & Scholl, Armin, 2009. "Sequencing mixed-model assembly lines: Survey, classification and model critique," European Journal of Operational Research, Elsevier, vol. 192(2), pages 349-373, January.
    2. Janis Brammer & Bernhard Lutz & Dirk Neumann, 2022. "Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 29-56, March.
    3. Giard, Vincent & Jeunet, Jully, 2010. "Optimal sequencing of mixed models with sequence-dependent setups and utility workers on an assembly line," International Journal of Production Economics, Elsevier, vol. 123(2), pages 290-300, February.
    4. Boysen, Nils & Scholl, Armin & Wopperer, Nico, 2012. "Resequencing of mixed-model assembly lines: Survey and research agenda," European Journal of Operational Research, Elsevier, vol. 216(3), pages 594-604.
    5. Ding, Fong-Yuen & Zhu, Jin & Sun, Hui, 2006. "Comparing two weighted approaches for sequencing mixed-model assembly lines with multiple objectives," International Journal of Production Economics, Elsevier, vol. 102(1), pages 108-131, July.
    6. H. Mosadegh & S.M.T. Fatemi Ghomi & G.A. Süer, 2017. "Heuristic approaches for mixed-model sequencing problem with stochastic processing times," International Journal of Production Research, Taylor & Francis Journals, vol. 55(10), pages 2857-2880, May.
    7. Yavuz, Mesut & Tufekci, Suleyman, 2006. "A bounded dynamic programming solution to the batching problem in mixed-model just-in-time manufacturing systems," International Journal of Production Economics, Elsevier, vol. 103(2), pages 841-862, October.
    8. Bautista, Joaquín & Alfaro, Rocío & Batalla, Cristina, 2015. "Modeling and solving the mixed-model sequencing problem to improve productivity," International Journal of Production Economics, Elsevier, vol. 161(C), pages 83-95.
    9. Heike, G. & Ramulu, M. & Sorenson, E. & Shanahan, P. & Moinzadeh, K., 2001. "Mixed model assembly alternatives for low-volume manufacturing: The case of the aerospace industry," International Journal of Production Economics, Elsevier, vol. 72(2), pages 103-120, July.
    10. Golle, Uli & Rothlauf, Franz & Boysen, Nils, 2014. "Car sequencing versus mixed-model sequencing: A computational study," European Journal of Operational Research, Elsevier, vol. 237(1), pages 50-61.
    11. Boysen, Nils & Fliedner, Malte, 2007. "Comments on "Solving real car sequencing problems with ant colony optimization"," European Journal of Operational Research, Elsevier, vol. 182(1), pages 466-468, October.
    12. Baoxi Wang & Zailin Guan & Saif Ullah & Xianhao Xu & Zongdong He, 2017. "Simultaneous order scheduling and mixed-model sequencing in assemble-to-order production environment: a multi-objective hybrid artificial bee colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 419-436, February.
    13. Sadeghi, Parisa & Rebelo, Rui Diogo & Ferreira, José Soeiro, 2021. "Using variable neighbourhood descent and genetic algorithms for sequencing mixed-model assembly systems in the footwear industry," Operations Research Perspectives, Elsevier, vol. 8(C).
    14. Chatterjee A K & Mukherjee, Saral, 2006. "Unified Concept of Bottleneck," IIMA Working Papers WP2006-05-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    15. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Jula, Payman & Pirayesh, Amir & Ahmadi, Hadi, 2020. "A learning-based metaheuristic for a multi-objective agile inspection planning model under uncertainty," European Journal of Operational Research, Elsevier, vol. 285(2), pages 513-537.
    16. Li, Zixiang & Kucukkoc, Ibrahim & Zhang, Zikai, 2020. "Branch, bound and remember algorithm for two-sided assembly line balancing problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 896-905.
    17. Hassan Zohali & Bahman Naderi & Vahid Roshanaei, 2022. "Solving the Type-2 Assembly Line Balancing with Setups Using Logic-Based Benders Decomposition," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 315-332, January.
    18. Sabuncuoglu, Ihsan & Gocgun, Yasin & Erel, Erdal, 2008. "Backtracking and exchange of information: Methods to enhance a beam search algorithm for assembly line scheduling," European Journal of Operational Research, Elsevier, vol. 186(3), pages 915-930, May.
    19. Drexl, Andreas & Jordan, Carsten, 1994. "Materialflußorientierte Produktionssteuerung bei Variantenfließfertigung," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 362, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    20. Ioanna Makarouni & John Psarras & Eleftherios Siskos, 2015. "Interactive bicriterion decision support for a large scale industrial scheduling system," Annals of Operations Research, Springer, vol. 227(1), pages 45-61, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2865-:d:1412704. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.