IDEAS home Printed from https://ideas.repec.org/p/ems/eureri/137111.html
   My bibliography  Save this paper

A Data-driven Approach to Enhance Worker Productivity by Optimizing Facility Layout

Author

Listed:
  • Ghorashi Khalilabadi, S. M.
  • Roy, D.
  • de Koster, M.B.M.

Abstract

The facility layout problem (FLP) is the problem of determining non-overlapping positions of departments on the shop floor to minimize material handling costs. Traditional methods for solving FLPs consider pairwise (from-to) flows to optimize layouts. This paper shows that these traditional methods underestimate the total travel distance of a layout, when departments have more than a single input/output point and some flows consist of visits to more than two de- partments. To accurately calculate the traveled distances, the actual routes of the workers and transporters (so-called connected movements) in the system need to be determined. The con- nected movements of the workers in a facility can now be captured using the Internet of Things network and stored in the cloud server for analysis. We propose a mixed-integer non-linear programming model for the FLP that minimizes the total travel distance using these connected movements as the input data. Because of the complexity of the problem, a biased random key genetic algorithm is used to find the layout. To ensure the validity of the method, a case study is carried out at a fertilizer production company that implemented an Internet of Things network to capture worker movement data to minimize worker productivity loss via an improved layout. By using these connected movements, the best layout for the case company is found. The results of the proposed data-driven optimization method indicate that leveraging connected movements can reduce the total travel distance by 10.6% compared to the best possible layout generated by the traditional pairwise method in the case study.

Suggested Citation

  • Ghorashi Khalilabadi, S. M. & Roy, D. & de Koster, M.B.M., 2022. "A Data-driven Approach to Enhance Worker Productivity by Optimizing Facility Layout," ERIM Report Series Research in Management ERS-2022-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:137111
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/137111/ERS-2022-003-LIS.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kelachankuttu, Hari & Batta, Rajan & Nagi, Rakesh, 2007. "Contour line construction for a new rectangular facility in an existing layout with rectangular departments," European Journal of Operational Research, Elsevier, vol. 180(1), pages 149-162, July.
    2. Gonçalves, José Fernando & Resende, Mauricio G.C., 2015. "A biased random-key genetic algorithm for the unequal area facility layout problem," European Journal of Operational Research, Elsevier, vol. 246(1), pages 86-107.
    3. J-G Kim & Y-D Kim, 1999. "A branch and bound algorithm for locating input and output points of departments on the block layout," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(5), pages 517-525, May.
    4. Yavuz A. Bozer & Russell D. Meller & Steven J. Erlebacher, 1994. "An Improvement-Type Layout Algorithm for Single and Multiple-Floor Facilities," Management Science, INFORMS, vol. 40(7), pages 918-932, July.
    5. Anjos, Miguel F. & Vieira, Manuel V.C., 2017. "Mathematical optimization approaches for facility layout problems: The state-of-the-art and future research directions," European Journal of Operational Research, Elsevier, vol. 261(1), pages 1-16.
    6. Yang, Taho & Peters, Brett A. & Tu, Mingan, 2005. "Layout design for flexible manufacturing systems considering single-loop directional flow patterns," European Journal of Operational Research, Elsevier, vol. 164(2), pages 440-455, July.
    7. Thiago Noronha & Mauricio Resende & Celso Ribeiro, 2011. "A biased random-key genetic algorithm for routing and wavelength assignment," Journal of Global Optimization, Springer, vol. 50(3), pages 503-518, July.
    8. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
    9. Yeming Gong & René de Koster & Hans J.B.G. Frenk & Adriana F. Gabor, 2013. "Increasing the Revenue of Self-Storage Warehouses by Facility Design," Post-Print hal-02313025, HAL.
    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. Gonçalves, José Fernando & Wäscher, Gerhard, 2020. "A MIP model and a biased random-key genetic algorithm based approach for a two-dimensional cutting problem with defects," European Journal of Operational Research, Elsevier, vol. 286(3), pages 867-882.
    2. Fowler, John W. & Mönch, Lars, 2022. "A survey of scheduling with parallel batch (p-batch) processing," European Journal of Operational Research, Elsevier, vol. 298(1), pages 1-24.
    3. Andrade, Carlos E. & Toso, Rodrigo F. & Gonçalves, José F. & Resende, Mauricio G.C., 2021. "The Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path-Relinking and its real-world applications," European Journal of Operational Research, Elsevier, vol. 289(1), pages 17-30.
    4. F. Stefanello & L. S. Buriol & M. J. Hirsch & P. M. Pardalos & T. Querido & M. G. C. Resende & M. Ritt, 2017. "On the minimization of traffic congestion in road networks with tolls," Annals of Operations Research, Springer, vol. 249(1), pages 119-139, February.
    5. Soares, Leonardo Cabral R. & Carvalho, Marco Antonio M., 2020. "Biased random-key genetic algorithm for scheduling identical parallel machines with tooling constraints," European Journal of Operational Research, Elsevier, vol. 285(3), pages 955-964.
    6. Wang, Haibo & Alidaee, Bahram, 2019. "The multi-floor cross-dock door assignment problem: Rising challenges for the new trend in logistics industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 132(C), pages 30-47.
    7. Fernando Stefanello & Vaneet Aggarwal & Luciana S. Buriol & Mauricio G. C. Resende, 2019. "Hybrid algorithms for placement of virtual machines across geo-separated data centers," Journal of Combinatorial Optimization, Springer, vol. 38(3), pages 748-793, October.
    8. Geiza Silva & André Leite & Raydonal Ospina & Víctor Leiva & Jorge Figueroa-Zúñiga & Cecilia Castro, 2023. "Biased Random-Key Genetic Algorithm with Local Search Applied to the Maximum Diversity Problem," Mathematics, MDPI, vol. 11(14), pages 1-11, July.
    9. Pinto, Bruno Q. & Ribeiro, Celso C. & Rosseti, Isabel & Plastino, Alexandre, 2018. "A biased random-key genetic algorithm for the maximum quasi-clique problem," European Journal of Operational Research, Elsevier, vol. 271(3), pages 849-865.
    10. Edson Ticona-Zegarra & Rafael CS Schouery & Leandro A Villas & Flávio K Miyazawa, 2018. "Improved continuous enhancement routing solution for energy-aware data aggregation in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 14(5), pages 15501477187, May.
    11. Li, Xueping & Zhang, Kaike, 2018. "Single batch processing machine scheduling with two-dimensional bin packing constraints," International Journal of Production Economics, Elsevier, vol. 196(C), pages 113-121.
    12. Bruno Q. Pinto & Celso C. Ribeiro & Isabel Rosseti & Thiago F. Noronha, 2020. "A biased random-key genetic algorithm for routing and wavelength assignment under a sliding scheduled traffic model," Journal of Global Optimization, Springer, vol. 77(4), pages 949-973, August.
    13. Caio César Freitas & Dario José Aloise & Fábio Francisco Costa Fontes & Andréa Cynthia Santos & Matheus Silva Menezes, 2023. "A biased random-key genetic algorithm for the two-level hub location routing problem with directed tours," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(3), pages 903-924, September.
    14. Paola Festa & Panos Pardalos, 2012. "Efficient solutions for the far from most string problem," Annals of Operations Research, Springer, vol. 196(1), pages 663-682, July.
    15. Ayşegül Altın & Bernard Fortz & Mikkel Thorup & Hakan Ümit, 2013. "Intra-domain traffic engineering with shortest path routing protocols," Annals of Operations Research, Springer, vol. 204(1), pages 65-95, April.
    16. Schirmer, Andreas & Riesenberg, Sven, 1997. "Parameterized heuristics for project scheduling: Biased random sampling methods," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 456, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    17. Mariem Besbes & Marc Zolghadri & Roberta Costa Affonso & Faouzi Masmoudi & Mohamed Haddar, 2020. "A methodology for solving facility layout problem considering barriers: genetic algorithm coupled with A* search," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 615-640, March.
    18. Qingzheng Xu & Na Wang & Lei Wang & Wei Li & Qian Sun, 2021. "Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review," Mathematics, MDPI, vol. 9(8), pages 1-44, April.
    19. Zhongwei Zhang & Lihui Wu & Zhaoyun Wu & Wenqiang Zhang & Shun Jia & Tao Peng, 2022. "Energy-Saving Oriented Manufacturing Workshop Facility Layout: A Solution Approach Using Multi-Objective Particle Swarm Optimization," Sustainability, MDPI, vol. 14(5), pages 1-28, February.
    20. Xiao, Lei & Zhang, Xinghui & Tang, Junxuan & Zhou, Yaqin, 2020. "Joint optimization of opportunistic maintenance and production scheduling considering batch production mode and varying operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 202(C).

    More about this item

    Keywords

    Facility layout problem; Data-driven optimization; Connected movements; Sequence; Internet of Things; IoT;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ems:eureri:137111. 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: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/erimanl.html .

    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.