IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v270y2018i1d10.1007_s10479-016-2237-x.html
   My bibliography  Save this article

Integrating big data analytic and hybrid firefly-chaotic simulated annealing approach for facility layout problem

Author

Listed:
  • Akash Tayal

    (Indira Gandhi Delhi Technical University for Women)

  • Surya Prakash Singh

    (Indian Institute of Technology)

Abstract

Manufacturing industries have become larger, diverse and the factors affecting a facility layout design have grown rapidly. Handling and evaluating these large set of criteria (factors) is difficult in designing and solving facility layout problems. These factors and uncertainties have a large impact on manufacturing time, manufacturing cost, product quality and delivery performance. In order to operate efficiently, these facilities should adapt to these variations over multiple time periods and this must be addressed while designing an optimal layout. This paper proposes a novel integrated framework by combining Big Data Analtics and Hybrid meta-heuristic approach to design an optimal facility layout under stochastic demand over multiple periods. Firstly, factors affecting a facility layout design are identified. The survey is conducted to generate data reflecting 3V’s of Big Data. Secondly, a reduced set of factors are obtained using Big Data Analytics. These reduced set of factors are considered to mathematically model a weighted aggregate objective for Multi-objective Stochastic Dynamic Facility Layout Problem (MO-SDFLP). Hybrid Meta-heuristic based on Firefly (FA) and Chaotic simulated annealing is used to solve the MO-SDFLP. To show the working methodology of proposed integrated framework an exemplary case is presented.

Suggested Citation

  • Akash Tayal & Surya Prakash Singh, 2018. "Integrating big data analytic and hybrid firefly-chaotic simulated annealing approach for facility layout problem," Annals of Operations Research, Springer, vol. 270(1), pages 489-514, November.
  • Handle: RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2237-x
    DOI: 10.1007/s10479-016-2237-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-016-2237-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-016-2237-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Sun, Edward W. & Chen, Yi-Ting & Yu, Min-Teh, 2015. "Generalized optimal wavelet decomposing algorithm for big financial data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 194-214.
    3. Kouvelis, Panagiotis & Kiran, Ali S., 1991. "Single and multiple period layout models for automated manufacturing systems," European Journal of Operational Research, Elsevier, vol. 52(3), pages 300-314, June.
    4. Kusiak, Andrew & Heragu, Sunderesh S., 1987. "The facility layout problem," European Journal of Operational Research, Elsevier, vol. 29(3), pages 229-251, June.
    5. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    6. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    7. Yang, Taho & Peters, Brett A., 1998. "Flexible machine layout design for dynamic and uncertain production environments," European Journal of Operational Research, Elsevier, vol. 108(1), pages 49-64, July.
    8. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    9. Palekar, Udatta S. & Batta, Rajan & Bosch, Robert M. & Elhence, Sharad, 1992. "Modeling uncertainties in plant layout problems," European Journal of Operational Research, Elsevier, vol. 63(2), pages 347-359, December.
    10. Jones, D. F. & Mirrazavi, S. K. & Tamiz, M., 2002. "Multi-objective meta-heuristics: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 137(1), pages 1-9, February.
    11. Krishna K. Krishnan & S. Hossein Cheraghi & Chandan N. Nayak, 2008. "Facility layout design for multiple production scenarios in a dynamic environment," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 3(2), pages 105-133.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Govindan, Kannan & Gholizadeh, Hadi, 2021. "Robust network design for sustainable-resilient reverse logistics network using big data: A case study of end-of-life vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    2. Omar. A. Alghamdi & Gomaa Agag, 2023. "Boosting Innovation Performance through Big Data Analytics Powered by Artificial Intelligence Use: An Empirical Exploration of the Role of Strategic Agility and Market Turbulence," Sustainability, MDPI, vol. 15(19), pages 1-19, September.
    3. Maya Vachkova & Arsalan Ghouri & Haidy Ashour & Normalisa Binti Md Isa & Gregory Barnes, 2023. "Big data and predictive analytics and Malaysian micro-, small and medium businesses," SN Business & Economics, Springer, vol. 3(8), pages 1-28, August.
    4. Khalid Mekamcha & Mehdi Souier & Hakim Nadhir Bessenouci & Mohammed Bennekrouf, 2021. "Two metaheuristics approaches for solving the traveling salesman problem: an Algerian waste collection case," Operational Research, Springer, vol. 21(3), pages 1641-1661, September.

    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. Saif Benjaafar & Sunderesh S. Heragu & Shahrukh A. Irani, 2002. "Next Generation Factory Layouts: Research Challenges and Recent Progress," Interfaces, INFORMS, vol. 32(6), pages 58-76, December.
    2. Akash Tayal & Surya Prakash Singh, 2017. "Designing Flexible Stochastic Dynamic Layout: An Integrated Firefly and Chaotic Simulated Annealing-Based Approach," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(2), pages 89-98, June.
    3. Bin Shen & Hau-Ling Chan, 2017. "Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-26, February.
    4. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    5. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Roubaud, David & Fosso Wamba, Samuel & Giannakis, Mihalis & Foropon, Cyril, 2019. "Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain," International Journal of Production Economics, Elsevier, vol. 210(C), pages 120-136.
    6. M. M. Malik & S. Abdallah & M. Ala’raj, 2018. "Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review," Annals of Operations Research, Springer, vol. 270(1), pages 287-312, November.
    7. S. Vijayakumar Bharathi, 2017. "Prioritizing and Ranking the Big Data Information Security Risk Spectrum," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 183-201, September.
    8. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    9. Balakrishnan, Jaydeep & Cheng, Chun Hung, 2007. "Multi-period planning and uncertainty issues in cellular manufacturing: A review and future directions," European Journal of Operational Research, Elsevier, vol. 177(1), pages 281-309, February.
    10. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    11. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    12. Ionica Oncioiu & Ovidiu Constantin Bunget & Mirela Cătălina Türkeș & Sorinel Căpușneanu & Dan Ioan Topor & Attila Szora Tamaș & Ileana-Sorina Rakoș & Mihaela Ștefan Hint, 2019. "The Impact of Big Data Analytics on Company Performance in Supply Chain Management," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    13. Samuel Fosso Wamba & Angappa Gunasekaran & Rameshwar Dubey & Eric W. T. Ngai, 2018. "Big data analytics in operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 1-4, November.
    14. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    15. Pan Liu & Shu-ping Yi, 2018. "A study on supply chain investment decision-making and coordination in the Big Data environment," Annals of Operations Research, Springer, vol. 270(1), pages 235-253, November.
    16. Hazen, Benjamin T. & Weigel, Fred K. & Ezell, Jeremy D. & Boehmke, Bradley C. & Bradley, Randy V., 2017. "Toward understanding outcomes associated with data quality improvement," International Journal of Production Economics, Elsevier, vol. 193(C), pages 737-747.
    17. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    18. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    19. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.
    20. Gupta, Shivam & Chen, Haozhe & Hazen, Benjamin T. & Kaur, Sarabjot & Santibañez Gonzalez, Ernesto D.R., 2019. "Circular economy and big data analytics: A stakeholder perspective," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 466-474.

    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:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2237-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.