IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v173y2021ics0040162521005485.html
   My bibliography  Save this article

Multi-objective optimization of costs and energy efficiency associated with autonomous industrial processes for sustainable growth

Author

Listed:
  • Rubio, Francisco
  • Llopis-Albert, Carlos
  • Valero, Francisco

Abstract

Digital technologies are transforming the industrial landscape and disrupting traditional business models. New business opportunities related to Industry 4.0 are emerging, so companies must adapt to the new environment. This work puts forward a multi-objective optimization algorithm to improve productivity and reduce the costs and energy consumption of autonomous industrial processes with the aim of achieving sustainable growth. The processes analyzed encompass an assembly line production with robotic cells and the subsequent material handling systems (MHS) using autonomous guided vehicles (AGVs) for indoor transport. An efficient algorithm has been implemented to integrate and minimize industrial robot arm working times, AGVs travel times and their trajectory, and the energy consumed in industrial processes while maximizing global business profits when manufacturing different products in an indoor industrial environment. Furthermore, this is carried out by considering the kinematics and dynamics of autonomous industrial processes and sustainable strategies to ensure compliance with government policies on environmental issues. These objectives are in line with the European Union (EU) guidelines on reducing greenhouse gas (GHG) emissions, renewable energy share, and improvements in energy efficiency for climate change mitigation and adaptation policies. Based on the difference in energy consumption between optimized and unoptimized industrial processes, the economic benefits can be quantified in terms of GHG emission quotas, volume of fuel consumed, and the indirect benefits with respect to improving corporate brand image. The methodology presented here has been successfully applied to several real case studies covering different manufacturing processes, robotic operations, and products. The results show that higher profits and sustainable growth are achieved when this methodology is used. It helps design Flexible Manufacturing Systems (FMS) and leads to shorter working times and higher energy efficiency and annual profits. In addition, Pareto frontiers show the trade-off between profits and product manufacturing times for different case studies.

Suggested Citation

  • Rubio, Francisco & Llopis-Albert, Carlos & Valero, Francisco, 2021. "Multi-objective optimization of costs and energy efficiency associated with autonomous industrial processes for sustainable growth," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
  • Handle: RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521005485
    DOI: 10.1016/j.techfore.2021.121115
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162521005485
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2021.121115?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. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    2. Abolhassani, Amir & James Harner, E. & Jaridi, Majid, 2019. "Empirical analysis of productivity enhancement strategies in the North American automotive industry," International Journal of Production Economics, Elsevier, vol. 208(C), pages 140-159.
    3. James P. Kelly & Jiefeng Xu, 1999. "A Set-Partitioning-Based Heuristic for the Vehicle Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 11(2), pages 161-172, May.
    4. Xiaoxue Zheng & Haiyan Lin & Zhi Liu & Dengfeng Li & Carlos Llopis-Albert & Shouzhen Zeng, 2018. "Manufacturing Decisions and Government Subsidies for Electric Vehicles in China: A Maximal Social Welfare Perspective," Sustainability, MDPI, vol. 10(3), pages 1-28, March.
    5. Llopis-Albert, Carlos & Rubio, Francisco & Valero, Francisco, 2021. "Impact of digital transformation on the automotive industry," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    6. Masae, Makusee & Glock, Christoph H. & Grosse, Eric H., 2020. "Order picker routing in warehouses: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 224(C).
    7. Makusee Masae & Christoph H. Glock & Panupong Vichitkunakorn, 2020. "Optimal order picker routing in the chevron warehouse," IISE Transactions, Taylor & Francis Journals, vol. 52(6), pages 665-687, June.
    8. Llopis-Albert, Carlos & Palacios-Marqués, Daniel & Simón-Moya, Virginia, 2021. "Fuzzy set qualitative comparative analysis (fsQCA) applied to the adaptation of the automobile industry to meet the emission standards of climate change policies via the deployment of electric vehicle," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    9. Llopis-Albert, Carlos & Rubio, Francisco & Valero, Francisco, 2019. "Fuzzy-set qualitative comparative analysis applied to the design of a network flow of automated guided vehicles for improving business productivity," Journal of Business Research, Elsevier, vol. 101(C), pages 737-742.
    10. Llopis-Albert, Carlos & Rubio, Francisco & Valero, Francisco, 2015. "Improving productivity using a multi-objective optimization of robotic trajectory planning," Journal of Business Research, Elsevier, vol. 68(7), pages 1429-1431.
    11. Laporte, Gilbert, 1992. "The vehicle routing problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(3), pages 345-358, June.
    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. Saverio Ferraro & Alessandra Cantini & Leonardo Leoni & Filippo De Carlo, 2023. "Sustainable Logistics 4.0: A Study on Selecting the Best Technology for Internal Material Handling," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    2. Shahriyar Mukhtarov & Hasan Dinçer & Halim Baş & Serhat Yüksel, 2022. "Policy Recommendations for Handling Brain Drains to Provide Sustainability in Emerging Economies," Sustainability, MDPI, vol. 14(23), pages 1-24, December.
    3. Agnieszka A. Tubis & Honorata Poturaj, 2022. "Risk Related to AGV Systems—Open-Access Literature Review," Energies, MDPI, vol. 15(23), pages 1-23, November.
    4. Calabrese, Armando & Costa, Roberta & Tiburzi, Luigi & Brem, Alexander, 2023. "Merging two revolutions: A human-artificial intelligence method to study how sustainability and Industry 4.0 are intertwined," Technological Forecasting and Social Change, Elsevier, vol. 188(C).

    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. Bhoopalam, Anirudh Kishore & Agatz, Niels & Zuidwijk, Rob, 2018. "Planning of truck platoons: A literature review and directions for future research," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 212-228.
    2. Maria A. M. Trindade & Paulo S. A. Sousa & Maria R. A. Moreira, 2022. "Ramping up a heuristic procedure for storage location assignment problem with precedence constraints," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 646-669, September.
    3. Vis, Iris F.A., 2006. "Survey of research in the design and control of automated guided vehicle systems," European Journal of Operational Research, Elsevier, vol. 170(3), pages 677-709, May.
    4. Rubio, Francisco & Llopis-Albert, Carlos & Besa, Antonio José, 2023. "Optimal allocation of energy sources in hydrogen production for sustainable deployment of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    5. Bhusiri, Narath & Qureshi, Ali Gul & Taniguchi, Eiichi, 2014. "The trade-off between fixed vehicle costs and time-dependent arrival penalties in a routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 1-22.
    6. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    7. Imen Ben Mohamed & Walid Klibi & Olivier Labarthe & Jean-Christophe Deschamps & Mohamed Zied Babai, 2017. "Modelling and solution approaches for the interconnected city logistics," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2664-2684, May.
    8. Çelik, Melih & Archetti, Claudia & Süral, Haldun, 2022. "Inventory routing in a warehouse: The storage replenishment routing problem," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1117-1132.
    9. Shandong Mou, 2022. "Integrated Order Picking and Multi-Skilled Picker Scheduling in Omni-Channel Retail Stores," Mathematics, MDPI, vol. 10(9), pages 1-19, April.
    10. Anna Konovalenko & Lars Magnus Hvattum, 2024. "Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State-Space Components," Logistics, MDPI, vol. 8(4), pages 1-18, October.
    11. Li Zhou & Huwei Liu & Junhui Zhao & Fan Wang & Jianglong Yang, 2022. "Performance Analysis of Picking Routing Strategies in the Leaf Layout Warehouse," Mathematics, MDPI, vol. 10(17), pages 1-28, September.
    12. Llopis-Albert, Carlos & Palacios-Marqués, Daniel & Simón-Moya, Virginia, 2021. "Fuzzy set qualitative comparative analysis (fsQCA) applied to the adaptation of the automobile industry to meet the emission standards of climate change policies via the deployment of electric vehicle," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    13. Shengbin Wang & Weizhen Rao & Yuan Hong, 2020. "A distance matrix based algorithm for solving the traveling salesman problem," Operational Research, Springer, vol. 20(3), pages 1505-1542, September.
    14. Du, Jianhui & Zhang, Zhiqin & Wang, Xu & Lau, Hoong Chuin, 2023. "A hierarchical optimization approach for dynamic pickup and delivery problem with LIFO constraints," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    15. Vichitkunakorn, Panupong & Emde, Simon & Masae, Makusee & Glock, Christoph H. & Grosse, Eric H., 2024. "Locating charging stations and routing drones for efficient automated stocktaking," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1129-1145.
    16. Laura Korbacher & Katrin Heßler & Stefan Irnich, 2023. "The Single Picker Routing Problem with Scattered Storage: Modeling and Evaluation of Routing and Storage Policies," Working Papers 2302, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    17. Masae, Makusee & Glock, Christoph H. & Vichitkunakorn, Panupong, 2021. "A method for efficiently routing order pickers in the leaf warehouse," International Journal of Production Economics, Elsevier, vol. 234(C).
    18. Celikoglu, Hilmi Berk, 2013. "Reconstructing freeway travel times with a simplified network flow model alternating the adopted fundamental diagram," European Journal of Operational Research, Elsevier, vol. 228(2), pages 457-466.
    19. Neves-Moreira, Fábio & Amorim, Pedro, 2024. "Learning efficient in-store picking strategies to reduce customer encounters in omnichannel retail," International Journal of Production Economics, Elsevier, vol. 267(C).
    20. Guodong Yu & Yu Yang, 2019. "Dynamic routing with real-time traffic information," Operational Research, Springer, vol. 19(4), pages 1033-1058, December.

    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:eee:tefoso:v:173:y:2021:i:c:s0040162521005485. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.