IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p16194-d993350.html
   My bibliography  Save this article

Dynamic Allocation of Manufacturing Resources in IoT Job Shop Considering Machine State Transfer and Carbon Emission

Author

Listed:
  • Xuan Su

    (School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China)

  • Wenquan Dong

    (Department of Industrial and Systems Engineering, The University of Tennessee, Knoxville, TN 37996, USA)

  • Jingyu Lu

    (School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China)

  • Chen Chen

    (School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China)

  • Weixi Ji

    (School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China
    Key Laboratory of Advanced Manufacturing Equipment Technology, Jiangnan University, Wuxi 214122, China)

Abstract

The optimal allocation of manufacturing resources plays an essential role in the production process. However, most of the existing resource allocation methods are designed for standard cases, lacking a dynamic optimal allocation framework for resources that can guide actual production. Therefore, this paper proposes a dynamic allocation method for discrete job shop resources in the Internet of Things (IoT), which considers the uncertainty of machine states, and carbon emission. First, a data-driven job shop resource status monitoring framework under the IoT environment is proposed, considering the real-time status of job shop manufacturing resources. A dynamic configuration mechanism of manufacturing resources based on the configuration threshold is proposed. Then, a real-time state-driven multi-objective manufacturing resource optimization allocation model is established, taking machine tool energy consumption and tool wear as carbon emission sources and combined with the maximum completion time. An improved imperialist competitive algorithm (I-ICA) is proposed to solve the model. Finally, taking an actual production process of a discrete job shop as an example, the proposed algorithm is compared with other low-carbon multi-objective optimization algorithms, and the results show that the proposed method is superior to similar methods in terms of completion time and carbon emissions. In addition, the practicability and effectiveness of the proposed dynamic resource allocation method are verified in a machine failure situation.

Suggested Citation

  • Xuan Su & Wenquan Dong & Jingyu Lu & Chen Chen & Weixi Ji, 2022. "Dynamic Allocation of Manufacturing Resources in IoT Job Shop Considering Machine State Transfer and Carbon Emission," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16194-:d:993350
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/16194/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/23/16194/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chaoyang Zhang & Zhengxu Wang & Kai Ding & Felix T.S. Chan & Weixi Ji, 2020. "An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7059-7077, December.
    2. Maroua Nouiri & Abdelghani Bekrar & Damien Trentesaux, 2020. "An energy-efficient scheduling and rescheduling method for production and logistics systems†," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3263-3283, June.
    3. Rajdeep Singh & Neeraj Bhanot, 2020. "An integrated DEMATEL-MMDE-ISM based approach for analysing the barriers of IoT implementation in the manufacturing industry," International Journal of Production Research, Taylor & Francis Journals, vol. 58(8), pages 2454-2476, April.
    4. Kaiqi Sun & Huangqing Xiao & Shengyuan Liu & Shutang You & Fan Yang & Yuqing Dong & Weikang Wang & Yilu Liu, 2020. "A Review of Clean Electricity Policies—From Countries to Utilities," Sustainability, MDPI, vol. 12(19), pages 1-22, September.
    5. Chaoyang Zhang & Pingyu Jiang, 2019. "Sustainability Evaluation of Process Planning for Single CNC Machine Tool under the Consideration of Energy-Efficient Control Strategies Using Random Forests," Sustainability, MDPI, vol. 11(11), pages 1-18, 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. Mehmet Ali Soytaş & Damla Durak Uşar & Meltem Denizel, 2022. "Estimation of the static corporate sustainability interactions," International Journal of Production Research, Taylor & Francis Journals, vol. 60(4), pages 1245-1264, February.
    2. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    3. Hamzeh Soltanali & Mehdi Khojastehpour & Siamak Kheybari, 2023. "Evaluating the critical success factors for maintenance management in agro-industries using multi-criteria decision-making techniques," Operations Management Research, Springer, vol. 16(2), pages 949-968, June.
    4. Zakaria Chekoubi & Wajdi Trabelsi & Nathalie Sauer & Ilias Majdouline, 2022. "The Integrated Production-Inventory-Routing Problem with Reverse Logistics and Remanufacturing: A Two-Phase Decomposition Heuristic," Sustainability, MDPI, vol. 14(20), pages 1-30, October.
    5. Asadi, Shahla & Nilashi, Mehrbakhsh & Iranmanesh, Mohammad & Hyun, Sunghyup Sean & Rezvani, Azadeh, 2022. "Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach," Technovation, Elsevier, vol. 118(C).
    6. Amjad Hussain & Muhammad Umar Farooq & Muhammad Salman Habib & Tariq Masood & Catalin I. Pruncu, 2021. "COVID-19 Challenges: Can Industry 4.0 Technologies Help with Business Continuity?," Sustainability, MDPI, vol. 13(21), pages 1-25, October.
    7. Rami Naimi & Maroua Nouiri & Olivier Cardin, 2021. "A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives," Sustainability, MDPI, vol. 13(23), pages 1-36, November.
    8. Shun Jia & Shang Wang & Jingxiang Lv & Wei Cai & Na Zhang & Zhongwei Zhang & Shuowei Bai, 2021. "Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption," Sustainability, MDPI, vol. 13(24), pages 1-23, December.
    9. Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.
    10. Fuli Zhou & Yandong He & Felix T. S. Chan & Panpan Ma & Francesco Schiavone, 2022. "Joint Distribution Promotion by Interactive Factor Analysis using an Interpretive Structural Modeling Approach," SAGE Open, , vol. 12(1), pages 21582440221, February.
    11. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    12. Anwer Mustafa Hilal & Aisha Hassan Abdalla Hashim & Marwa Obayya & Abdulbaset Gaddah & Abdullah Mohamed & Ishfaq Yaseen & Mohammed Rizwanullah & Abu Sarwar Zamani, 2022. "Metaheuristics Based Energy Efficient Task Scheduling Scheme for Cyber-Physical Systems Environment," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    13. Nguyen, Tiep & Duong, Quang Huy & Nguyen, Truong Van & Zhu, You & Zhou, Li, 2022. "Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 244(C).
    14. Monica Shukla & Ravi Shankar, 2022. "Modeling of critical success factors for adoption of smart manufacturing system in Indian SMEs: an integrated approach," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1271-1303, December.
    15. Jayakrishna Kandasamy & Yatin P. Kinare & Miheer T. Pawar & Abhijit Majumdar & Vimal K.E.K. & Rohit Agrawal, 2022. "Circular economy adoption challenges in medical waste management for sustainable development: An empirical study," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 958-975, October.
    16. Eitan, Avri, 2023. "How are public utilities responding to electricity market restructuring and the energy transition? Lessons from Israel," Utilities Policy, Elsevier, vol. 82(C).
    17. Snigdha Malhotra & Vernika Agarwal & P. K. Kapur, 2022. "Hierarchical framework for analysing the challenges of implementing industrial Internet of Things in manufacturing industries using ISM approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2356-2370, October.
    18. Sarthak Sahu & Saket Shanker & Aditya Kamat & Akhilesh Barve, 2023. "India’s public transportation system: the repercussions of COVID-19," Public Transport, Springer, vol. 15(2), pages 435-478, June.
    19. Marcin Bukowski & Janusz Majewski & Agnieszka Sobolewska, 2023. "The Environmental Impact of Changes in the Structure of Electricity Sources in Europe," Energies, MDPI, vol. 16(1), pages 1-22, January.
    20. Hail Jung & Jinsu Jeon & Dahui Choi & Jung-Ywn Park, 2021. "Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry," Sustainability, MDPI, vol. 13(8), pages 1-16, 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:jsusta:v:14:y:2022:i:23:p:16194-:d:993350. 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.