IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i18p2250-d634918.html
   My bibliography  Save this article

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Author

Listed:
  • Mei Li

    (Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China)

  • Gai-Ge Wang

    (Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
    Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning 530006, China)

  • Helong Yu

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

Abstract

In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

Suggested Citation

  • Mei Li & Gai-Ge Wang & Helong Yu, 2021. "Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem," Mathematics, MDPI, vol. 9(18), pages 1-30, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2250-:d:634918
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/18/2250/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/18/2250/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guiliang Gong & Raymond Chiong & Qianwang Deng & Xuran Gong, 2020. "A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility," International Journal of Production Research, Taylor & Francis Journals, vol. 58(14), pages 4406-4420, July.
    2. Fang Wang & Yunqing Rao & Chaoyong Zhang & Qiuhua Tang & Liping Zhang, 2016. "Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes," Sustainability, MDPI, vol. 8(8), pages 1-20, August.
    3. Lin, Yi-Kuei & Huang, Ding-Hsiang, 2020. "Reliability analysis for a hybrid flow shop with due date consideration," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    4. Peng Lin & Leidi Shen & Zhiheng Zhao & George Q. Huang, 2019. "Graduation manufacturing system: synchronization with IoT-enabled smart tickets," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2885-2900, December.
    5. Hua Xuan & Huixian Zhang & Bing Li, 2019. "An Improved Discrete Artificial Bee Colony Algorithm for Flexible Flowshop Scheduling with Step Deteriorating Jobs and Sequence-Dependent Setup Times," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-13, December.
    6. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
    7. Jun-qing Li & Shun-Chang Bai & Pei-yong Duan & Hong-yan Sang & Yu-yan Han & Zhi-xin Zheng, 2019. "An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 6922-6942, November.
    8. Qin, Tianbao & Du, Yuquan & Chen, Jiang Hang & Sha, Mei, 2020. "Combining mixed integer programming and constraint programming to solve the integrated scheduling problem of container handling operations of a single vessel," European Journal of Operational Research, Elsevier, vol. 285(3), pages 884-901.
    9. Melissa Shahgholi Zadeh & Yalda Katebi & Ali Doniavi, 2019. "A heuristic model for dynamic flexible job shop scheduling problem considering variable processing times," International Journal of Production Research, Taylor & Francis Journals, vol. 57(10), pages 3020-3035, May.
    10. Chang, Da-Yong, 1996. "Applications of the extent analysis method on fuzzy AHP," European Journal of Operational Research, Elsevier, vol. 95(3), pages 649-655, December.
    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. Saddam Aziz & Cheung-Ming Lai & Ka Hong Loo, 2023. "Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application," Mathematics, MDPI, vol. 11(13), pages 1-18, July.
    2. Di Liang & Jieyi Wang & Ran Bhamra & Liezhao Lu & Yuting Li, 2022. "A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS–ABC Algorithm," Mathematics, MDPI, vol. 10(21), pages 1-24, October.

    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. Perez-Gonzalez, Paz & Framinan, Jose M., 2024. "A review and classification on distributed permutation flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 1-21.
    2. Aidin Delgoshaei & Mohd Khairol Anuar Bin Mohd Ariffin & Zulkiflle B. Leman, 2022. "An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II," Mathematics, MDPI, vol. 10(23), pages 1-28, December.
    3. Benyou Jia & Slobodan P. Simonovic & Pingan Zhong & Zhongbo Yu, 2016. "A Multi-Objective Best Compromise Decision Model for Real-Time Flood Mitigation Operations of Multi-Reservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3363-3387, August.
    4. Pasura Aungkulanon & Walailak Atthirawong & Pongchanun Luangpaiboon & Wirachchaya Chanpuypetch, 2024. "Navigating Supply Chain Resilience: A Hybrid Approach to Agri-Food Supplier Selection," Mathematics, MDPI, vol. 12(10), pages 1-42, May.
    5. Juan Carlos Martín & Veronika Rudchenko & María-Victoria Sánchez-Rebull, 2020. "The Role of Nationality and Hotel Class on Guests’ Satisfaction. A Fuzzy-TOPSIS Approach Applied in Saint Petersburg," Administrative Sciences, MDPI, vol. 10(3), pages 1-24, September.
    6. Bozorgirad, Mir Abbas & Logendran, Rasaratnam, 2013. "Bi-criteria group scheduling in hybrid flowshops," International Journal of Production Economics, Elsevier, vol. 145(2), pages 599-612.
    7. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Determination of the Most Optimal On-Shore Wind Farm Site Location Using a GIS-MCDM Methodology: Evaluating the Case of South Korea," Energies, MDPI, vol. 10(12), pages 1-22, December.
    8. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    9. Choudhary, Devendra & Shankar, Ravi, 2012. "An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India," Energy, Elsevier, vol. 42(1), pages 510-521.
    10. Lupo, Toni, 2015. "Fuzzy ServPerf model combined with ELECTRE III to comparatively evaluate service quality of international airports in Sicily," Journal of Air Transport Management, Elsevier, vol. 42(C), pages 249-259.
    11. He-Yau Kang & Amy H. I. Lee & Tzu-Ting Huang, 2016. "Project Management for a Wind Turbine Construction by Applying Fuzzy Multiple Objective Linear Programming Models," Energies, MDPI, vol. 9(12), pages 1-15, December.
    12. Noori, Amir & Bonakdari, Hossein & Salimi, Amir Hossein & Gharabaghi, Bahram, 2021. "A group Multi-Criteria Decision-Making method for water supply choice optimization," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    13. Bojan Srdjevic & Yvonilde Medeiros, 2008. "Fuzzy AHP Assessment of Water Management Plans," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(7), pages 877-894, July.
    14. Wang, Ying-Ming & Luo, Ying & Hua, Zhongsheng, 2008. "On the extent analysis method for fuzzy AHP and its applications," European Journal of Operational Research, Elsevier, vol. 186(2), pages 735-747, April.
    15. Almalky,, 2020. "An Integrated Multi‐Criteria Decision Support Framework for the Selection of Suppliers in Small and Medium Enterprises based on Green Innovation Ability," OSF Preprints 3cg8j_v1, Center for Open Science.
    16. Nitidetch Koohathongsumrit & Pongchanun Luangpaiboon, 2022. "An integrated FAHP–ZODP approach for strategic marketing information system project selection," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 1792-1809, September.
    17. Deng, Yanfei & Xu, Jiuping & Liu, Ying & Mancl, Karen, 2014. "Biogas as a sustainable energy source in China: Regional development strategy application and decision making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 294-303.
    18. Xin, Li & Xi, Chen & Sagir, Mujgan & Wenbo, Zhang, 2023. "How can infectious medical waste be forecasted and transported during the COVID-19 pandemic? A hybrid two-stage method," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    19. Waseem Alam & Haiyan Wang & Amjad Pervez & Muhammad Safdar & Arshad Jamal & Meshal Almoshaogeh & Hassan M. Al-Ahmadi, 2024. "Analysis and Prediction of Risky Driving Behaviors Using Fuzzy Analytical Hierarchy Process and Machine Learning Techniques," Sustainability, MDPI, vol. 16(11), pages 1-27, May.
    20. Rovick Tarife & Yosuke Nakanishi & Yicheng Zhou & Noel Estoperez & Anacita Tahud, 2023. "Integrated GIS and Fuzzy-AHP Framework for Suitability Analysis of Hybrid Renewable Energy Systems: A Case in Southern Philippines," Sustainability, MDPI, vol. 15(3), pages 1-25, January.

    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:jmathe:v:9:y:2021:i:18:p:2250-:d:634918. 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.