IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i9p3785-d1386716.html
   My bibliography  Save this article

Towards Sustainable Production: An Adaptive Intelligent Optimization Genetic Algorithm for Solid Wood Panel Manufacturing

Author

Listed:
  • Jingzhe Yang

    (School of Technology, Beijing Forestry University, Beijing 100083, China)

  • Yili Zheng

    (School of Technology, Beijing Forestry University, Beijing 100083, China)

  • Jian Wu

    (School of Technology, Beijing Forestry University, Beijing 100083, China)

Abstract

Optimizing production processes to conserve resources and reduce waste has become crucial in pursuing sustainable manufacturing practices. The solid wood panel industry, marked by substantial raw materials and energy consumption, stands at the forefront of addressing this challenge. This research delves into production scheduling and equipment utilization inefficiencies, offering innovative solutions for the solid wood panel processing line aimed at achieving environmental sustainability and operational efficiency. The study is articulated through two main segments: (1) an exhaustive analysis and the development of a simulation system for the solid wood panel processing line, delineating all production elements and operational logic, furnished with a user-friendly simulation interface, and (2) a comprehensive evaluation and enhancement of various scheduling algorithms specific to the Flexible Job-Shop Scheduling Problem (FJSP) encountered in solid wood panel workshops. A significant leap forward is made with the introduction of the Adaptive Intelligent Optimization Genetic Algorithm (AIOGA), an evolved version of the standard Genetic Algorithm (GA) engineered for optimal scheduling within the solid wood panel processing line. AIOGA incorporates advanced features such as encoding strategy, population initialization, objective function setting, selection strategy, crossover operation, and mutation operation, demonstrating the methodological depth of the study. We applied AIOGA in a designed FJSP, and AIOGA substantially reduced the maximum completion time to 90 min. It evidenced an improvement of 39.60% over the conventional GA, enhancing the equilibrium of the equipment workload across the system. This research presents a multifaceted strategy to address the scheduling complications inherent in solid wood panel production and highlights the extensive applicability of adaptive intelligent optimization in diverse industrial settings. This study establishes a new paradigm in manufacturing optimization, underlining the valuable integration of sustainability and efficiency in production methodologies.

Suggested Citation

  • Jingzhe Yang & Yili Zheng & Jian Wu, 2024. "Towards Sustainable Production: An Adaptive Intelligent Optimization Genetic Algorithm for Solid Wood Panel Manufacturing," Sustainability, MDPI, vol. 16(9), pages 1-29, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3785-:d:1386716
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/9/3785/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/9/3785/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Allahverdi, Ali, 2015. "The third comprehensive survey on scheduling problems with setup times/costs," European Journal of Operational Research, Elsevier, vol. 246(2), pages 345-378.
    2. Dong, Wenquan & Jin, Mingzhou, 2024. "Automated storage and retrieval system design with variant lane depths," European Journal of Operational Research, Elsevier, vol. 314(2), pages 630-646.
    3. Kurz, Mary E. & Askin, Ronald G., 2003. "Comparing scheduling rules for flexible flow lines," International Journal of Production Economics, Elsevier, vol. 85(3), pages 371-388, September.
    4. J Antony & M Kumar & A Labib, 2008. "Gearing Six Sigma into UK manufacturing SMEs: results from a pilot study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 482-493, April.
    5. Eglese, R. W., 1990. "Simulated annealing: A tool for operational research," European Journal of Operational Research, Elsevier, vol. 46(3), pages 271-281, June.
    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. Maria da Conceição Cunha, 1999. "On Solving Aquifer Management Problems with Simulated Annealing Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(3), pages 153-170, June.
    2. Michael Geurtsen & Jelle Adan & Alp Akçay, 2024. "Integrated maintenance and production scheduling for unrelated parallel machines with setup times," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 1046-1079, September.
    3. Francesca Gennari, 2023. "The transition towards a circular economy. A framework for SMEs," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(4), pages 1423-1457, December.
    4. Meyr, H., 2000. "Simultaneous lotsizing and scheduling by combining local search with dual reoptimization," European Journal of Operational Research, Elsevier, vol. 120(2), pages 311-326, January.
    5. Jianxin Fang & Brenda Cheang & Andrew Lim, 2023. "Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey," Sustainability, MDPI, vol. 15(17), pages 1-44, August.
    6. Ganesan, Viswanath Kumar & Sivakumar, Appa Iyer, 2006. "Scheduling in static jobshops for minimizing mean flowtime subject to minimum total deviation of job completion times," International Journal of Production Economics, Elsevier, vol. 103(2), pages 633-647, October.
    7. H. A. J. Crauwels & C. N. Potts & L. N. Van Wassenhove, 1998. "Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 10(3), pages 341-350, August.
    8. Eva K. Lee & Siddhartha Maheshwary & Jacquelyn Mason & William Glisson, 2006. "Large-Scale Dispensing for Emergency Response to Bioterrorism and Infectious-Disease Outbreak," Interfaces, INFORMS, vol. 36(6), pages 591-607, December.
    9. Shahvari, Omid & Logendran, Rasaratnam, 2016. "Hybrid flow shop batching and scheduling with a bi-criteria objective," International Journal of Production Economics, Elsevier, vol. 179(C), pages 239-258.
    10. Vinicius M. Ton & Nathália C. O. da Silva & Angel Ruiz & José E. Pécora & Cassius T. Scarpin & Valérie Bélenger, 2024. "Real-time management of intra-hospital patient transport requests," Health Care Management Science, Springer, vol. 27(2), pages 208-222, June.
    11. M Kumral & P A Dowd, 2005. "A simulated annealing approach to mine production scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 922-930, August.
    12. Lucic, Panta & Teodorovic, Dusan, 1999. "Simulated annealing for the multi-objective aircrew rostering problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(1), pages 19-45, January.
    13. Hinder, Oliver & Mason, Andrew J., 2017. "A novel integer programing formulation for scheduling with family setup times on a single machine to minimize maximum lateness," European Journal of Operational Research, Elsevier, vol. 262(2), pages 411-423.
    14. Ravindran Vijayalakshmi, Vipin & Schröder, Marc & Tamir, Tami, 2024. "Minimizing total completion time with machine-dependent priority lists," European Journal of Operational Research, Elsevier, vol. 315(3), pages 844-854.
    15. Hongjun Wei & Jinjiang Yuan & Yuan Gao, 2019. "Transportation and Batching Scheduling for Minimizing Total Weighted Completion Time," Mathematics, MDPI, vol. 7(9), pages 1-10, September.
    16. Ramesh Teegavarapu & Slobodan Simonovic, 2002. "Optimal Operation of Reservoir Systems using Simulated Annealing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(5), pages 401-428, October.
    17. Pan, Quan-Ke & Gao, Liang & Li, Xin-Yu & Gao, Kai-Zhou, 2017. "Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 89-112.
    18. Dirk Briskorn & Konrad Stephan & Nils Boysen, 2022. "Minimizing the makespan on a single machine subject to modular setups," Journal of Scheduling, Springer, vol. 25(1), pages 125-137, February.
    19. Morais, Rafael & Bulhões, Teobaldo & Subramanian, Anand, 2024. "Exact and heuristic algorithms for minimizing the makespan on a single machine scheduling problem with sequence-dependent setup times and release dates," European Journal of Operational Research, Elsevier, vol. 315(2), pages 442-453.
    20. Jin Xu & Natarajan Gautam, 2020. "On competitive analysis for polling systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(6), pages 404-419, September.

    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:16:y:2024:i:9:p:3785-:d:1386716. 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.