IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i7p1059-d1368546.html
   My bibliography  Save this article

WOA: Wombat Optimization Algorithm for Solving Supply Chain Optimization Problems

Author

Listed:
  • Zoubida Benmamoun

    (Faculty of Engineering, Liwa College, Abu Dhabi 41009, United Arab Emirates)

  • Khaoula Khlie

    (Faculty of Business, Liwa College, Abu Dhabi 41009, United Arab Emirates)

  • Mohammad Dehghani

    (Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz 71557-13876, Iran)

  • Youness Gherabi

    (Research Laboratory in Economics, Management, and Business Management (LAREGMA), Faculty of Economics and Management, Hassan I University, Settat 26002, Morocco)

Abstract

Supply Chain (SC) Optimization is a key activity in today’s industry with the goal of increasing operational efficiency, reducing costs, and improving customer satisfaction. Traditional optimization methods often struggle to effectively use resources while handling complex and dynamic Supply chain networks. This paper introduces a novel biomimetic metaheuristic algorithm called the Wombat Optimization Algorithm (WOA) for supply chain optimization. This algorithm replicates the natural behaviors observed in wombats living in the wild, particularly focusing on their foraging tactics and evasive maneuvers towards predators. The theory of WOA is described and then mathematically modeled in two phases: (i) exploration based on the simulation of wombat movements during foraging and trying to find food and (ii) exploitation based on simulating wombat movements when diving towards nearby tunnels to defend against its predators. The effectiveness of WOA in addressing optimization challenges is assessed by handling the CEC 2017 test suite across various problem dimensions, including 10, 30, 50, and 100. The findings of the optimization indicate that WOA demonstrates a strong ability to effectively manage exploration and exploitation, and maintains a balance between them throughout the search phase to deliver optimal solutions for optimization problems. A total of twelve well-known metaheuristic algorithms are called upon to test their performance against WOA in the optimization process. The outcomes of the simulations reveal that WOA outperforms the other algorithms, achieving superior results across most benchmark functions and securing the top ranking as the most efficient optimizer. Using a Wilcoxon rank sum test statistical analysis, it has been proven that WOA outperforms other algorithms significantly. WOA is put to the test with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems to showcase its ability to solve real-world optimization problems. The results of the simulations demonstrate that WOA excels in real-world applications by delivering superior solutions and outperforming its competitors.

Suggested Citation

  • Zoubida Benmamoun & Khaoula Khlie & Mohammad Dehghani & Youness Gherabi, 2024. "WOA: Wombat Optimization Algorithm for Solving Supply Chain Optimization Problems," Mathematics, MDPI, vol. 12(7), pages 1-61, April.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:1059-:d:1368546
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/7/1059/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/7/1059/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mondal, Arijit & Giri, Binoy Krishna & Roy, Sankar Kumar, 2023. "An integrated sustainable bio-fuel and bio-energy supply chain: A novel approach based on DEMATEL and fuzzy-random robust flexible programming with Me measure," Applied Energy, Elsevier, vol. 343(C).
    2. Hong, Jiangtao & Diabat, Ali & Panicker, Vinay V. & Rajagopalan, Sridharan, 2018. "A two-stage supply chain problem with fixed costs: An ant colony optimization approach," International Journal of Production Economics, Elsevier, vol. 204(C), pages 214-226.
    3. Luttiely Santos Oliveira & Ricardo Luiz Machado, 2021. "Application of optimization methods in the closed-loop supply chain: a literature review," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 357-400, February.
    4. Kalpit Patne & Nagesh Shukla & Senevi Kiridena & Manoj Kumar Tiwari, 2018. "Solving closed-loop supply chain problems using game theoretic particle swarm optimisation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5836-5853, September.
    5. Zoubida Benmamoun & Widad Fethallah & Mustapha Ahlaqqach & Ikhlef Jebbor & Mouad Benmamoun & Mariam Elkhechafi, 2023. "Butterfly Algorithm for Sustainable Lot Size Optimization," Sustainability, MDPI, vol. 15(15), pages 1-21, July.
    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. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    2. Chang Liu & Ying Ji & Xinqi Li, 2023. "Closed-Loop Supply Chain Network Design with Flexible Capacity under Uncertain Environment," Sustainability, MDPI, vol. 15(19), pages 1-38, October.
    3. Ahmed Mostafa & Kamal Moustafa & Raafat Elshaer, 2023. "Impact of Fixed Cost Increase on the Optimization of Two-Stage Sustainable Supply Chain Networks," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    4. Ali Pedram & Shahryar Sorooshian & Freselam Mulubrhan & Afshin Abbaspour, 2023. "Incorporating Vehicle-Routing Problems into a Closed-Loop Supply Chain Network Using a Mixed-Integer Linear-Programming Model," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    5. José M. Ferrer & M. Teresa Ortuño & Gregorio Tirado, 2020. "A New Ant Colony-Based Methodology for Disaster Relief," Mathematics, MDPI, vol. 8(4), pages 1-23, April.
    6. Ovidiu Cosma & Petrică C. Pop & Cosmin Sabo, 2020. "An Efficient Hybrid Genetic Approach for Solving the Two-Stage Supply Chain Network Design Problem with Fixed Costs," Mathematics, MDPI, vol. 8(5), pages 1-20, May.
    7. Luttiely Santos Oliveira & Ricardo Luiz Machado, 2021. "Application of optimization methods in the closed-loop supply chain: a literature review," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 357-400, February.
    8. Ahmed E. Barakat & Mahmoud A. Hammad & Hend Adel, 2021. "A Literature Review On Closed-Loop Supply Chain," Business Logistics in Modern Management, Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Croatia, vol. 21, pages 533-545.
    9. Abdul Salam Khan & Qazi Salman Khalid & Khawar Naeem & Rafiq Ahmad & Razaullah Khan & Waqas Saleem & Catalin Iulian Pruncu, 2021. "Application of Exact and Multi-Heuristic Approaches to a Sustainable Closed Loop Supply Chain Network Design," Sustainability, MDPI, vol. 13(5), pages 1-25, February.
    10. Yang, Yuxiang & Goodarzi, Shadi & Bozorgi, Ali & Fahimnia, Behnam, 2021. "Carbon cap-and-trade schemes in closed-loop supply chains: Why firms do not comply?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    11. Shaoren Wang & Yenchun Jim Wu & Ruiting Li, 2022. "An Improved Genetic Algorithm for Location Allocation Problem with Grey Theory in Public Health Emergencies," IJERPH, MDPI, vol. 19(15), pages 1-18, August.
    12. Prajapati, Dhirendra & Pratap, Saurabh & Zhang, Mengdi & Lakshay, & Huang, George Q., 2022. "Sustainable forward-reverse logistics for multi-product delivery and pickup in B2C E-commerce towards the circular economy," International Journal of Production Economics, Elsevier, vol. 253(C).
    13. Jian Zhou & Wenying Xia & Ke Wang & Hui Li & Qianyu Zhang, 2020. "Fuzzy Bi-Objective Closed-Loop Supply Chain Network Design Problem with Multiple Recovery Options," Sustainability, MDPI, vol. 12(17), pages 1-26, August.
    14. Mohsen Tehrani & Surendra M. Gupta, 2021. "Designing a Sustainable Green Closed-Loop Supply Chain under Uncertainty and Various Capacity Levels," Logistics, MDPI, vol. 5(2), pages 1-31, April.
    15. Battaïa, Olga & Guillaume, Romain & Krug, Zoé & Oloruntoba, Richard, 2023. "Environmental and social equity in network design of sustainable closed-loop supply chains," International Journal of Production Economics, Elsevier, vol. 264(C).

    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:12:y:2024:i:7:p:1059-:d:1368546. 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.