IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v229y2015i1p451-47410.1007-s10479-014-1770-8.html
   My bibliography  Save this article

Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises

Author

Listed:
  • Ali Hosseinabadi
  • Hajar Siar
  • Shahaboddin Shamshirband
  • Mohammad Shojafar
  • Mohd Nasir

Abstract

Scheduling problems are naturally dynamic. Increasing flexibility will help solve bottleneck issues, increase production, and improve performance and competitive advantage of Small Medium Enterprises (SMEs). Maximum make span, as well as the average workflow time and latency time of parts are considered the objectives of scheduling, which are compatible with the philosophy of on-time production and supply chain management goals. In this study, these objectives were selected to optimize the resource utilization, minimize inventory turnover, and improve commitment to customers; simultaneously controlling these objectives improved system performance. In the job-shop scheduling problem considered in this paper, the three objectives were to find the best total weight of the objectives, maximize the number of reserved jobs and improve job-shop performance. To realize these targets, a multi-parametric objective function was introduced with dynamic and flexible parameters. The other key accomplishment is the development of a new method called TIME_GELS that uses the gravitational emulation local search algorithm (GELS) for solving the multi-objective flexible dynamic job-shop scheduling problem. The proposed algorithm used two of the four parameters, namely velocity and gravity. The searching agents in this algorithm are a set of masses that interact with each other based on Newton’s laws of gravity and motion. The results of the proposed method are presented for slight, mediocre and complete flexibility stages. These provided average improvements of 6.61, 6.5 and 6.54 %. The results supported the efficiency of the proposed method for solving the job-shop scheduling problem particularly in improving SME’s productivity. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Ali Hosseinabadi & Hajar Siar & Shahaboddin Shamshirband & Mohammad Shojafar & Mohd Nasir, 2015. "Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises," Annals of Operations Research, Springer, vol. 229(1), pages 451-474, June.
    • Handle: RePEc:spr:annopr:v:229:y:2015:i:1:p:451-474:10.1007/s10479-014-1770-8
    DOI: 10.1007/s10479-014-1770-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-014-1770-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-014-1770-8?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. Mariano Frutos & Ana Olivera & Fernando Tohmé, 2010. "A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem," Annals of Operations Research, Springer, vol. 181(1), pages 745-765, December.
    2. 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.
    3. Wong, Ka In & Wong, Pak Kin & Cheung, Chun Shun & Vong, Chi Man, 2013. "Modeling and optimization of biodiesel engine performance using advanced machine learning methods," Energy, Elsevier, vol. 55(C), pages 519-528.
    4. Jacomine Grobler & Andries Engelbrecht & Schalk Kok & Sarma Yadavalli, 2010. "Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time," Annals of Operations Research, Springer, vol. 180(1), pages 165-196, November.
    5. Carlos Paternina-Arboleda & Jairo Montoya-Torres & Milton Acero-Dominguez & Maria Herrera-Hernandez, 2008. "Scheduling jobs on a k-stage flexible flow-shop," Annals of Operations Research, Springer, vol. 164(1), pages 29-40, November.
    6. Kyparisis, George J. & Koulamas, Christos, 2006. "Flexible flow shop scheduling with uniform parallel machines," European Journal of Operational Research, Elsevier, vol. 168(3), pages 985-997, February.
    7. Nowicki, Eugeniusz & Smutnicki, Czeslaw, 1998. "The flow shop with parallel machines: A tabu search approach," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 226-253, April.
    8. Riane, Fouad & Artiba, Abdelhakim & E. Elmaghraby, Salah, 1998. "A hybrid three-stage flowshop problem: Efficient heuristics to minimize makespan," European Journal of Operational Research, Elsevier, vol. 109(2), pages 321-329, September.
    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. Xin Yang & Zhenxiang Zeng & Ruidong Wang & Xueshan Sun, 2016. "Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-13, December.
    2. Shahaboddin Shamshirband & Mohammad Shojafar & A. Hosseinabadi & Maryam Kardgar & M. Nasir & Rodina Ahmad, 2015. "OSGA: genetic-based open-shop scheduling with consideration of machine maintenance in small and medium enterprises," Annals of Operations Research, Springer, vol. 229(1), pages 743-758, June.
    3. Hankun Zhang & Borut Buchmeister & Xueyan Li & Robert Ojstersek, 2021. "Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment," Mathematics, MDPI, vol. 9(8), pages 1-22, April.
    4. Zigao Wu & Shaohua Yu & Tiancheng Li, 2019. "A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling," Mathematics, MDPI, vol. 7(6), pages 1-19, June.
    5. Jinchao Chen & Chenglie Du & Pengcheng Han, 2016. "Scheduling Independent Partitions in Integrated Modular Avionics Systems," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-18, December.

    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. 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.
    2. Chen, Lu & Bostel, Nathalie & Dejax, Pierre & Cai, Jianguo & Xi, Lifeng, 2007. "A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal," European Journal of Operational Research, Elsevier, vol. 181(1), pages 40-58, August.
    3. Kurz, Mary E. & Askin, Ronald G., 2004. "Scheduling flexible flow lines with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 159(1), pages 66-82, November.
    4. Ruiz, Ruben & Maroto, Concepcion, 2006. "A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility," European Journal of Operational Research, Elsevier, vol. 169(3), pages 781-800, March.
    5. Pan, Quan-Ke & Wang, Ling & Li, Jun-Qing & Duan, Jun-Hua, 2014. "A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation," Omega, Elsevier, vol. 45(C), pages 42-56.
    6. Quadt, Daniel & Kuhn, Heinrich, 2007. "A taxonomy of flexible flow line scheduling procedures," European Journal of Operational Research, Elsevier, vol. 178(3), pages 686-698, May.
    7. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.
    8. Quadt, Daniel & Kuhn, Heinrich, 2007. "Batch scheduling of jobs with identical process times on flexible flow lines," International Journal of Production Economics, Elsevier, vol. 105(2), pages 385-401, February.
    9. Oguz, Ceyda & Zinder, Yakov & Ha Do, Van & Janiak, Adam & Lichtenstein, Maciej, 2004. "Hybrid flow-shop scheduling problems with multiprocessor task systems," European Journal of Operational Research, Elsevier, vol. 152(1), pages 115-131, January.
    10. Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.
    11. Amin-Naseri, Mohammad Reza & Beheshti-Nia, Mohammad Ali, 2009. "Hybrid flow shop scheduling with parallel batching," International Journal of Production Economics, Elsevier, vol. 117(1), pages 185-196, January.
    12. Zhen Song & Håkan Schunnesson & Mikael Rinne & John Sturgul, 2015. "Intelligent Scheduling for Underground Mobile Mining Equipment," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-21, June.
    13. Seyed Habib A. Rahmati & Abbas Ahmadi & Kannan Govindan, 2018. "A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach," Annals of Operations Research, Springer, vol. 269(1), pages 583-621, October.
    14. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    15. Sungur, Bilal & Basar, Cem & Kaleli, Alirıza, 2023. "Multi-objective optimisation of the emission parameters and efficiency of pellet stove at different supply airflow positions based on machine learning approach," Energy, Elsevier, vol. 278(PA).
    16. Hazar, Hanbey & Gul, Hakan, 2016. "Modeling analysis of chrome carbide (Cr3C2) coating on parts of combustion chamber of a SI engine," Energy, Elsevier, vol. 115(P1), pages 76-87.
    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. Mostafaei, Mostafa & Javadikia, Hossein & Naderloo, Leila, 2016. "Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy," Energy, Elsevier, vol. 115(P1), pages 626-636.
    19. Shesh Narayan Sahu & Yuvraj Gajpal & Swapan Debbarma, 2018. "Two-agent-based single-machine scheduling with switchover time to minimize total weighted completion time and makespan objectives," Annals of Operations Research, Springer, vol. 269(1), pages 623-640, October.
    20. Berghman, Lotte & Leus, Roel, 2015. "Practical solutions for a dock assignment problem with trailer transportation," European Journal of Operational Research, Elsevier, vol. 246(3), pages 787-799.

    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:spr:annopr:v:229:y:2015:i:1:p:451-474:10.1007/s10479-014-1770-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.