IDEAS home Printed from https://ideas.repec.org/a/spr/topjnl/v29y2021i3d10.1007_s11750-020-00588-5.html
   My bibliography  Save this article

Multi-objective scheduling on two dedicated processors

Author

Listed:
  • Adel Kacem

    (University of Sfax)

  • Abdelaziz Dammak

    (University of Sfax)

Abstract

We study a multi-objective scheduling problem on two dedicated processors. The aim is to minimize simultaneously the makespan, the total tardiness and the total completion time. This NP-hard problem requires the use of well-adapted methods. For this, we adapted genetic algorithms to multi-objective case. Four methods are presented to solve this problem. The first is an aggregative genetic algorithm (GA), the second is a Pareto GA, the third is a non-dominated sorting GA (NSGA-II) and the fourth is a constructive algorithm based on lower bounds (CABLB). We proposed some adapted lower bounds for each criterion to evaluate the quality of the found results on a large set of instances. Indeed, these bounds also make it possible to determine the dominance of one algorithm over another based on the different results found by each of them. We used two metrics to measure the quality of the Pareto front: the hypervolume indicator (HV) and the number of solutions in the Pareto front (ND). The obtained results show the effectiveness of the proposed algorithms.

Suggested Citation

  • Adel Kacem & Abdelaziz Dammak, 2021. "Multi-objective scheduling on two dedicated processors," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 694-721, October.
  • Handle: RePEc:spr:topjnl:v:29:y:2021:i:3:d:10.1007_s11750-020-00588-5
    DOI: 10.1007/s11750-020-00588-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11750-020-00588-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11750-020-00588-5?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. Eva Vallada & Rubén Ruiz, 2012. "Scheduling Unrelated Parallel Machines with Sequence Dependent Setup Times and Weighted Earliness–Tardiness Minimization," Springer Optimization and Its Applications, in: Roger Z. Ríos-Mercado & Yasmín A. Ríos-Solís (ed.), Just-in-Time Systems, chapter 0, pages 67-90, Springer.
    2. I. Alberto & P. Mateo, 2011. "A crossover operator that uses Pareto optimality in its definition," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 67-92, July.
    3. Drozdowski, Maciej, 1996. "Scheduling multiprocessor tasks -- An overview," European Journal of Operational Research, Elsevier, vol. 94(2), pages 215-230, October.
    4. George Li, 1997. "Single machine earliness and tardiness scheduling," European Journal of Operational Research, Elsevier, vol. 96(3), pages 546-558, February.
    5. Adel Kacem & Abdelaziz Dammak, 2019. "Bi-objective scheduling on two dedicated processors," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 13(5), pages 681-700.
    6. Jacek Blazewicz & Klaus H. Ecker & Erwin Pesch & Günter Schmidt & Malgorzata Sterna & Jan Weglarz, 2019. "Handbook on Scheduling," International Handbooks on Information Systems, Springer, edition 2, number 978-3-319-99849-7, November.
    7. Chengbin Chu, 1992. "A branch‐and‐bound algorithm to minimize total tardiness with different release dates," Naval Research Logistics (NRL), John Wiley & Sons, vol. 39(2), pages 265-283, March.
    8. Adel Manaa & Chengbin Chu, 2010. "Scheduling multiprocessor tasks to minimise the makespan on two dedicated processors," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 4(3), pages 265-279.
    9. Chengbin Chu, 1992. "A branch‐and‐bound algorithm to minimize total flow time with unequal release dates," Naval Research Logistics (NRL), John Wiley & Sons, vol. 39(6), pages 859-875, October.
    10. Vallada, Eva & Ruiz, Rubén, 2011. "A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times," European Journal of Operational Research, Elsevier, vol. 211(3), pages 612-622, June.
    11. Gais Alhadi & Imed Kacem & Pierre Laroche & Izzeldin M. Osman, 2020. "Approximation algorithms for minimizing the maximum lateness and makespan on parallel machines," Annals of Operations Research, Springer, vol. 285(1), pages 369-395, February.
    12. López-Ibáñez, Manuel & Stützle, Thomas, 2014. "Automatically improving the anytime behaviour of optimisation algorithms," European Journal of Operational Research, Elsevier, vol. 235(3), pages 569-582.
    13. L. Bianco & J. Blazewicz & P. Dell'Olmo & M. Drozdowski, 1997. "Preemptive multiprocessor task scheduling with release times and time windows," Annals of Operations Research, Springer, vol. 70(0), pages 43-55, April.
    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. Fatma-Zohra Baatout & Mhand Hifi, 2023. "A two-phase hybrid evolutionary algorithm for solving the bi-objective scheduling multiprocessor tasks on two dedicated processors," Journal of Heuristics, Springer, vol. 29(2), pages 229-267, June.
    2. Arthur Kramer & Anand Subramanian, 2019. "A unified heuristic and an annotated bibliography for a large class of earliness–tardiness scheduling problems," Journal of Scheduling, Springer, vol. 22(1), pages 21-57, February.
    3. Christos Koulamas, 1997. "Decomposition and hybrid simulated annealing heuristics for the parallel‐machine total tardiness problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(1), pages 109-125, February.
    4. K. H. Adjallah & K. P. Adzakpa, 2007. "Minimizing maintenance cost involving flow-time and tardiness penalty with unequal release dates," Journal of Risk and Reliability, , vol. 221(1), pages 57-65, March.
    5. Philippe Baptiste & Ruslan Sadykov, 2009. "On scheduling a single machine to minimize a piecewise linear objective function: A compact MIP formulation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(6), pages 487-502, September.
    6. Hongfeng Wang & Min Huang & Junwei Wang, 2019. "An effective metaheuristic algorithm for flowshop scheduling with deteriorating jobs," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2733-2742, October.
    7. Christos Koulamas, 1996. "A total tardiness problem with preprocessing included," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(5), pages 721-735, August.
    8. Dung-Ying Lin & Tzu-Yun Huang, 2021. "A Hybrid Metaheuristic for the Unrelated Parallel Machine Scheduling Problem," Mathematics, MDPI, vol. 9(7), pages 1-20, April.
    9. Yung-Chia Chang & Kuei-Hu Chang & Ching-Ping Zheng, 2022. "Application of a Non-Dominated Sorting Genetic Algorithm to Solve a Bi-Objective Scheduling Problem Regarding Printed Circuit Boards," Mathematics, MDPI, vol. 10(13), pages 1-21, July.
    10. Ali Kordmostafapour & Javad Rezaeian & Iraj Mahdavi & Mahdi Yar Farjad, 2022. "Scheduling unrelated parallel machine problem with multi-mode processing times and batch delivery cost," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1438-1470, December.
    11. Pablo Alvarez-Campana & Felix Villafanez & Fernando Acebes & David Poza, 2024. "Simulation-based approach for Multiproject Scheduling based on composite priority rules," Papers 2406.02102, arXiv.org.
    12. Wu, Lingxiao & Wang, Shuaian, 2018. "Exact and heuristic methods to solve the parallel machine scheduling problem with multi-processor tasks," International Journal of Production Economics, Elsevier, vol. 201(C), pages 26-40.
    13. 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.
    14. Jun-Ho Lee & Hyun-Jung Kim, 2021. "A heuristic algorithm for identical parallel machine scheduling: splitting jobs, sequence-dependent setup times, and limited setup operators," Flexible Services and Manufacturing Journal, Springer, vol. 33(4), pages 992-1026, December.
    15. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2021. "Scheduling Human-Robot Teams in collaborative working cells," International Journal of Production Economics, Elsevier, vol. 235(C).
    16. Yepes-Borrero, Juan C. & Perea, Federico & Ruiz, Rubén & Villa, Fulgencia, 2021. "Bi-objective parallel machine scheduling with additional resources during setups," European Journal of Operational Research, Elsevier, vol. 292(2), pages 443-455.
    17. J M S Valente & R A F S Alves, 2005. "Improved lower bounds for the early/tardy scheduling problem with no idle time," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 604-612, May.
    18. Chen, Shih-Hsin & Chen, Min-Chih, 2013. "Addressing the advantages of using ensemble probabilistic models in Estimation of Distribution Algorithms for scheduling problems," International Journal of Production Economics, Elsevier, vol. 141(1), pages 24-33.
    19. Hasani, Ali & Hosseini, Seyed Mohammad Hassan, 2020. "A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: Trade-off between production costs and energy consumption," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    20. F. Angel-Bello & Y. Cardona-Valdés & A. Álvarez, 2019. "Mixed integer formulations for the multiple minimum latency problem," Operational Research, Springer, vol. 19(2), pages 369-398, June.

    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:topjnl:v:29:y:2021:i:3:d:10.1007_s11750-020-00588-5. 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.