IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v2y2018i3p12-d158141.html
   My bibliography  Save this article

Scheduling Approach for the Simulation of a Sustainable Resource Supply Chain

Author

Listed:
  • Henning Strubelt

    (Institute of Logistics and Material Handling Systems, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany)

  • Sebastian Trojahn

    (Institute of Logistics and Material Handling Systems, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany)

  • Sebastian Lang

    (Institute of Logistics and Material Handling Systems, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany)

  • Abdulrahman Nahhas

    (Institute of Logistics and Material Handling Systems, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany)

Abstract

The general goal of waste management is to conserve resources and avoid negative environmental impacts. This paper deals with the optimization of logistics processes at an underground waste storage site by means of solving scheduling issues and reducing setup times, with the help of a simulation model. Specific to underground waste storage is the fact that it is often only a side business to actual mining. With limited capacity and resources, all legal requirements must be met, while the business should still be profitable. This paper discusses the improvement of a logistical system’s performance using machine scheduling approaches with the support of a plant simulation model. The process sequence is determined by means of a priority index. Genetic algorithms are then applied to improve the priority index to further increase performance. Results of the simulation model show that the performance of the logistics system can be increased by up to 400 percent, ensuring adequate system performance for current as well as future demand without changes to the system’s capacities and resources.

Suggested Citation

  • Henning Strubelt & Sebastian Trojahn & Sebastian Lang & Abdulrahman Nahhas, 2018. "Scheduling Approach for the Simulation of a Sustainable Resource Supply Chain," Logistics, MDPI, vol. 2(3), pages 1-11, July.
    • Handle: RePEc:gam:jlogis:v:2:y:2018:i:3:p:12-:d:158141
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/2/3/12/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2305-6290/2/3/12/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gupta, Sushil K & Kyparisis, Jerzy, 1987. "Single machine scheduling research," Omega, Elsevier, vol. 15(3), pages 207-227.
    2. Gupta, Jatinder N.D. & Stafford, Edward Jr., 2006. "Flowshop scheduling research after five decades," European Journal of Operational Research, Elsevier, vol. 169(3), pages 699-711, March.
    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. Kate, H.A. ten & Wijngaard, J. & Zijm, W.H.M., 1995. "Minimizing weighted total earliness, total tardiness and setup costs," Research Report 95A37, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    2. Yeung, Wing-Kwan & Choi, Tsan-Ming & Cheng, T.C.E., 2011. "Supply chain scheduling and coordination with dual delivery modes and inventory storage cost," International Journal of Production Economics, Elsevier, vol. 132(2), pages 223-229, August.
    3. B-J Joo & Y-D Kim, 2009. "A branch-and-bound algorithm for a two-machine flowshop scheduling problem with limited waiting time constraints," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(4), pages 572-582, April.
    4. Kolahan, F. & Liang, M., 1998. "An adaptive TS approach to JIT sequencing with variable processing times and sequence-dependent setups," European Journal of Operational Research, Elsevier, vol. 109(1), pages 142-159, August.
    5. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    6. Waldherr, Stefan & Knust, Sigrid & Briskorn, Dirk, 2017. "Synchronous flow shop problems: How much can we gain by leaving machines idle?," Omega, Elsevier, vol. 72(C), pages 15-24.
    7. Figielska, Ewa, 2014. "A heuristic for scheduling in a two-stage hybrid flowshop with renewable resources shared among the stages," European Journal of Operational Research, Elsevier, vol. 236(2), pages 433-444.
    8. 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.
    9. Matthias Bultmann & Sigrid Knust & Stefan Waldherr, 2018. "Flow shop scheduling with flexible processing times," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(3), pages 809-829, July.
    10. Allahverdi, Ali & Gupta, Jatinder N. D. & Aldowaisan, Tariq, 1999. "A review of scheduling research involving setup considerations," Omega, Elsevier, vol. 27(2), pages 219-239, April.
    11. Li, Wei & Nault, Barrie R. & Ye, Honghan, 2019. "Trade-off balancing in scheduling for flow shop production and perioperative processes," European Journal of Operational Research, Elsevier, vol. 273(3), pages 817-830.
    12. Alfaro-Fernández, Pedro & Ruiz, Rubén & Pagnozzi, Federico & Stützle, Thomas, 2020. "Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems," European Journal of Operational Research, Elsevier, vol. 282(3), pages 835-845.
    13. Hatami, Sara & Ruiz, Rubén & Andrés-Romano, Carlos, 2015. "Heuristics and metaheuristics for the distributed assembly permutation flowshop scheduling problem with sequence dependent setup times," International Journal of Production Economics, Elsevier, vol. 169(C), pages 76-88.
    14. Ruiz-Torres, Alex J. & Ho, Johnny C. & Ablanedo-Rosas, José H., 2011. "Makespan and workstation utilization minimization in a flowshop with operations flexibility," Omega, Elsevier, vol. 39(3), pages 273-282, June.
    15. Elisa Negri & Vibhor Pandhare & Laura Cattaneo & Jaskaran Singh & Marco Macchi & Jay Lee, 2021. "Field-synchronized Digital Twin framework for production scheduling with uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1207-1228, April.
    16. Abdeljaouad, Mohamed Amine & Bahroun, Zied & Omrane, Anissa & Fondrevelle, Julien, 2015. "Job-shop production scheduling with reverse flows," European Journal of Operational Research, Elsevier, vol. 244(1), pages 117-128.
    17. Ding, Jian-Ya & Song, Shiji & Wu, Cheng, 2016. "Carbon-efficient scheduling of flow shops by multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 248(3), pages 758-771.
    18. Pan, Quan-Ke & Ruiz, Rubén, 2014. "An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem," Omega, Elsevier, vol. 44(C), pages 41-50.
    19. Liji Shen & Jatinder N. D. Gupta, 2018. "Family scheduling with batch availability in flow shops to minimize makespan," Journal of Scheduling, Springer, vol. 21(2), pages 235-249, April.
    20. Shiuan Wan & Yi-Ping Wang, 2020. "The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data," Agriculture, MDPI, vol. 10(10), pages 1-17, October.

    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:jlogis:v:2:y:2018:i:3:p:12-:d:158141. 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.