IDEAS home Printed from https://ideas.repec.org/a/taf/oabmxx/v3y2016i1p1220662.html
   My bibliography  Save this article

Enhancing heuristic bubble algorithm with simulated annealing

Author

Listed:
  • Mehmet Fatih Yuce
  • Erhan Musaoglu
  • Ali Gunes

Abstract

In this study, a new way to improve the Heuristic Bubble Algorithm (HBA) is presented. HBA is a nature-inspired algorithm, which is a new approach to and initially implemented for, vehicle routing problems of pickup and delivery (VRPPD). Later, it was reinforced to solve other routing problems, such as vehicle routing problem with time windows (VRPTW), and vehicle routing problem with stochastic demands (VRPSD). HBA is a greedy algorithm. It will mostly find local optimal solutions. The proposed method is an improvement over HBA enabling it to reach the global minimum. It uses specialized simulated annealing methods in its operators. A well-known data-set is used to benchmark the proposed method. Better results over HBA and some best results in literature are recorded.

Suggested Citation

  • Mehmet Fatih Yuce & Erhan Musaoglu & Ali Gunes, 2016. "Enhancing heuristic bubble algorithm with simulated annealing," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1220662-122, December.
    • Handle: RePEc:taf:oabmxx:v:3:y:2016:i:1:p:1220662
    DOI: 10.1080/23311975.2016.1220662
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23311975.2016.1220662
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/23311975.2016.1220662?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. R Baldacci & E Bartolini & G Laporte, 2010. "Some applications of the generalized vehicle routing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(7), pages 1072-1077, July.
    2. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    3. Jayaraman, Vaidyanathan & Ross, Anthony, 2003. "A simulated annealing methodology to distribution network design and management," European Journal of Operational Research, Elsevier, vol. 144(3), pages 629-645, February.
    4. H. A. Eiselt & Michel Gendreau & Gilbert Laporte, 1995. "Arc Routing Problems, Part II: The Rural Postman Problem," Operations Research, INFORMS, vol. 43(3), pages 399-414, June.
    5. Vigo, Daniele, 1996. "A heuristic algorithm for the asymmetric capacitated vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 89(1), pages 108-126, February.
    6. J Crispim & J Brandão, 2005. "Metaheuristics applied to mixed and simultaneous extensions of vehicle routing problems with backhauls," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(11), pages 1296-1302, November.
    7. D. Abramson, 1991. "Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms," Management Science, INFORMS, vol. 37(1), pages 98-113, January.
    8. Nagy, Gabor & Salhi, Said, 2007. "Location-routing: Issues, models and methods," European Journal of Operational Research, Elsevier, vol. 177(2), pages 649-672, 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. Maria João Santos & Pedro Amorim & Alexandra Marques & Ana Carvalho & Ana Póvoa, 2020. "The vehicle routing problem with backhauls towards a sustainability perspective: a review," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 358-401, July.
    2. Bergmann, Felix M. & Wagner, Stephan M. & Winkenbach, Matthias, 2020. "Integrating first-mile pickup and last-mile delivery on shared vehicle routes for efficient urban e-commerce distribution," Transportation Research Part B: Methodological, Elsevier, vol. 131(C), pages 26-62.
    3. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    4. Fung, Richard Y.K. & Liu, Ran & Jiang, Zhibin, 2013. "A memetic algorithm for the open capacitated arc routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 50(C), pages 53-67.
    5. Park, Junhyuk & Kim, Byung-In, 2010. "The school bus routing problem: A review," European Journal of Operational Research, Elsevier, vol. 202(2), pages 311-319, April.
    6. Chunlin Xin & Jie Wang & Ziping Wang & Chia-Huei Wu & Muhammad Nawaz & Sang-Bing Tsai, 2022. "Reverse logistics research of municipal hazardous waste: a literature review," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 1495-1531, February.
    7. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2013. "Heuristics for multi-attribute vehicle routing problems: A survey and synthesis," European Journal of Operational Research, Elsevier, vol. 231(1), pages 1-21.
    8. Contreras, Ivan & Fernández, Elena, 2012. "General network design: A unified view of combined location and network design problems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 680-697.
    9. Benjamin C. Shelbourne & Maria Battarra & Chris N. Potts, 2017. "The Vehicle Routing Problem with Release and Due Dates," INFORMS Journal on Computing, INFORMS, vol. 29(4), pages 705-723, November.
    10. Drexl, Michael, 2013. "Applications of the vehicle routing problem with trailers and transshipments," European Journal of Operational Research, Elsevier, vol. 227(2), pages 275-283.
    11. Curtin, Kevin M. & Biba, Steve, 2011. "The Transit Route Arc-Node Service Maximization problem," European Journal of Operational Research, Elsevier, vol. 208(1), pages 46-56, January.
    12. Lahyani, Rahma & Khemakhem, Mahdi & Semet, Frédéric, 2015. "Rich vehicle routing problems: From a taxonomy to a definition," European Journal of Operational Research, Elsevier, vol. 241(1), pages 1-14.
    13. Çetinkaya, Cihan & Karaoglan, Ismail & Gökçen, Hadi, 2013. "Two-stage vehicle routing problem with arc time windows: A mixed integer programming formulation and a heuristic approach," European Journal of Operational Research, Elsevier, vol. 230(3), pages 539-550.
    14. Line Blander Reinhardt & Mads Kehlet Jepsen & David Pisinger, 2016. "The Edge Set Cost of the Vehicle Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 50(2), pages 694-707, May.
    15. Zare Mehrjerdi, Yahia & Nadizadeh, Ali, 2013. "Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands," European Journal of Operational Research, Elsevier, vol. 229(1), pages 75-84.
    16. Manzini, Riccardo, 2012. "A top-down approach and a decision support system for the design and management of logistic networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(6), pages 1185-1204.
    17. Yang, Fei & Dai, Ying & Ma, Zu-Jun, 2020. "A cooperative rich vehicle routing problem in the last-mile logistics industry in rural areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    18. Yang, Senyan & Ning, Lianju & Shang, Pan & (Carol) Tong, Lu, 2020. "Augmented Lagrangian relaxation approach for logistics vehicle routing problem with mixed backhauls and time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    19. Lee, Chungmok & Han, Jinil, 2017. "Benders-and-Price approach for electric vehicle charging station location problem under probabilistic travel range," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 130-152.
    20. Jumbo, Olga & Moghaddass, Ramin, 2022. "Resource optimization and image processing for vegetation management programs in power distribution networks," Applied Energy, Elsevier, vol. 319(C).

    More about this item

    Statistics

    Access and download statistics

    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:taf:oabmxx:v:3:y:2016:i:1:p:1220662. 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: Chris Longhurst (email available below). General contact details of provider: http://cogentoa.tandfonline.com/OABM20 .

    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.