IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v9y2018i3d10.1007_s13198-014-0328-0.html
   My bibliography  Save this article

Novel GA for metropolitan stations of Indian railways when modelled as a TSP

Author

Listed:
  • Kusum Deep

    (Indian Institute of Technology Roorkee)

  • Hadush Mebrahtu

    (Indian Institute of Technology Roorkee)

  • Atulya K. Nagar

    (Liverpool Hope University)

Abstract

In this paper, seven cities that have a direct connection link by Indian railways are modeled as a travelling salesman problem. Then genetic algorithm (GA) is used to solve it by considering three different objective functions, namely: distance, cost and time. For the implementation of GA, the fourth variation of order crossover (OX4) as proposed in Deep and Mebrahtu (Int J Comb Optim Probl Inform 2(3):1–23, 2011a) with inversion mutation and inverted displacement mutations are used. These are programmed in C++ and implemented on the distance, cost and time data obtained from the Indian railways. The minimum and maximum distances of travel, costs of travel and time taken to cover the stations are evaluated. According to the analysis of results that is based on numerical experimentations the sequence of choosing stations really matters. This is observed by the big difference between the minimum and maximum distance, cost and time of travel evaluated. Especially the difference between the minimum and maximum results of distance travelled and time taken to cover the tours is almost twice.

Suggested Citation

  • Kusum Deep & Hadush Mebrahtu & Atulya K. Nagar, 2018. "Novel GA for metropolitan stations of Indian railways when modelled as a TSP," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(3), pages 639-645, June.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:3:d:10.1007_s13198-014-0328-0
    DOI: 10.1007/s13198-014-0328-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-014-0328-0
    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/s13198-014-0328-0?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. Laporte, Gilbert, 1992. "The traveling salesman problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(2), pages 231-247, June.
    2. Richard M. Karp, 1977. "Probabilistic Analysis of Partitioning Algorithms for the Traveling-Salesman Problem in the Plane," Mathematics of Operations Research, INFORMS, vol. 2(3), pages 209-224, August.
    3. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    4. M. Bellmore & G. L. Nemhauser, 1968. "The Traveling Salesman Problem: A Survey," Operations Research, INFORMS, vol. 16(3), pages 538-558, June.
    5. Jon Jouis Bentley, 1992. "Fast Algorithms for Geometric Traveling Salesman Problems," INFORMS Journal on Computing, INFORMS, vol. 4(4), pages 387-411, November.
    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. Scianna, Marco, 2024. "The AddACO: A bio-inspired modified version of the ant colony optimization algorithm to solve travel salesman problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 218(C), pages 357-382.
    2. Shengbin Wang & Weizhen Rao & Yuan Hong, 2020. "A distance matrix based algorithm for solving the traveling salesman problem," Operational Research, Springer, vol. 20(3), pages 1505-1542, September.
    3. Yannis Marinakis & Athanasios Migdalas & Panos M. Pardalos, 2009. "Multiple phase neighborhood Search—GRASP based on Lagrangean relaxation, random backtracking Lin–Kernighan and path relinking for the TSP," Journal of Combinatorial Optimization, Springer, vol. 17(2), pages 134-156, February.
    4. Bismark Singh & Lena Oberfichtner & Sergey Ivliev, 2023. "Heuristics for a cash-collection routing problem with a cluster-first route-second approach," Annals of Operations Research, Springer, vol. 322(1), pages 413-440, March.
    5. Yannis Marinakis & Athanasios Migdalas & Panos M. Pardalos, 2005. "A Hybrid Genetic—GRASP Algorithm Using Lagrangean Relaxation for the Traveling Salesman Problem," Journal of Combinatorial Optimization, Springer, vol. 10(4), pages 311-326, December.
    6. Jean Bertrand Gauthier & Stefan Irnich, 2020. "Inter-Depot Moves and Dynamic-Radius Search for Multi-Depot Vehicle Routing Problems," Working Papers 2004, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    7. Luca Maria Gambardella & Marco Dorigo, 2000. "An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem," INFORMS Journal on Computing, INFORMS, vol. 12(3), pages 237-255, August.
    8. Kafle, Nabin & Zou, Bo & Lin, Jane, 2017. "Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 62-82.
    9. Renaud, Jacques & Boctor, Fayez F., 2002. "A sweep-based algorithm for the fleet size and mix vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 140(3), pages 618-628, August.
    10. Muren, & Wu, Jianjun & Zhou, Li & Du, Zhiping & Lv, Ying, 2019. "Mixed steepest descent algorithm for the traveling salesman problem and application in air logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 87-102.
    11. William Cook & André Rohe, 1999. "Computing Minimum-Weight Perfect Matchings," INFORMS Journal on Computing, INFORMS, vol. 11(2), pages 138-148, May.
    12. Luc Muyldermans & Patrick Beullens & Dirk Cattrysse & Dirk Van Oudheusden, 2005. "Exploring Variants of 2-Opt and 3-Opt for the General Routing Problem," Operations Research, INFORMS, vol. 53(6), pages 982-995, December.
    13. Manerba, Daniele & Mansini, Renata & Riera-Ledesma, Jorge, 2017. "The Traveling Purchaser Problem and its variants," European Journal of Operational Research, Elsevier, vol. 259(1), pages 1-18.
    14. Tzur, Michal & Drezner, Ehud, 2011. "A lookahead partitioning heuristic for a new assignment and scheduling problem in a distribution system," European Journal of Operational Research, Elsevier, vol. 215(2), pages 325-336, December.
    15. Marc Francke & Alex Van de Minne, 2021. "Modeling unobserved heterogeneity in hedonic price models," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(4), pages 1315-1339, December.
    16. Menezes, Mozart B.C. & Ruiz-Hernández, Diego & Verter, Vedat, 2016. "A rough-cut approach for evaluating location-routing decisions via approximation algorithms," Transportation Research Part B: Methodological, Elsevier, vol. 87(C), pages 89-106.
    17. Timo Hintsch & Stefan Irnich, 2018. "Exact Solution of the Soft-Clustered Vehicle Routing Problem," Working Papers 1813, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    18. Mauro Birattari & Prasanna Balaprakash & Thomas Stützle & Marco Dorigo, 2008. "Estimation-Based Local Search for Stochastic Combinatorial Optimization Using Delta Evaluations: A Case Study on the Probabilistic Traveling Salesman Problem," INFORMS Journal on Computing, INFORMS, vol. 20(4), pages 644-658, November.
    19. Jamal Ouenniche & Prasanna K. Ramaswamy & Michel Gendreau, 2017. "A dual local search framework for combinatorial optimization problems with TSP application," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1377-1398, November.
    20. Jorge E. Mendoza & Bruno Castanier & Christelle Guéret & Andrés L. Medaglia & Nubia Velasco, 2011. "Constructive Heuristics for the Multicompartment Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 45(3), pages 346-363, August.

    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:ijsaem:v:9:y:2018:i:3:d:10.1007_s13198-014-0328-0. 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.