IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v25y2023i1d10.1007_s10668-021-02066-9.html
   My bibliography  Save this article

Genetic-based algorithms for cash-in-transit multi depot vehicle routing problems: economic and environmental optimization

Author

Listed:
  • Xianlong Ge

    (Chongqing Jiaotong University)

  • Yuanzhi Jin

    (Chongqing Jiaotong University
    Sanmenxia Polytechnic)

  • Long Zhang

    (Xinyang Normal University)

Abstract

With the gradual increase of commercial banks and the expansion of their branches, the demand for cash transportation inflates sharply, bringing opportunities to the business development of Cash-In-Transit (CIT) sectors. However, the branches are often distributed in densely populated areas where traffic jams occur from time to time, which poses a severe challenge to the route planning of CIT vehicles. In addition, risk factors need to be considered during the optimization process because the goods transported belong to valuables. In order to effectively deal with the routing problem of CIT sectors, this paper established a bi-objective model and a goal programming model of Risk-Constrained Multi Depot Vehicle Routing Problems (RCMDVRPs) using real-time traffic data. Based on the traditional genetic algorithm, a Hybrid Genetic Algorithm with Intensification procedures (HGAI) is proposed to solve the goal programming model by using a three-level linked list structure to express chromosomes visually. Then, a new Self-constrained Hybrid Genetic Algorithm (SHGA) is designed for the bi-objective model. Besides, an online path updating strategy is developed to guide remote vehicles against time-dependent traffic flows. Finally, the HGAI is performed on benchmark instances to verify its accuracy. Experimental results of performance test show that the algorithm can achieve a gap of about 3% compared with the Best Known Result (BKR). The results of a case study also show that the two models and the corresponding algorithms are feasible and can be used to solve large-scale problems according to the special preferences and goals of decision-makers.

Suggested Citation

  • Xianlong Ge & Yuanzhi Jin & Long Zhang, 2023. "Genetic-based algorithms for cash-in-transit multi depot vehicle routing problems: economic and environmental optimization," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(1), pages 557-586, January.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:1:d:10.1007_s10668-021-02066-9
    DOI: 10.1007/s10668-021-02066-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-021-02066-9
    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/s10668-021-02066-9?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. Zajac, Sandra & Huber, Sandra, 2021. "Objectives and methods in multi-objective routing problems: a survey and classification scheme," European Journal of Operational Research, Elsevier, vol. 290(1), pages 1-25.
    2. Kumar, Anand & Roy, Debjit & Verter, Vedat & Sharma, Dheeraj, 2018. "Integrated fleet mix and routing decision for hazmat transportation: A developing country perspective," European Journal of Operational Research, Elsevier, vol. 264(1), pages 225-238.
    3. Soriano, Adria & Vidal, Thibaut & Gansterer, Margaretha & Doerner, Karl, 2020. "The vehicle routing problem with arrival time diversification on a multigraph," European Journal of Operational Research, Elsevier, vol. 286(2), pages 564-575.
    4. Talarico, Luca & Sörensen, Kenneth & Springael, Johan, 2015. "Metaheuristics for the risk-constrained cash-in-transit vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 244(2), pages 457-470.
    5. Chen, Yujie & Cowling, Peter & Polack, Fiona & Remde, Stephen & Mourdjis, Philip, 2017. "Dynamic optimisation of preventative and corrective maintenance schedules for a large scale urban drainage system," European Journal of Operational Research, Elsevier, vol. 257(2), pages 494-510.
    6. Crevier, Benoit & Cordeau, Jean-Francois & Laporte, Gilbert, 2007. "The multi-depot vehicle routing problem with inter-depot routes," European Journal of Operational Research, Elsevier, vol. 176(2), pages 756-773, January.
    7. Arostegui, Marvin Jr. & Kadipasaoglu, Sukran N. & Khumawala, Basheer M., 2006. "An empirical comparison of Tabu Search, Simulated Annealing, and Genetic Algorithms for facilities location problems," International Journal of Production Economics, Elsevier, vol. 103(2), pages 742-754, October.
    8. Ghaderi, Abdolsalam & Burdett, Robert L., 2019. "An integrated location and routing approach for transporting hazardous materials in a bi-modal transportation network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 49-65.
    9. Calvete, Herminia I. & Gale, Carmen & Oliveros, Maria-Jose & Sanchez-Valverde, Belen, 2007. "A goal programming approach to vehicle routing problems with soft time windows," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1720-1733, March.
    10. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2014. "The bi-objective Pollution-Routing Problem," European Journal of Operational Research, Elsevier, vol. 232(3), pages 464-478.
    11. Talarico, L. & Sörensen, K. & Springael, J., 2015. "The k-dissimilar vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 244(1), pages 129-140.
    12. Ammar Ahmed Musa, 2021. "Goal programming model for optimal water allocation of limited resources under increasing demands," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5956-5984, 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. Tikani, Hamid & Setak, Mostafa & Demir, Emrah, 2021. "A risk-constrained time-dependent cash-in-transit routing problem in multigraph under uncertainty," European Journal of Operational Research, Elsevier, vol. 293(2), pages 703-730.
    2. Zajac, Sandra & Huber, Sandra, 2021. "Objectives and methods in multi-objective routing problems: a survey and classification scheme," European Journal of Operational Research, Elsevier, vol. 290(1), pages 1-25.
    3. Allahyari, Somayeh & Yaghoubi, Saeed & Van Woensel, Tom, 2021. "A novel risk perspective on location-routing planning: An application in cash transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    4. Zandieh, Fatemeh & Ghannadpour, Seyed Farid & Mazdeh, Mohammad Mahdavi, 2024. "New integrated routing and surveillance model with drones and charging station considerations," European Journal of Operational Research, Elsevier, vol. 313(2), pages 527-547.
    5. Dukkanci, Okan & Karsu, Özlem & Kara, Bahar Y., 2022. "Planning sustainable routes: Economic, environmental and welfare concerns," European Journal of Operational Research, Elsevier, vol. 301(1), pages 110-123.
    6. Hoogeboom, Maaike & Dullaert, Wout, 2019. "Vehicle routing with arrival time diversification," European Journal of Operational Research, Elsevier, vol. 275(1), pages 93-107.
    7. Wu, Weitiao & Ma, Jian & Liu, Ronghui & Jin, Wenzhou, 2022. "Multi-class hazmat distribution network design with inventory and superimposed risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    8. Goeke, Dominik & Schneider, Michael, 2015. "Routing a mixed fleet of electric and conventional vehicles," European Journal of Operational Research, Elsevier, vol. 245(1), pages 81-99.
    9. Soriano, Adria & Vidal, Thibaut & Gansterer, Margaretha & Doerner, Karl, 2020. "The vehicle routing problem with arrival time diversification on a multigraph," European Journal of Operational Research, Elsevier, vol. 286(2), pages 564-575.
    10. Goeke, D. & Schneider, M., 2015. "Routing a Mixed Fleet of Electric and Conventional Vehicles," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65939, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    11. Hughes, Michael S. & Lunday, Brian J. & Weir, Jeffrey D. & Hopkinson, Kenneth M., 2021. "The multiple shortest path problem with path deconfliction," European Journal of Operational Research, Elsevier, vol. 292(3), pages 818-829.
    12. Kian, Ramez & Erdoğan, Güneş & de Leeuw, Sander & Sibel Salman, F. & Sabet, Ehsan & Kara, Bahar Y. & Demir, Muhittin H., 2022. "Logistics planning of cash transfer to Syrian refugees in Turkey," European Journal of Operational Research, Elsevier, vol. 296(3), pages 1007-1024.
    13. Vidal, Thibaut & Laporte, Gilbert & Matl, Piotr, 2020. "A concise guide to existing and emerging vehicle routing problem variants," European Journal of Operational Research, Elsevier, vol. 286(2), pages 401-416.
    14. Zhang, Meng & Wang, Nengmin & He, Zhengwen & Jiang, Bin, 2021. "Vehicle routing optimization for hazmat shipments considering catastrophe avoidance and failed edges," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    15. Malihe Niksirat & Mohsen Saffarian & Javad Tayyebi & Adrian Marius Deaconu & Delia Elena Spridon, 2024. "Fuzzy Multi-Objective, Multi-Period Integrated Routing–Scheduling Problem to Distribute Relief to Disaster Areas: A Hybrid Ant Colony Optimization Approach," Mathematics, MDPI, vol. 12(18), pages 1-17, September.
    16. Babagolzadeh, Mahla & Zhang, Yahua & Abbasi, Babak & Shrestha, Anup & Zhang, Anming, 2022. "Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem," Transport Policy, Elsevier, vol. 128(C), pages 38-51.
    17. Sébastien Mouthuy & Florence Massen & Yves Deville & Pascal Van Hentenryck, 2015. "A Multistage Very Large-Scale Neighborhood Search for the Vehicle Routing Problem with Soft Time Windows," Transportation Science, INFORMS, vol. 49(2), pages 223-238, May.
    18. Mo, Pengli & Yao, Yu & D’Ariano, Andrea & Liu, Zhiyuan, 2023. "The vehicle routing problem with underground logistics: Formulation and algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    19. Maiyar, Lohithaksha M & Thakkar, Jitesh J, 2019. "Environmentally conscious logistics planning for food grain industry considering wastages employing multi objective hybrid particle swarm optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 220-248.
    20. Mohri, Seyed Sina & Mohammadi, Mehrdad & Gendreau, Michel & Pirayesh, Amir & Ghasemaghaei, Ali & Salehi, Vahid, 2022. "Hazardous material transportation problems: A comprehensive overview of models and solution approaches," European Journal of Operational Research, Elsevier, vol. 302(1), pages 1-38.

    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:endesu:v:25:y:2023:i:1:d:10.1007_s10668-021-02066-9. 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.