IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i5p509-d508651.html
   My bibliography  Save this article

An Efficient Algorithm for Crowd Logistics Optimization

Author

Listed:
  • Raúl Martín-Santamaría

    (Computer Science and Statistics Department, Universidad Rey Juan Carlos, Tulipán s/n, Móstoles, 28933 Madrid, Spain)

  • Ana D. López-Sánchez

    (Department of Economics, Quantitative Methods and Economic History, Universidad Pablo de Olavide, Ctra. de Utrera km 1, 41013 Sevilla, Spain)

  • María Luisa Delgado-Jalón

    (Computer Science and Statistics Department, Universidad Rey Juan Carlos, Tulipán s/n, Móstoles, 28933 Madrid, Spain
    Department of Economics, Quantitative Methods and Economic History, Universidad Rey Juan Carlos, Tulipán s/n, Móstoles, 28933 Madrid, Spain)

  • J. Manuel Colmenar

    (Computer Science and Statistics Department, Universidad Rey Juan Carlos, Tulipán s/n, Móstoles, 28933 Madrid, Spain)

Abstract

Crowd logistics is a recent trend that proposes the participation of ordinary people in the distribution process of products and goods. This idea is becoming increasingly important to both delivery and retail companies, because it allows them to reduce their delivery costs and, hence, to increase the sustainability of the company. One way to obtain these reductions is to hire external drivers who use their own vehicles to make deliveries to destinations which are close to their daily trips from work to home, for instance. This situation is modelled as the Vehicle Routing Problem with Occasional Drivers (VRPOD), which seeks to minimize the total cost incurred to perform the deliveries using vehicles belonging to the company and occasionally hiring regular citizens to make just one delivery. However, the integration of this features into the distribution system of a company requires a fast and efficient algorithm. In this paper, we propose three different implementations based on the Iterated Local Search algorithm that are able to outperform the state-of-art of this problem with regard to the quality performance. Besides, our proposal is a light-weight algorithm which can produce results in small computation times, allowing its integration into corporate information systems.

Suggested Citation

  • Raúl Martín-Santamaría & Ana D. López-Sánchez & María Luisa Delgado-Jalón & J. Manuel Colmenar, 2021. "An Efficient Algorithm for Crowd Logistics Optimization," Mathematics, MDPI, vol. 9(5), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:509-:d:508651
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/5/509/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/5/509/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Valentina Carbone & Aurélien Rouquet & Christine Roussat, 2017. "The Rise of Crowd Logistics: A New Way to Co‐Create Logistics Value," Post-Print hal-03118967, HAL.
    2. Behrend, Moritz & Meisel, Frank & Fagerholt, Kjetil & Andersson, Henrik, 2019. "An exact solution method for the capacitated item-sharing and crowdshipping problem," European Journal of Operational Research, Elsevier, vol. 279(2), pages 589-604.
    3. Manuel Laguna & Thomas A. Feo & Hal C. Elrod, 1994. "A Greedy Randomized Adaptive Search Procedure for the Two-Partition Problem," Operations Research, INFORMS, vol. 42(4), pages 677-687, August.
    4. Michele D. Simoni & Edoardo Marcucci & Valerio Gatta & Christian G. Claudel, 2020. "Potential last-mile impacts of crowdshipping services: a simulation-based evaluation," Transportation, Springer, vol. 47(4), pages 1933-1954, August.
    5. Wang, Yuan & Zhang, Dongxiang & Liu, Qing & Shen, Fumin & Lee, Loo Hay, 2016. "Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 279-293.
    6. Devari, Aashwinikumar & Nikolaev, Alexander G. & He, Qing, 2017. "Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 105-122.
    7. Thomas A. Feo & Mauricio G. C. Resende & Stuart H. Smith, 1994. "A Greedy Randomized Adaptive Search Procedure for Maximum Independent Set," Operations Research, INFORMS, vol. 42(5), pages 860-878, October.
    8. G. A. Croes, 1958. "A Method for Solving Traveling-Salesman Problems," Operations Research, INFORMS, vol. 6(6), pages 791-812, December.
    9. Michele D. Simoni & Edoardo Marcucci & Valerio Gatta & Christian G. Claudel, 0. "Potential last-mile impacts of crowdshipping services: a simulation-based evaluation," Transportation, Springer, vol. 0, pages 1-22.
    10. Luigi Ranieri & Salvatore Digiesi & Bartolomeo Silvestri & Michele Roccotelli, 2018. "A Review of Last Mile Logistics Innovations in an Externalities Cost Reduction Vision," Sustainability, MDPI, vol. 10(3), pages 1-18, March.
    11. Archetti, Claudia & Savelsbergh, Martin & Speranza, M. Grazia, 2016. "The Vehicle Routing Problem with Occasional Drivers," European Journal of Operational Research, Elsevier, vol. 254(2), pages 472-480.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aleksandra Stanković & Milan Andrejić & Vukašin Pajić & Milorad Kilibarda & Dragan Djurdjević, 2023. "A Novel Survey-QFD-WASPAS Methodological Approach for Designing Crowd Storage Platforms: A Case Study of Serbia," Sustainability, MDPI, vol. 15(10), pages 1-32, May.
    2. Marco Bortolini & Francesca Calabrese & Francesco Gabriele Galizia, 2022. "Crowd Logistics: A Survey of Successful Applications and Implementation Potential in Northern Italy," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    3. Sami Abdulla Mohsen Saleh & A. Halim Kadarman & Shahrel Azmin Suandi & Sanaa A. A. Ghaleb & Waheed A. H. M. Ghanem & Solehuddin Shuib & Qusay Shihab Hamad, 2023. "A Tracklet-before-Clustering Initialization Strategy Based on Hierarchical KLT Tracklet Association for Coherent Motion Filtering Enhancement," Mathematics, MDPI, vol. 11(5), pages 1-21, February.

    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. Nils Boysen & Stefan Fedtke & Stefan Schwerdfeger, 2021. "Last-mile delivery concepts: a survey from an operational research perspective," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 1-58, March.
    2. Pourrahmani, Elham & Jaller, Miguel, 2021. "Crowdshipping in last mile deliveries: Operational challenges and research opportunities," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    3. Ghaderi, Hadi & Zhang, Lele & Tsai, Pei-Wei & Woo, Jihoon, 2022. "Crowdsourced last-mile delivery with parcel lockers," International Journal of Production Economics, Elsevier, vol. 251(C).
    4. Marta Viu-Roig & Eduard J. Alvarez-Palau, 2020. "The Impact of E-Commerce-Related Last-Mile Logistics on Cities: A Systematic Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    5. Marlin Ulmer & Martin Savelsbergh, 2020. "Workforce Scheduling in the Era of Crowdsourced Delivery," Transportation Science, INFORMS, vol. 54(4), pages 1113-1133, July.
    6. Bathke, Henrik & Hartmann, Evi, 2021. "Accepting a crowdsourced delivery - A choice-based conjoint analysis," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Adapting to the Future: Maritime and City Logistics in the Context of Digitalization and Sustainability. Proceedings of the Hamburg International Conf, volume 32, pages 65-95, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    7. Dariusz Milewski & Beata Milewska, 2021. "The Energy Efficiency of the Last Mile in the E-Commerce Distribution in the Context the COVID-19 Pandemic," Energies, MDPI, vol. 14(23), pages 1-13, November.
    8. Sergio Maria Patella & Gianluca Grazieschi & Valerio Gatta & Edoardo Marcucci & Stefano Carrese, 2020. "The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review," Sustainability, MDPI, vol. 13(1), pages 1-29, December.
    9. Lijuan Huang & Guojie Xie & John Blenkinsopp & Raoyi Huang & Hou Bin, 2020. "Crowdsourcing for Sustainable Urban Logistics: Exploring the Factors Influencing Crowd Workers’ Participative Behavior," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    10. Mancini, Simona & Gansterer, Margaretha, 2022. "Bundle generation for last-mile delivery with occasional drivers," Omega, Elsevier, vol. 108(C).
    11. Tapia, Rodrigo J. & Kourounioti, Ioanna & Thoen, Sebastian & de Bok, Michiel & Tavasszy, Lori, 2023. "A disaggregate model of passenger-freight matching in crowdshipping services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    12. Gleb V. Savin, 2021. "The smart city transport and logistics system: Theory, methodology and practice," Upravlenets, Ural State University of Economics, vol. 12(6), pages 67-86, October.
    13. Jagienka Rześny-Cieplińska & Agnieszka Szmelter-Jarosz, 2019. "Assessment of the Crowd Logistics Solutions—The Stakeholders’ Analysis Approach," Sustainability, MDPI, vol. 11(19), pages 1-26, September.
    14. Behrend, Moritz & Meisel, Frank & Fagerholt, Kjetil & Andersson, Henrik, 2019. "An exact solution method for the capacitated item-sharing and crowdshipping problem," European Journal of Operational Research, Elsevier, vol. 279(2), pages 589-604.
    15. Le, Tho V. & Ukkusuri, Satish V. & Xue, Jiawei & Van Woensel, Tom, 2021. "Designing pricing and compensation schemes by integrating matching and routing models for crowd-shipping systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    16. Kexin Bi & Mengke Yang & Latif Zahid & Xiaoguang Zhou, 2020. "A New Solution for City Distribution to Achieve Environmental Benefits within the Trend of Green Logistics: A Case Study in China," Sustainability, MDPI, vol. 12(20), pages 1-25, October.
    17. Xiao, Fei & Wang, Haijun & Guo, Shuojia & Guan, Xu & Liu, Baoshan, 2021. "Efficient and truthful multi-attribute auctions for crowdsourced delivery," International Journal of Production Economics, Elsevier, vol. 240(C).
    18. Agnieszka Szmelter-Jarosz & Jagienka Rześny-Cieplińska, 2019. "Priorities of Urban Transport System Stakeholders According to Crowd Logistics Solutions in City Areas. A Sustainability Perspective," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    19. Cleophas, Catherine & Cottrill, Caitlin & Ehmke, Jan Fabian & Tierney, Kevin, 2019. "Collaborative urban transportation: Recent advances in theory and practice," European Journal of Operational Research, Elsevier, vol. 273(3), pages 801-816.
    20. Fernanda Alves de Araújo & João Gilberto Mendes dos Reis & Marcia Terra da Silva & Emel Aktas, 2022. "A Fuzzy Analytic Hierarchy Process Model to Evaluate Logistics Service Expectations and Delivery Methods in Last-Mile Delivery in Brazil," Sustainability, MDPI, vol. 14(10), pages 1-18, May.

    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:jmathe:v:9:y:2021:i:5:p:509-:d:508651. 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.