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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
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Martín-Santamaría, Raúl & López-Ibáñez, Manuel & Stützle, Thomas & Colmenar, J. Manuel, 2024. "On the automatic generation of metaheuristic algorithms for combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 318(3), pages 740-751.

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