IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i16p1982-d617450.html
   My bibliography  Save this article

Optimization of the Real-Time Response to Roadside Incidents through Heuristic and Linear Programming

Author

Listed:
  • Roman Buil

    (Studies of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Rambla Poble Nou, 18, 08018 Barcelona, Spain
    Accenture S.L., Passeig de Sant Gervasi, 51, 08022 Barcelona, Spain)

  • Jesica de Armas

    (Department of Economics and Business, Universitat Pompeu Fabra, Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain)

  • Daniel Riera

    (Studies of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Rambla Poble Nou, 18, 08018 Barcelona, Spain)

  • Sandra Orozco

    (Accenture S.L., Passeig de Sant Gervasi, 51, 08022 Barcelona, Spain)

Abstract

This paper presents a solution for a real-world roadside assistance problem. Roadside incidents can happen at any time. Depending on the type of incident, a specific resource from the roadside assistance company can be sent on site. The problem of allocating resources to these road-side incidents can be stated as a multi-objective function and a large set of constraints, including priorities and preferences, resource capacities and skills, calendars, and extra hours. The request from the client is to a have real-time response and to attempt to use only open source tools. The optimization objectives to consider are the minimization of the operational costs and the minimization of the time to arrive to each incident. In this work, an innovative approach to near-optimally solving this problem in real-time is proposed, combining a heuristic approach and linear programming. The results show the great potential of this approach: operational costs were reduced by 19%, the use of external providers was reduced to half, and the productivity of the resources owned by the client was significantly increased.

Suggested Citation

  • Roman Buil & Jesica de Armas & Daniel Riera & Sandra Orozco, 2021. "Optimization of the Real-Time Response to Roadside Incidents through Heuristic and Linear Programming," Mathematics, MDPI, vol. 9(16), pages 1-20, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1982-:d:617450
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/16/1982/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/16/1982/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    2. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    3. James R. Jackson, 1957. "Simulation research on job shop production," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 4(4), pages 287-295, December.
    4. Suresh, V. & Chaudhuri, Dipak, 1993. "Dynamic scheduling--a survey of research," International Journal of Production Economics, Elsevier, vol. 32(1), pages 53-63, August.
    5. Jun-Ho Lee & Hoon Jang, 2019. "Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    6. Wu, Xueqi & Che, Ada, 2019. "A memetic differential evolution algorithm for energy-efficient parallel machine scheduling," Omega, Elsevier, vol. 82(C), pages 155-165.
    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. Ji, Chenlu & Mandania, Rupal & Liu, Jiyin & Liret, Anne, 2022. "Scheduling on-site service deliveries to minimise the risk of missing appointment times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    2. John E. Fontecha & Oscar O. Guaje & Daniel Duque & Raha Akhavan-Tabatabaei & Juan P. Rodríguez & Andrés L. Medaglia, 2020. "Combined maintenance and routing optimization for large-scale sewage cleaning," Annals of Operations Research, Springer, vol. 286(1), pages 441-474, March.
    3. Yuxin Liu & Zihang Qin & Jin Liu, 2023. "An Improved Genetic Algorithm for the Granularity-Based Split Vehicle Routing Problem with Simultaneous Delivery and Pickup," Mathematics, MDPI, vol. 11(15), pages 1-15, July.
    4. Stefan Vonolfen & Michael Affenzeller, 2016. "Distribution of waiting time for dynamic pickup and delivery problems," Annals of Operations Research, Springer, vol. 236(2), pages 359-382, January.
    5. Richard Eglese & Sofoclis Zambirinis, 2018. "Disruption management in vehicle routing and scheduling for road freight transport: a review," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 1-17, April.
    6. Tao Dai & Xiangqi Fan, 2021. "Multi-Stove Scheduling for Sustainable On-Demand Food Delivery," Sustainability, MDPI, vol. 13(23), pages 1-13, November.
    7. Yan Cheng Hsu & Jose L. Walteros & Rajan Batta, 2020. "Solving the petroleum replenishment and routing problem with variable demands and time windows," Annals of Operations Research, Springer, vol. 294(1), pages 9-46, November.
    8. Xian Cheng & Shaoyi Liao & Zhongsheng Hua, 2017. "A policy of picking up parcels for express courier service in dynamic environments," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2470-2488, May.
    9. Briseida Sarasola & Karl Doerner & Verena Schmid & Enrique Alba, 2016. "Variable neighborhood search for the stochastic and dynamic vehicle routing problem," Annals of Operations Research, Springer, vol. 236(2), pages 425-461, January.
    10. Wang, Jianxin & Lim, Ming K. & Liu, Weihua, 2024. "Promoting intelligent IoT-driven logistics through integrating dynamic demand and sustainable logistics operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    11. 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.
    12. Epaminondas G. Kyriakidis & Theodosis D. Dimitrakos & Constantinos C. Karamatsoukis, 2020. "A Stochastic Single Vehicle Routing Problem with a Predefined Sequence of Customers and Collection of Two Similar Materials," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1559-1582, December.
    13. Ioannou, Petros & Giuliano, Genevieve & Dessouky, Maged & Chen, Pengfei & Dexter, Sue, 2020. "Freight Load Balancing and Efficiencies in Alternative Fuel Freight Modes," Institute of Transportation Studies, Working Paper Series qt3ns4b894, Institute of Transportation Studies, UC Davis.
    14. Stefan Vonolfen & Michael Affenzeller, 2016. "Distribution of waiting time for dynamic pickup and delivery problems," Annals of Operations Research, Springer, vol. 236(2), pages 359-382, January.
    15. Briseida Sarasola & Karl F. Doerner & Verena Schmid & Enrique Alba, 2016. "Variable neighborhood search for the stochastic and dynamic vehicle routing problem," Annals of Operations Research, Springer, vol. 236(2), pages 425-461, January.
    16. Kate, Joeri ten & Teunter, Ruud & Kusumastuti, Ratih Dyah & van Donk, Dirk Pieter, 2017. "Bio-diesel production using mobile processing units: A case in Indonesia," Agricultural Systems, Elsevier, vol. 152(C), pages 121-130.
    17. Ioannou, Petros & Chen, Pengfei, 2023. "Centrally Coordinated Schedules and Routes of Airport Shuttles with LAX Terminals as Application Area," Institute of Transportation Studies, Working Paper Series qt6gg7r6c5, Institute of Transportation Studies, UC Davis.
    18. Nicolas Rincon-Garcia & Ben J. Waterson & Tom J. Cherrett, 2018. "Requirements from vehicle routing software: perspectives from literature, developers and the freight industry," Transport Reviews, Taylor & Francis Journals, vol. 38(1), pages 117-138, January.
    19. Markov, Iliya & Bierlaire, Michel & Cordeau, Jean-François & Maknoon, Yousef & Varone, Sacha, 2018. "A unified framework for rich routing problems with stochastic demands," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 213-240.
    20. Mohamed Cissé & Semih Yalçindag & Yannick Kergosien & Evren Sahin & Christophe Lenté & Andrea Matta, 2017. "OR problems related to Home Health Care: A review of relevant routing and scheduling problems," Post-Print hal-01736714, HAL.

    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:16:p:1982-:d:617450. 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.