IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v62y2014icp68-88.html
   My bibliography  Save this article

Selective vehicle routing problems under uncertainty without recourse

Author

Listed:
  • Allahviranloo, Mahdieh
  • Chow, Joseph Y.J.
  • Recker, Will W.

Abstract

We argue that the selective vehicle routing problem is more appropriate than the conventional VRP in handling uncertainty with limited resources. However, previous formulations of selective VRPs have all been deterministic. Three new formulations are proposed to account for different optimization strategies under uncertain demand (or utility) level: reliable, robust, and fuzzy selective vehicle routing problems. Three parallel genetic algorithms (PGAs) and a classic genetic algorithm are developed and compared to the deterministic solution. PGAs differ based on their communication strategies and diversity in sub-populations. Results show that a PGA, wherein communication between demes, or subpopulations, occurs in every generation and does not eliminate repeated chromosomes, outperforms other algorithms at the cost of higher computation time. A faster variation of PGA is used to solve the non-convex reliable selective VRP, robust selective VRP and the large-scale fuzzy selective VRP, consisting of 200 nodes. Large scale application demonstrates the value of fuzzy selective vehicle routing problem FSVRP in humanitarian logistics.

Suggested Citation

  • Allahviranloo, Mahdieh & Chow, Joseph Y.J. & Recker, Will W., 2014. "Selective vehicle routing problems under uncertainty without recourse," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 68-88.
    • Handle: RePEc:eee:transe:v:62:y:2014:i:c:p:68-88
    DOI: 10.1016/j.tre.2013.12.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554513001981
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2013.12.004?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. Gendreau, Michel & Laporte, Gilbert & Seguin, Rene, 1996. "Stochastic vehicle routing," European Journal of Operational Research, Elsevier, vol. 88(1), pages 3-12, January.
    2. Lam, Terence C. & Small, Kenneth A., 2003. "The Value of Time and Reliability: Measurement from a Value Pricing Experiment," University of California Transportation Center, Working Papers qt47s4z7z5, University of California Transportation Center.
    3. Shapiro, Arnold F., 2009. "Fuzzy random variables," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 307-314, April.
    4. Tan, K.C. & Cheong, C.Y. & Goh, C.K., 2007. "Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation," European Journal of Operational Research, Elsevier, vol. 177(2), pages 813-839, March.
    5. Dimitris J. Bertsimas & Patrick Jaillet & Amedeo R. Odoni, 1990. "A Priori Optimization," Operations Research, INFORMS, vol. 38(6), pages 1019-1033, December.
    6. Huang, Michael & Smilowitz, Karen & Balcik, Burcu, 2012. "Models for relief routing: Equity, efficiency and efficacy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 2-18.
    7. Michel Gendreau & Gilbert Laporte & René Séguin, 1995. "An Exact Algorithm for the Vehicle Routing Problem with Stochastic Demands and Customers," Transportation Science, INFORMS, vol. 29(2), pages 143-155, May.
    8. Patrick Jaillet, 1988. "A Priori Solution of a Traveling Salesman Problem in Which a Random Subset of the Customers Are Visited," Operations Research, INFORMS, vol. 36(6), pages 929-936, December.
    9. Lam, Terence C. & Small, Kenneth A., 0. "The value of time and reliability: measurement from a value pricing experiment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(2-3), pages 231-251, April.
    10. Yin, Yafeng, 2008. "Robust optimal traffic signal timing," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 911-924, December.
    11. Chen, Lichun & Miller-Hooks, Elise, 2012. "Optimal team deployment in urban search and rescue," Transportation Research Part B: Methodological, Elsevier, vol. 46(8), pages 984-999.
    12. Chow, Joseph Y.J. & Regan, Amelia C., 2011. "Network-based real option models," Transportation Research Part B: Methodological, Elsevier, vol. 45(4), pages 682-695, May.
    13. Small, Kenneth A., 2001. "The Value of Pricing," University of California Transportation Center, Working Papers qt0rm449sx, University of California Transportation Center.
    14. Stewart, William R. & Golden, Bruce L., 1983. "Stochastic vehicle routing: A comprehensive approach," European Journal of Operational Research, Elsevier, vol. 14(4), pages 371-385, December.
    15. Sheu, Jiuh-Biing, 2007. "An emergency logistics distribution approach for quick response to urgent relief demand in disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 687-709, November.
    16. Dimitris J. Bertsimas, 1992. "A Vehicle Routing Problem with Stochastic Demand," Operations Research, INFORMS, vol. 40(3), pages 574-585, June.
    17. Holguín-Veras, José & Jaller, Miguel & Wachtendorf, Tricia, 2012. "Comparative performance of alternative humanitarian logistic structures after the Port-au-Prince earthquake: ACEs, PIEs, and CANs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1623-1640.
    18. Xu, Jiuping & Yan, Fang & Li, Steven, 2011. "Vehicle routing optimization with soft time windows in a fuzzy random environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1075-1091.
    19. Sheu, Jiuh-Biing, 2010. "Dynamic relief-demand management for emergency logistics operations under large-scale disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(1), pages 1-17, January.
    20. Astrid S. Kenyon & David P. Morton, 2003. "Stochastic Vehicle Routing with Random Travel Times," Transportation Science, INFORMS, vol. 37(1), pages 69-82, February.
    21. Dominique Feillet & Pierre Dejax & Michel Gendreau, 2005. "Traveling Salesman Problems with Profits," Transportation Science, INFORMS, vol. 39(2), pages 188-205, May.
    22. Ghiani, Gianpaolo & Guerriero, Francesca & Laporte, Gilbert & Musmanno, Roberto, 2003. "Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies," European Journal of Operational Research, Elsevier, vol. 151(1), pages 1-11, November.
    23. Michel Gendreau & Gilbert Laporte & René Séguin, 1996. "A Tabu Search Heuristic for the Vehicle Routing Problem with Stochastic Demands and Customers," Operations Research, INFORMS, vol. 44(3), pages 469-477, June.
    24. Gilbert Laporte & FranÇois V. Louveaux & Luc van Hamme, 2002. "An Integer L -Shaped Algorithm for the Capacitated Vehicle Routing Problem with Stochastic Demands," Operations Research, INFORMS, vol. 50(3), pages 415-423, June.
    25. Joseph Y. J. Chow & Amelia C. Regan, 2011. "Real Option Pricing of Network Design Investments," Transportation Science, INFORMS, vol. 45(1), pages 50-63, February.
    26. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    27. Recker, W. W., 1995. "The household activity pattern problem: General formulation and solution," Transportation Research Part B: Methodological, Elsevier, vol. 29(1), pages 61-77, February.
    28. Li Ping Gan & Will Recker, 2013. "Stochastic Preplanned Household Activity Pattern Problem with Uncertain Activity Participation (SHAPP)," Transportation Science, INFORMS, vol. 47(3), pages 439-454, August.
    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. He, Yuxuan & Liu, Nan, 2015. "Methodology of emergency medical logistics for public health emergencies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 178-200.
    2. Luo, Zhixing & Qin, Hu & Zhang, Dezhi & Lim, Andrew, 2016. "Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 85(C), pages 69-89.
    3. Carlsson, John Gunnar & Behroozi, Mehdi, 2017. "Worst-case demand distributions in vehicle routing," European Journal of Operational Research, Elsevier, vol. 256(2), pages 462-472.
    4. Noorizadegan, Mahdi & Chen, Bo, 2018. "Vehicle routing with probabilistic capacity constraints," European Journal of Operational Research, Elsevier, vol. 270(2), pages 544-555.
    5. Hao Zhang & Yan Cui & Hepu Deng & Shuxian Cui & Huijia Mu, 2021. "An Improved Genetic Algorithm for the Optimal Distribution of Fresh Products under Uncertain Demand," Mathematics, MDPI, vol. 9(18), pages 1-18, September.
    6. Balcik, Burcu, 2017. "Site selection and vehicle routing for post-disaster rapid needs assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 30-58.
    7. Mahdieh Allahviranloo & Thomas Bonet & Jérémy Diez, 2021. "Introducing shared life experience metric in urban planning," Transportation, Springer, vol. 48(3), pages 1125-1148, June.
    8. Giménez-Palacios, Iván & Parreño, Francisco & Álvarez-Valdés, Ramón & Paquay, Célia & Oliveira, Beatriz Brito & Carravilla, Maria Antónia & Oliveira, José Fernando, 2022. "First-mile logistics parcel pickup: Vehicle routing with packing constraints under disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    9. Allahviranloo, Mahdieh & Axhausen, Kay, 2018. "An optimization model to measure utility of joint and solo activities," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 172-187.
    10. Rafael Dawid & David McMillan & Matthew Revie, 2018. "Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty," Energies, MDPI, vol. 11(9), pages 1-17, August.
    11. Kundu, Tanmoy & Sheu, Jiuh-Biing & Kuo, Hsin-Tsz, 2022. "Emergency logistics management—Review and propositions for future research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    12. Santos, Maria João & Curcio, Eduardo & Mulati, Mauro Henrique & Amorim, Pedro & Miyazawa, Flávio Keidi, 2020. "A robust optimization approach for the vehicle routing problem with selective backhauls," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    13. Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2017. "Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 22-37.

    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. Prasanna Balaprakash & Mauro Birattari & Thomas Stützle & Marco Dorigo, 2015. "Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers," Computational Optimization and Applications, Springer, vol. 61(2), pages 463-487, June.
    2. Bertazzi, Luca & Secomandi, Nicola, 2018. "Faster rollout search for the vehicle routing problem with stochastic demands and restocking," European Journal of Operational Research, Elsevier, vol. 270(2), pages 487-497.
    3. Jinil Han & Chungmok Lee & Sungsoo Park, 2014. "A Robust Scenario Approach for the Vehicle Routing Problem with Uncertain Travel Times," Transportation Science, INFORMS, vol. 48(3), pages 373-390, August.
    4. Wen-Huei Yang & Kamlesh Mathur & Ronald H. Ballou, 2000. "Stochastic Vehicle Routing Problem with Restocking," Transportation Science, INFORMS, vol. 34(1), pages 99-112, February.
    5. Ann M. Campbell & Barrett W. Thomas, 2008. "Probabilistic Traveling Salesman Problem with Deadlines," Transportation Science, INFORMS, vol. 42(1), pages 1-21, February.
    6. Soumia Ichoua & Michel Gendreau & Jean-Yves Potvin, 2006. "Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching," Transportation Science, INFORMS, vol. 40(2), pages 211-225, May.
    7. Alan L. Erera & Juan C. Morales & Martin Savelsbergh, 2010. "The Vehicle Routing Problem with Stochastic Demand and Duration Constraints," Transportation Science, INFORMS, vol. 44(4), pages 474-492, November.
    8. Florent Hernandez & Michel Gendreau & Ola Jabali & Walter Rei, 2019. "A local branching matheuristic for the multi-vehicle routing problem with stochastic demands," Journal of Heuristics, Springer, vol. 25(2), pages 215-245, April.
    9. Aykagan Ak & Alan L. Erera, 2007. "A Paired-Vehicle Recourse Strategy for the Vehicle-Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 41(2), pages 222-237, May.
    10. Gendreau, Michel & Laporte, Gilbert & Seguin, Rene, 1996. "Stochastic vehicle routing," European Journal of Operational Research, Elsevier, vol. 88(1), pages 3-12, January.
    11. Chrysanthos E. Gounaris & Wolfram Wiesemann & Christodoulos A. Floudas, 2013. "The Robust Capacitated Vehicle Routing Problem Under Demand Uncertainty," Operations Research, INFORMS, vol. 61(3), pages 677-693, June.
    12. Luo, Zhixing & Qin, Hu & Zhang, Dezhi & Lim, Andrew, 2016. "Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 85(C), pages 69-89.
    13. Beraldi, Patrizia & Bruni, Maria Elena & Laganà, Demetrio & Musmanno, Roberto, 2015. "The mixed capacitated general routing problem under uncertainty," European Journal of Operational Research, Elsevier, vol. 240(2), pages 382-392.
    14. Majid Salavati-Khoshghalb & Michel Gendreau & Ola Jabali & Walter Rei, 2019. "A hybrid recourse policy for the vehicle routing problem with stochastic demands," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(3), pages 269-298, September.
    15. Jorge Oyola & Halvard Arntzen & David L. Woodruff, 2017. "The stochastic vehicle routing problem, a literature review, Part II: solution methods," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 349-388, December.
    16. Shukla, Nagesh & Choudhary, A.K. & Prakash, P.K.S. & Fernandes, K.J. & Tiwari, M.K., 2013. "Algorithm portfolios for logistics optimization considering stochastic demands and mobility allowance," International Journal of Production Economics, Elsevier, vol. 141(1), pages 146-166.
    17. Li, Xiangyong & Tian, Peng & Leung, Stephen C.H., 2010. "Vehicle routing problems with time windows and stochastic travel and service times: Models and algorithm," International Journal of Production Economics, Elsevier, vol. 125(1), pages 137-145, May.
    18. Walter Rei & Michel Gendreau & Patrick Soriano, 2010. "A Hybrid Monte Carlo Local Branching Algorithm for the Single Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 44(1), pages 136-146, February.
    19. Jiang, J. & Ng, K.M. & Teo, K.M., 2016. "Satisficing measure approach for vehicle routing problem with time windows under uncertaintyAuthor-Name: Nguyen, V.A," European Journal of Operational Research, Elsevier, vol. 248(2), pages 404-414.
    20. Nicola Secomandi & François Margot, 2009. "Reoptimization Approaches for the Vehicle-Routing Problem with Stochastic Demands," Operations Research, INFORMS, vol. 57(1), pages 214-230, February.

    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:eee:transe:v:62:y:2014:i:c:p:68-88. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.