IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v47y2011i6p1075-1091.html
   My bibliography  Save this article

Vehicle routing optimization with soft time windows in a fuzzy random environment

Author

Listed:
  • Xu, Jiuping
  • Yan, Fang
  • Li, Steven

Abstract

This paper is concerned with a vehicle routing problem with soft time windows (VRPSTW) in a fuzzy random environment. Two objectives are considered: (1) minimize the total travel cost and (2) maximize the average satisfaction level of all customers. After setting up the model for the VRPSTW in a fuzzy random environment, the fuzzy random expected value concept is used to deal with the constraints and its equivalent crisp model is derived. The global–local–neighbor particle swarm optimization with exchangeable particles (GLNPSO-ep) is employed to solve the equivalent crisp model. A case study is also presented to illustrate the effectiveness of the proposed approach.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transe:v:47:y:2011:i:6:p:1075-1091
    DOI: 10.1016/j.tre.2011.04.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554511000445
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2011.04.002?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. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    2. Figliozzi, Miguel Andres, 2009. "Planning approximations to the average length of vehicle routing problems with time window constraints," Transportation Research Part B: Methodological, Elsevier, vol. 43(4), pages 438-447, May.
    3. Nather, Wolfgang, 2006. "Regression with fuzzy random data," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 235-252, November.
    4. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
    5. Ferland, Jacques A. & Fortin, Luc, 1989. "Vehicles scheduling with sliding time windows," European Journal of Operational Research, Elsevier, vol. 38(2), pages 213-226, January.
    6. Dimitris J. Bertsimas, 1992. "A Vehicle Routing Problem with Stochastic Demand," Operations Research, INFORMS, vol. 40(3), pages 574-585, June.
    7. G. Gutiérrez-Jarpa & V. Marianov & C. Obreque, 2009. "A single vehicle routing problem with fixed delivery and optional collections," IISE Transactions, Taylor & Francis Journals, vol. 41(12), pages 1067-1079.
    8. Min, Hokey, 1991. "A multiobjective vehicle routing problem with soft time windows: the case of a public library distribution system," Socio-Economic Planning Sciences, Elsevier, vol. 25(3), pages 179-188.
    9. Laporte, Gilbert, 1992. "The vehicle routing problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(3), pages 345-358, June.
    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. Wang, S. & Xie, Y.L. & Huang, G.H. & Yao, Y. & Wang, S.Y. & Li, Y.F., 2021. "A Structural Adjustment optimization model for electric-power system management under multiple Uncertainties—A case study of Urumqi city, China," Energy Policy, Elsevier, vol. 149(C).
    2. Nourinejad, Mehdi & Roorda, Matthew J., 2014. "A dynamic carsharing decision support system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 66(C), pages 36-50.
    3. D. G. Mogale & Sri Krishna Kumar & Manoj Kumar Tiwari, 2020. "Green food supply chain design considering risk and post-harvest losses: a case study," Annals of Operations Research, Springer, vol. 295(1), pages 257-284, December.
    4. Taiwo Adetiloye, 2021. "Collaboration Planning of Stakeholders for Sustainable City Logistics Operations," Papers 2107.14049, arXiv.org.
    5. 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.
    6. Samuel Reong & Hui-Ming Wee & Yu-Lin Hsiao, 2022. "20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis," Mathematics, MDPI, vol. 10(19), pages 1-19, October.
    7. Yan Sun & Xinya Li, 2019. "Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncerta," Mathematics, MDPI, vol. 7(8), pages 1-40, August.
    8. Subrat Sarangi & Sudipta Sarangi & Nasim S. Sabounchi, 2023. "How managerial perspectives affect the optimal fleet size and mix model: a multi-objective approach," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 1-23, March.
    9. Wanke, Peter & Barros, C.P. & Nwaogbe, Obioma R., 2016. "Assessing productive efficiency in Nigerian airports using Fuzzy-DEA," Transport Policy, Elsevier, vol. 49(C), pages 9-19.

    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. Shahparvari, Shahrooz & Abbasi, Babak & Chhetri, Prem, 2017. "Possibilistic scheduling routing for short-notice bushfire emergency evacuation under uncertainties: An Australian case study," Omega, Elsevier, vol. 72(C), pages 96-117.
    2. Matteo Salani & Maria Battarra, 2018. "The opportunity cost of time window violations," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(4), pages 343-361, December.
    3. R A Russell & T L Urban, 2008. "Vehicle routing with soft time windows and Erlang travel times," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1220-1228, September.
    4. Hongsheng Zhong & Randolph W. Hall & Maged Dessouky, 2007. "Territory Planning and Vehicle Dispatching with Driver Learning," Transportation Science, INFORMS, vol. 41(1), pages 74-89, February.
    5. Schaumann, Sarah K. & Bergmann, Felix M. & Wagner, Stephan M. & Winkenbach, Matthias, 2023. "Route efficiency implications of time windows and vehicle capacities in first- and last-mile logistics," European Journal of Operational Research, Elsevier, vol. 311(1), pages 88-111.
    6. 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.
    7. Aderemi Oluyinka Adewumi & Olawale Joshua Adeleke, 2018. "A survey of recent advances in vehicle routing problems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 155-172, February.
    8. 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.
    9. Seyedmehdi Mirmohammadsadeghi & Shamsuddin Ahmed, 2015. "Memetic Heuristic Approach for Solving Truck and Trailer Routing Problems with Stochastic Demands and Time Windows," Networks and Spatial Economics, Springer, vol. 15(4), pages 1093-1115, December.
    10. 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.
    11. Kritikos, Manolis N. & Ioannou, George, 2010. "The balanced cargo vehicle routing problem with time windows," International Journal of Production Economics, Elsevier, vol. 123(1), pages 42-51, January.
    12. P. Kabcome & T. Mouktonglang, 2015. "Vehicle Routing Problem for Multiple Product Types, Compartments, and Trips with Soft Time Windows," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2015, pages 1-9, July.
    13. Peng, Fan & Ouyang, Yanfeng, 2012. "Track maintenance production team scheduling in railroad networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1474-1488.
    14. Phan Nguyen Ky Phuc & Nguyen Le Phuong Thao, 2021. "Ant Colony Optimization for Multiple Pickup and Multiple Delivery Vehicle Routing Problem with Time Window and Heterogeneous Fleets," Logistics, MDPI, vol. 5(2), pages 1-13, May.
    15. Liu, Ran & Jiang, Zhibin, 2012. "The close–open mixed vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 220(2), pages 349-360.
    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. Gutiérrez-Jarpa, Gabriel & Desaulniers, Guy & Laporte, Gilbert & Marianov, Vladimir, 2010. "A branch-and-price algorithm for the Vehicle Routing Problem with Deliveries, Selective Pickups and Time Windows," European Journal of Operational Research, Elsevier, vol. 206(2), pages 341-349, October.
    18. Pradhananga, Rojee & Taniguchi, Eiichi & Yamada, Tadashi & Qureshi, Ali Gul, 2014. "Bi-objective decision support system for routing and scheduling of hazardous materials," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 135-148.
    19. Qi, Mingyao & Lin, Wei-Hua & Li, Nan & Miao, Lixin, 2012. "A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 248-257.
    20. 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.

    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:47:y:2011:i:6:p:1075-1091. 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.