IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i14p1625-d591554.html
   My bibliography  Save this article

Simulated Annealing with Restart Strategy for the Path Cover Problem with Time Windows

Author

Listed:
  • Vincent F. Yu

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
    Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, Taiwan)

  • Winarno

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
    Department of Industrial Engineering, University Singaperbangsa Karawang, Karawang 41361, Indonesia)

  • Achmad Maulidin

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan)

  • A. A. N. Perwira Redi

    (Industrial Engineering Department, BINUS Graduate Program—Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia)

  • Shih-Wei Lin

    (Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan
    Department of Industrial and Management, Ming Chi University of Technology, New Taipei 24301, Taiwan
    Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan)

  • Chao-Lung Yang

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan)

Abstract

This research presents a variant of the vehicle routing problem known as the path cover problem with time windows (PCPTW), in which each vehicle starts with a particular customer and finishes its route at another customer. The vehicles serve each customer within the customer’s time windows. PCPTW is motivated by a practical strategy for companies to reduce operational cost by hiring freelance workers, thus allowing workers to directly service customers without reporting to the office. A mathematical programming model is formulated for the problem. This research also proposes a simulated annealing heuristic with restart strategy (SARS) to solve PCPTW and test it on several benchmark datasets. Computational results indicate that the proposed SARS effectively solves PCPTW.

Suggested Citation

  • Vincent F. Yu & Winarno & Achmad Maulidin & A. A. N. Perwira Redi & Shih-Wei Lin & Chao-Lung Yang, 2021. "Simulated Annealing with Restart Strategy for the Path Cover Problem with Time Windows," Mathematics, MDPI, vol. 9(14), pages 1-22, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:14:p:1625-:d:591554
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/14/1625/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/14/1625/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eglese, R. W., 1990. "Simulated annealing: A tool for operational research," European Journal of Operational Research, Elsevier, vol. 46(3), pages 271-281, June.
    2. D Sariklis & S Powell, 2000. "A heuristic method for the open vehicle routing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(5), pages 564-573, May.
    3. Gschwind, Timo & Bianchessi, Nicola & Irnich, Stefan, 2019. "Stabilized branch-price-and-cut for the commodity-constrained split delivery vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 278(1), pages 91-104.
    4. P P Repoussis & C D Tarantilis & G Ioannou, 2007. "The open vehicle routing problem with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(3), pages 355-367, March.
    5. 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.
    6. 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.
    7. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    8. Baozhen Yao & Qianqian Yan & Mengjie Zhang & Yunong Yang, 2017. "Improved artificial bee colony algorithm for vehicle routing problem with time windows," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-18, September.
    9. Wei, Lijun & Zhang, Zhenzhen & Zhang, Defu & Leung, Stephen C.H., 2018. "A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints," European Journal of Operational Research, Elsevier, vol. 265(3), pages 843-859.
    10. Yangkun Xia & Zhuo Fu & Lijun Pan & Fenghua Duan, 2018. "Tabu search algorithm for the distance-constrained vehicle routing problem with split deliveries by order," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-19, May.
    11. Huang, Yixiao & Zhao, Lei & Van Woensel, Tom & Gross, Jean-Philippe, 2017. "Time-dependent vehicle routing problem with path flexibility," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 169-195.
    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. Fabian Riquelme & Elizabeth Montero & Leslie Pérez-Cáceres & Nicolás Rojas-Morales, 2022. "A Track-Based Conference Scheduling Problem," Mathematics, MDPI, vol. 10(21), pages 1-25, October.
    2. Vincent F. Yu & Hadi Susanto & Yu-Hsuan Yeh & Shih-Wei Lin & Yu-Tsung Huang, 2022. "The Vehicle Routing Problem with Simultaneous Pickup and Delivery and Parcel Lockers," Mathematics, MDPI, vol. 10(6), pages 1-22, March.

    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. Sadati, Mir Ehsan Hesam & Çatay, Bülent, 2021. "A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    2. Qiuping Ni & Yuanxiang Tang, 2023. "A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research," Sustainability, MDPI, vol. 15(9), pages 1-37, April.
    3. N. Norouzi & R. Tavakkoli-Moghaddam & M. Ghazanfari & M. Alinaghian & A. Salamatbakhsh, 2012. "A New Multi-objective Competitive Open Vehicle Routing Problem Solved by Particle Swarm Optimization," Networks and Spatial Economics, Springer, vol. 12(4), pages 609-633, December.
    4. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2022. "A Multi-Depot Dynamic Vehicle Routing Problem with Stochastic Road Capacity: An MDP Model and Dynamic Policy for Post-Decision State Rollout Algorithm in Reinforcement Learning," Mathematics, MDPI, vol. 10(15), pages 1-70, July.
    5. Jian Li & Yang Li & Panos M. Pardalos, 2016. "Multi-depot vehicle routing problem with time windows under shared depot resources," Journal of Combinatorial Optimization, Springer, vol. 31(2), pages 515-532, February.
    6. Atefi, Reza & Salari, Majid & C. Coelho, Leandro & Renaud, Jacques, 2018. "The open vehicle routing problem with decoupling points," European Journal of Operational Research, Elsevier, vol. 265(1), pages 316-327.
    7. Liu, Ran & Jiang, Zhibin, 2012. "The close–open mixed vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 220(2), pages 349-360.
    8. 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.
    9. Ehmke, Jan Fabian & Campbell, Ann M. & Thomas, Barrett W., 2018. "Optimizing for total costs in vehicle routing in urban areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 242-265.
    10. 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.
    11. Fleming, Christopher L. & Griffis, Stanley E. & Bell, John E., 2013. "The effects of triangle inequality on the vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 224(1), pages 1-7.
    12. Müller, Juliane, 2010. "Approximative solutions to the bicriterion Vehicle Routing Problem with Time Windows," European Journal of Operational Research, Elsevier, vol. 202(1), pages 223-231, April.
    13. Amir Saeed Nikkhah Qamsari & Seyyed-Mahdi Hosseini-Motlagh & Seyed Farid Ghannadpour, 2022. "A column generation approach for an inventory routing problem with fuzzy time windows," Operational Research, Springer, vol. 22(2), pages 1157-1207, April.
    14. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part II: Metaheuristics," Transportation Science, INFORMS, vol. 39(1), pages 119-139, February.
    15. Yao, Yu & Zhu, Xiaoning & Dong, Hongyu & Wu, Shengnan & Wu, Hailong & Carol Tong, Lu & Zhou, Xuesong, 2019. "ADMM-based problem decomposition scheme for vehicle routing problem with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 156-174.
    16. 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.
    17. Diego Cattaruzza & Nabil Absi & Dominique Feillet, 2018. "Vehicle routing problems with multiple trips," Annals of Operations Research, Springer, vol. 271(1), pages 127-159, December.
    18. Diego Cattaruzza & Nabil Absi & Dominique Feillet, 2016. "Vehicle routing problems with multiple trips," 4OR, Springer, vol. 14(3), pages 223-259, September.
    19. Lee, Jongsung & Kim, Byung-In & Johnson, Andrew L. & Lee, Kiho, 2014. "The nuclear medicine production and delivery problem," European Journal of Operational Research, Elsevier, vol. 236(2), pages 461-472.
    20. Zachariadis, Emmanouil E. & Tarantilis, Christos D. & Kiranoudis, Chris T., 2010. "An adaptive memory methodology for the vehicle routing problem with simultaneous pick-ups and deliveries," European Journal of Operational Research, Elsevier, vol. 202(2), pages 401-411, April.

    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:14:p:1625-:d:591554. 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.