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A New Heuristic for the Traveling Salesman Problem with Time Windows

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  • Roberto Wolfler Calvo

    (Istitute for Systems Informations and Safety, Joint Research Centre, European Commision, via E. Fermi, 21020 Ispra, and Politecnico di Milano–Dipartimento di Elettronica e Informazione, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy)

Abstract

The aim of this paper is to present a new heuristic method for the Traveling Salesman Problem with Time Windows, based on the solution of an auxiliary problem. The idea is to solve an assignment problem with an ad hoc objective function to obtain a solution close enough to a feasible solution of the original problem. Given this solution, made by a long main tour containing the depot and few small subtours, it is easy to insert all the subtours into the main path using a greedy insertion procedure. The algorithm described applies the proposed constructive scheme and then uses a local search procedure to improve the initial solution. The computational results show the effectiveness of this approach.

Suggested Citation

  • Roberto Wolfler Calvo, 2000. "A New Heuristic for the Traveling Salesman Problem with Time Windows," Transportation Science, INFORMS, vol. 34(1), pages 113-124, February.
    • Handle: RePEc:inm:ortrsc:v:34:y:2000:i:1:p:113-124
    DOI: 10.1287/trsc.34.1.113.12284
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    References listed on IDEAS

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    1. Michel Gendreau & Alain Hertz & Gilbert Laporte & Mihnea Stan, 1998. "A Generalized Insertion Heuristic for the Traveling Salesman Problem with Time Windows," Operations Research, INFORMS, vol. 46(3), pages 330-335, June.
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    Cited by:

    1. Roberto Baldacci & Aristide Mingozzi & Roberto Roberti, 2012. "New State-Space Relaxations for Solving the Traveling Salesman Problem with Time Windows," INFORMS Journal on Computing, INFORMS, vol. 24(3), pages 356-371, August.
    2. Dieter, Peter & Caron, Matthew & Schryen, Guido, 2023. "Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework," European Journal of Operational Research, Elsevier, vol. 311(1), pages 283-300.
    3. G Ioannou & M N Kritikos, 2004. "A synthesis of assignment and heuristic solutions for vehicle routing with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 2-11, January.
    4. Ann M. Campbell & Barrett W. Thomas, 2008. "Probabilistic Traveling Salesman Problem with Deadlines," Transportation Science, INFORMS, vol. 42(1), pages 1-21, February.
    5. Jeffrey W. Ohlmann & Barrett W. Thomas, 2007. "A Compressed-Annealing Heuristic for the Traveling Salesman Problem with Time Windows," INFORMS Journal on Computing, INFORMS, vol. 19(1), pages 80-90, February.
    6. Majed G. Alharbi & Ahmed Stohy & Mohammed Elhenawy & Mahmoud Masoud & Hamiden Abd El-Wahed Khalifa, 2021. "Solving Traveling Salesman Problem with Time Windows Using Hybrid Pointer Networks with Time Features," Sustainability, MDPI, vol. 13(22), pages 1-12, November.
    7. Tomoko Sakiyama & Ikuo Arizono, 2018. "Coordination of Pheromone Deposition Might Solve Time-Constrained Travelling Salesman Problem," Complexity, Hindawi, vol. 2018, pages 1-5, December.
    8. Chang, Tsung-Sheng & Wan, Yat-wah & OOI, Wei Tsang, 2009. "A stochastic dynamic traveling salesman problem with hard time windows," European Journal of Operational Research, Elsevier, vol. 198(3), pages 748-759, November.
    9. Albiach, José & Sanchis, José Marí­a & Soler, David, 2008. "An asymmetric TSP with time windows and with time-dependent travel times and costs: An exact solution through a graph transformation," European Journal of Operational Research, Elsevier, vol. 189(3), pages 789-802, September.

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