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The multi-visit team orienteering problem with precedence constraints

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  • Hanafi, Saïd
  • Mansini, Renata
  • Zanotti, Roberto

Abstract

We study a new variant of the Team Orienteering Problem (TOP) where precedence constraints are introduced. Each customer has a set of tasks that have to be accomplished according to a predefined order by an heterogeneous fleet of vehicles. If a customer is selected, then all the tasks have to be completed by possibly different vehicles. To tackle the problem, we propose an enhancement of the Kernel Search (KS) framework that makes use of different sorting strategies and compare its performance to a Branch-and-Cut algorithm embedding the dynamic separations of different valid inequalities and the use of a simplified KS as primal heuristic. The Branch-and-Cut strongly improves the performance of Gurobi when used to solve the compact problem formulation, whereas the variant of KS comes up to be an extremely effective approach also as primal heuristic embedded into a MIP solver. New benchmark instances and corresponding best known values are provided. Both solution approaches have also been tested on instances of the special case TOP providing extremely good results.

Suggested Citation

  • Hanafi, Saïd & Mansini, Renata & Zanotti, Roberto, 2020. "The multi-visit team orienteering problem with precedence constraints," European Journal of Operational Research, Elsevier, vol. 282(2), pages 515-529.
    • Handle: RePEc:eee:ejores:v:282:y:2020:i:2:p:515-529
    DOI: 10.1016/j.ejor.2019.09.045
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    References listed on IDEAS

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    1. Morteza Keshtkaran & Koorush Ziarati & Andrea Bettinelli & Daniele Vigo, 2016. "Enhanced exact solution methods for the Team Orienteering Problem," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 591-601, January.
    2. Rasmussen, Matias Sevel & Justesen, Tor & Dohn, Anders & Larsen, Jesper, 2012. "The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies," European Journal of Operational Research, Elsevier, vol. 219(3), pages 598-610.
    3. Chao, I-Ming & Golden, Bruce L. & Wasil, Edward A., 1996. "A fast and effective heuristic for the orienteering problem," European Journal of Operational Research, Elsevier, vol. 88(3), pages 475-489, February.
    4. Gunawan, Aldy & Lau, Hoong Chuin & Vansteenwegen, Pieter, 2016. "Orienteering Problem: A survey of recent variants, solution approaches and applications," European Journal of Operational Research, Elsevier, vol. 255(2), pages 315-332.
    5. Enrico Angelelli & Renata Mansini & M. Speranza, 2012. "Kernel Search: a new heuristic framework for portfolio selection," Computational Optimization and Applications, Springer, vol. 51(1), pages 345-361, January.
    6. Vansteenwegen, Pieter & Souffriau, Wouter & Oudheusden, Dirk Van, 2011. "The orienteering problem: A survey," European Journal of Operational Research, Elsevier, vol. 209(1), pages 1-10, February.
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    Cited by:

    1. Wu, Qinghua & He, Mu & Hao, Jin-Kao & Lu, Yongliang, 2024. "An effective hybrid evolutionary algorithm for the clustered orienteering problem," European Journal of Operational Research, Elsevier, vol. 313(2), pages 418-434.
    2. Mansini, Renata & Zanella, Marina & Zanotti, Roberto, 2023. "Optimizing a complex multi-objective personnel scheduling problem jointly complying with requests from customers and staff," Omega, Elsevier, vol. 114(C).
    3. Yang, Yu & Yan, Chiwei & Cao, Yufeng & Roberti, Roberto, 2023. "Planning robust drone-truck delivery routes under road traffic uncertainty," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1145-1160.
    4. Zhen, Lu & Baldacci, Roberto & Tan, Zheyi & Wang, Shuaian & Lyu, Junyan, 2022. "Scheduling heterogeneous delivery tasks on a mixed logistics platform," European Journal of Operational Research, Elsevier, vol. 298(2), pages 680-698.

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