A Genetic Algorithm Framework for the Orienteering Problem with Time Windows

  • Claudio CiancioEmail author
  • Annarita De Maio
  • Demetrio Laganà
  • Francesco Santoro
  • Antonio Violi
Part of the AIRO Springer Series book series (AIROSS, volume 1)


The Orienteering Problem (OP) is a routing problem which has many applications in logistics, tourism and defense. Given a set of nodes, where each node represents a Point of Interest (POI), the orienteering problem aims to design a tour leaving from a starting POI, visiting a subset of POIs and finally arriving at the ending POI. The objective of the problem is to maximize the total score of the visited POIs while the total travel time and the total cost of the route do not exceed two predefined thresholds. Each POI is characterized by a score, a position, a visit time, and a time window in which the POI can be visited. This problem is often investigated to develop tourism trip planning mobile applications. Usually these apps must be able to generate good solutions in few seconds. Therefore, the use of efficient heuristic approaches to find good quality solutions is needed. In this paper we present a genetic algorithm framework combined with some local search operators to deal with the analyzed problem.


Orienteering problem Tourist trip design problem Local search Genetic algorithm 


  1. 1.
    Golden, B.L., Levy, L., Vohra, R.: The orienteering problem. Naval Res. Logist. 34(3), 307–318 (1987)CrossRefGoogle Scholar
  2. 2.
    Kantor, M.G., Rosenwein, M.B.: The orienteering problem with time windows. J. Oper. Res. Soc. 43(6), 629–635 (1992)CrossRefGoogle Scholar
  3. 3.
    Vincent, F.Y., Jewpanya, P., Ting, C.J., Redi, A.P.: Two-level particle swarm optimization for the multi-modal team orienteering problem with time windows. Appl. Soft Comput. 61, 1022–1040 (2017)CrossRefGoogle Scholar
  4. 4.
    Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G.: Mobile recommender systems in tourism. J. Netw. Comput. Appl. 39, 319–333 (2014)CrossRefGoogle Scholar
  5. 5.
    Feillet, D., Dejax, P., Gendreau, M.: Traveling salesman problems with profits. Transp. Sci. 39(2), 188–205 (2005)CrossRefGoogle Scholar
  6. 6.
    Ciancio, C., Ambrogio, G., Gagliardi, F., Musmanno, R.: Heuristic techniques to optimize neural network architecture in manufacturing applications. Neural Comput. Appl. 27(7), 2001–2015 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Claudio Ciancio
    • 1
    Email author
  • Annarita De Maio
    • 2
  • Demetrio Laganà
    • 1
  • Francesco Santoro
    • 3
  • Antonio Violi
    • 1
  1. 1.DIMEGUniversity of CalabriaRendeItaly
  2. 2.DISCUniversity of Milano BicoccaMilanItaly
  3. 3.ITACA srl.RendeItaly

Personalised recommendations