• DocumentCode
    1586451
  • Title

    A contribution to the resolution of stochastic dynamic dial a ride problem with NSGAII

  • Author

    Issaoui, Brahim ; Lazhar, Khelifi ; Zidi, Issam ; Zidi, Kamel ; Ghedira, Khaled

  • Author_Institution
    Univ. of Tunis SOIE-Manage. Higher Inst., Bouchoucha Le Bardo, Tunisia
  • fYear
    2013
  • Firstpage
    54
  • Lastpage
    59
  • Abstract
    This paper presents a mathematical model that aims at describing and to resolving the Stochastic DRP with an approach based on the NSGAII. DRP consists in taking the passenger from a place of departure to a place of arrival. The ultimate aim is to offer an alternative to individually and collectively optimized displacements. The DRP is classified as NP-hard problem. That´s why, most researches have been concentrated on the use of approximate methods to solve it. Indeed, the DRP is a multi-criteria problem. The proposed solution aims to reduce both route and duration in response to a certain quality of service provided. Actually, during the system of transport on demand (TOD) there are many problems that can inhibit the proper functioning of the system, such as failure of the car and bottling. In this work, we have contributed to the resolution of stochastic DRP in solving possible problems when touring vehicles such as bottling, vehicle failures, accidents and vehicle occupancy service. We have developed an approach which is based on the genetic algorithm kind of the kind NSGAII to optimize travel time and elapsed travel time during the shot, taking into account the possible rate of risk in this selected itinerary.
  • Keywords
    computational complexity; genetic algorithms; public transport; stochastic processes; NP-hard problem; NSGAII; TOD system; genetic algorithm; stochastic DRP; stochastic dynamic dial a ride problem; transport on demand; Meteorology; Petroleum; Roads; Simulated annealing; Vehicles; Wheels; Heuristics; Stochastic Dial a ride Problem “SDRP”; combinatorial optimization “CO”; distributed artificial intelligence “DAI”; distributed processing “DP”; genitic algorithm “GA”; multi-agent system “MAS”; multi-objective optimization “MOO”; simulated annealing “SA”; transport on demand genetic algorithm kind NSGAII “TOD-GA”; transport on demand simulated annealing “TOD-SA”;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Hybrid Intelligent Systems (HIS), 2013 13th International Conference on
  • Conference_Location
    Gammarth
  • Print_ISBN
    978-1-4799-2438-7
  • Type

    conf

  • DOI
    10.1109/HIS.2013.6920454
  • Filename
    6920454