Multiple Objective Optimization for Multistage Transportation System Under Uncertainty

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 241)


This paper discusses a multistage dynamic transportation allocation problem (DTAP) in a earth-rock transportation system under fuzzy environment, which is a multi-objective optimization process for minimizing total cost, duration and waste. Uncertain parameters are characterized as triangular fuzzy variables and fuzzy expected value concept is introduced to deal with the uncertainty. Dynamic programming particle swarm optimization algorithm (DP-based PSO) is developed to solve the above problem. Finally, the earth and rockfill dam construction in Pubugou Hydropower is used as a practical application example to demonstrate the practical application value of the optimization method, and the result is presented to highlight the newly developed innovation of the model and optimization algorithm.


Dynamic transportation allocation problem Multiple objective optimization Fuzzy environment Particle swarm optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Clerc M, Kennedy J (2002) The particle swarm: Explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1):58–73Google Scholar
  2. 2.
    Dubois D, Prade H (1978) Operations on fuzzy numbers. International Journal of System Sciences 9:613–626Google Scholar
  3. 3.
    Papadopoulos HT (1996) A field service support system using a queueing network model and the priority MVA algorithm. Omega 24(2):195–203Google Scholar
  4. 4.
    Johann H, Sigrid K (2001) Makespan minimization for flow-shop problems with transportation times and a single robot. Discrete Applied Mathematics 112(1-3):199–216Google Scholar
  5. 5.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks 1942–1948Google Scholar
  6. 6.
    Michael JM, Zhang X, Dirck VV (2001) A bi-level programming approach for trip matrix estimation and traffic control problems with stochastic user equilibrium link flows. Transportation Research Part B: Methodological 35(1):23–40Google Scholar
  7. 7.
    Metin C, Selcuk C, Cengiz K et al (2009) Application of axiomatic design and TOPSIS methodologies under fuzzy environment for proposing competitive strategies on Turkish container ports in maritime transportation network. Expert Systems with Applications 36(3):4541–4557Google Scholar
  8. 8.
    Paramet L, Agachai S, William HKL et al (2011) Global optimization method for mixed transportation network design problem: A mixed-integer linear programming approach. Transportation Research Part B: Methodological 45(5):808–827Google Scholar
  9. 9.
    Teodor GC, Nicoletta R, Giovanni S (2004) Advanced freight transportation systems for congested urbanareas. Transportation Research Part C: Emerging Technologies 12(2):119–137Google Scholar
  10. 10.
    Xu J, Zhou X (2011) Fuzzy-like multiple objective decision making Springer-Verlag. Heidelberg, BerlinGoogle Scholar
  11. 11.
    Zadeh LA (1965) Fuzzy sets. Information and Control 8:338–353Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Uncertainty Decision-Making LaboratorySichuan UniversityChengduPeople’s Republic of China

Personalised recommendations