4 présentations

• 
  15h30 - 15h55

  Lagrangean Relaxation for the Cross-Dock Door Assignment Problem

  • Wael Nassief, prés., Université Concordia
  • Ivan Contreras, Concordia University
  • Rami A. M. As'ad, American University of Sharjah

  In this talk, we study a Cross-dock door assignment problem. We present a mixed integer programming formulation that is embedded into a Lagrangean relaxation to obtain bounds on the optimal solution value. A heuristic is used to obtain high quality feasible solutions. Numerical results are reported.

• 
  15h55 - 16h20

  Heuristics for Truck Scheduling in Cross-Docking Terminals

  • Wenying Yan, prés., Concordia University
  • Ivan Contreras, Concordia University

  In this talk we study a truck scheduling problem arising in cross-docking terminals. It consists of sequencing a set of inbound and outbound trucks to a single strip and stack door to minimize the makespan. We present four different heuristic algorithms: a local search, a simulated annealing, a large neighborhood search, and a beam search. Computational experiments are reported.

• 
  16h20 - 16h45

  A Two Stage Optimization Approach to Schedule Material Handling at Cross-Cock

  • Mohammad Yousef Maknoon, prés., Polytechnique Montréal
  • Pierre Baptiste, Polytechnique Montréal
  • Miguel F. Anjos, GERAD, Polytechnique Montréal

  The operational cost at the cross-dock relates to the manner of processing freight that includes unloading, transferring, consolidating and loading products. In this talk, we represent a two stage optimization approach to synchronize the internal process that minimizes the operational cost.

• 
  16h45 - 17h10

  Column Generation for the Cross-Dock Assignment and Vehicle Routing Problem

  • Furkan Enderer, prés., Université Concordia
  • Ivan Contreras, Concordia University
  • Claudio Contardo, GERAD - ESG UQÀM

  In this talk we study an integrated cross-dock assignment and vehicle routing problem. An MIP formulation based on a set partitioning reformulation is presented and a column generation is used to obtain lower bounds. A heuristic algorithm is also developed to efficiently obtain upper bounds. Computational results are reported.