Résumé:
The research activity of this research falls under two major themes: multi objective optimization and supply chain management (SCM). Multiobjective
optimization is a branch of combinatorial optimization whose specificity is to
seek to optimize several objectives simultaneously for the same problem. As
a result, the use of multi-objective optimization methods allows companies to
use optimal strategic solutions to optimize and improve the quality of deci sions for SCM.
In this thesis, we proposed three new multi-objective evolutionary algorithms
to solve three important problems in SCM where our attention was focused on
the part related to transportation and distribution problems, namely the vehi cle routing problem with time windows, the multi-depot green vehicle routing
problem, and the ambulance relocation and dispatching problem.
The first algorithm called External Archive Guided Nondominated Sorting Ge netic Algorithm II (EAG-NSGA-II) is based on improving the NSGA-II algo rithm by integrating the local search method and an external population called
“archive” to store the non-dominated solutions to solve the multi-depot green
vehicle routing problem. While in the second algorithm, an improved MOEA/D
algorithm using simulated annealing (SA) was proposed to solve the ambu lance relocation and dispatching problem. In the last algorithm, a new evolu tionary algorithm was developed by combining the ϵ-MOEA algorithm, local
search method, and multi-type crossover operators to solve the vehicle routing
problem with time windows.
After being tested, the proposed algorithms have shown encouraging results in
both solving the vehicle routing problem with time windows, the multi-depot
green vehicle routing problem, and the ambulance relocation and dispatching
problem, and have been found to be more efficient compared to many multi objective evolutionary algorithms.
