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Bi-objective optimization of a three-echelon multi-server supply-chain problem in congested systems: Modeling and solution

Maghsoudlou, H ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1016/j.cie.2016.07.008
  3. Publisher: Elsevier Ltd , 2016
  4. Abstract:
  5. A novel bi-objective three-echelon supply chain problem is formulated in this paper in which cross-dock facilities to transport the products are modeled as an M/M/m queuing system. The proposed model is validated using the epsilon constraint method when applied to solve some small-size problems. Since the problem belongs to the class of NP-hard and that it is of a bi-objective type, a multi-objective particle swarm optimization (MOPSO) algorithm with a new solution structure that satisfies all of the constraints is developed to find Pareto solutions. As there is no benchmark available in literature, three other multi-objective meta-heuristic algorithms called non-dominated ranking genetic algorithm (NRGA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective imperialist competitive algorithm (MOICA) are utilized as well to validate the solutions obtained for large-scale problems. The parameters of the solution algorithms are calibrated using the Taguchi method. The comparison results based on five multi-objective performance metrics used in the AHP-TOPSIS method show that the parameter-tuned MOPSO acts better than the other parameter-tuned algorithms to solve, small, medium, and large-size problems
  6. Keywords:
  7. AHP-Topsis method ; Disruption cost ; Multi-echelon supply chains ; Multi-objective particle swarm optimization ; Algorithms ; Analytic hierarchy process ; Genetic algorithms ; Heuristic algorithms ; Hierarchical systems ; Optimization ; Parameter estimation ; Particle swarm optimization (PSO) ; Queueing networks ; Supply chains ; Taguchi methods ; Bi-objective optimization ; Epsilon-constraint method ; Imperialist competitive algorithms ; Multi echelon supply chains ; Multi objective particle swarm optimization ; Non dominated sorting genetic algorithm (NSGA II) ; Queuing systems ; Topsis method ; Multiobjective optimization
  8. Source: Computers and Industrial Engineering ; Volume 99 , 2016 , Pages 41-62 ; 03608352 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0360835216302339