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Multi-Objective Simulation Optimization Within MCDM Framework: A Bi-Objective Inventory System

Ramezani, Iman | 2011

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 43689 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Akhavan Niaki, Mohammad Taghi
  7. Abstract:
  8. System design, regardless of the type of system being considered, needs to determine parametersto maximize the system performance criteria.One of solutions of finding best system performance is using simulation optimization. In real world, end users have models with more than one objective and these objectives are conflicting objectives. There are a lot ofmeta-heuristic algorithms to solve multi-objective optimization problems. NSGA-II is one of the most popular proposed meta-heuristic algorithms to solve multi-objective problems. Because of using average to evaluate solutions, process of selecting new generations in this algorithm is such that in every generation some of suitable solutions are removed from considering and to solve this problem, we have used statistical rank and selection or on the other word pareto line was converted to area. Another problem originates from considering pareto dominance in selecting generations leads to have non-dominated solutions that are good in one objective and they are not good in other objectives and to solve this problem, running algorithm is predicted in two stages. In the first stage to ranking solutions we used sum of rank of every solution in all objectives and in the next stage to acquire non-dominated solution we used pareto dominance. After that a two-bin inventory system with two conflicting objectives of service level and cost was modeled and the results were compared. Computational results showed that proposed algorithms can make better diversity of solutions with the paying attention of diversity criteria
  9. Keywords:
  10. Simulation Optimization ; Multiobjective Optimization ; Ranking ; Pareto Front ; Statistical Selection

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