The FLP can be viewed as a generalization of Quadratic Assignment Problem (QAP), and thus is belongs to the class of NP-completed. The exact method can be applied to large instances but it will be time consuming. Hence the heuristic methods have been built to obtain a near optimal solution of the problem.
This thesis proposes hybrid meta-heuristic approaches to solve multi-line facility layout problem (MLFLP). The proposed algorithm is the hybridization of estimation distribution algorithm (EDA), particle swarm optimization (PSO), tabu search (TS) and enchanted by heuristic procedure. Heuristic procedure includes three steps: descendent table, drawing facilities, and heuristic initial particles. Heuristic procedure can prevent the algorithm trapped in local optimal. The multi–lines layout problem assigns a few facilities in two or more rows into industrial plant. Proposed algorithm applies in 3 case studies from others researches. For evaluating the proposed algorithm, the results are compared with other researches. From the results proposed algorithm can solve MLFLP effectively and efficiently. Proposed algorithm perform better than other approaches.

The FLP can be viewed as a generalization of Quadratic Assignment Problem (QAP), and thus is belongs to the class of NP-completed. The exact method can be applied to large instances but it will be time consuming. Hence the heuristic methods have been built to obtain a near optimal solution of the problem.
This thesis proposes hybrid meta-heuristic approaches to solve multi-line facility layout problem (MLFLP). The proposed algorithm is the hybridization of estimation distribution algorithm (EDA), particle swarm optimization (PSO), tabu search (TS) and enchanted by heuristic procedure. Heuristic procedure includes three steps: descendent table, drawing facilities, and heuristic initial particles. Heuristic procedure can prevent the algorithm trapped in local optimal. The multi–lines layout problem assigns a few facilities in two or more rows into industrial plant. Proposed algorithm applies in 3 case studies from others researches. For evaluating the proposed algorithm, the results are compared with other researches. From the results proposed algorithm can solve MLFLP effectively and efficiently. Proposed algorithm perform better than other approaches.

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