AI SearchAn adaptive particle swarm optimization algorithm for multiobjective job shop scheduling problems
Call Number: AIT Thesis no.ISE-09-17 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. ISE-09-17Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2009Description: 67 p. : illSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 2009 Summary: This research introduces an adaptive particle swarm optimization algorithm for multi objective job shop scheduling problems. The three objectives considered in this research are: minimize makespan, minimize total weighted earliness and minimize total weight tardiness. The objective is to find optimal or near optimal results with these objectives. The solution of multi objective cases will be represented in two ways as weight aggregating approach and Pareto optimization approach. The proposed algorithm uses an adaptive PSO which is approach to solve the problem. The parameter adaptation of PSO is focused on inertia weight and acceleration constants. In order to get high quality of solution the re-initialization strategy and local search and also applied in the proposed algorithm. The proposed algorithm is evaluated by using the benchmark problems provided by the OR-library and compared it with best known results from published works for both single and multi objective cases. The proposed PSO algorithm can find good solutions in both of single and multi objective cases in term of solution quality and computational time. However, the result from the proposed algorithm cannot performed better than 2ST-PSO in some cases because of the number of particles. Also, when the proposed algorithm use the same number of particles with 2ST-PSO, then the result performed better than 2ST-PSO in term of solution quality but take longer computational time.
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Submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Industrial and Manufacturing Engineering, School of Engineering and Technology
This research introduces an adaptive particle swarm optimization algorithm for multi objective job shop scheduling problems. The three objectives considered in this research are: minimize makespan, minimize total weighted earliness and minimize total weight tardiness. The objective is to find optimal or near optimal results with these objectives. The solution of multi objective cases will be represented in two ways as weight aggregating approach and Pareto optimization approach. The proposed algorithm uses an adaptive PSO which is approach to solve the problem. The parameter adaptation of PSO is focused on inertia weight and acceleration constants. In order to get high quality of solution the re-initialization strategy and local search and also applied in the proposed algorithm. The proposed algorithm is evaluated by using the benchmark problems provided by the OR-library and compared it with best known results from published works for both single and multi objective cases. The proposed PSO algorithm can find good solutions in both of single and multi objective cases in term of solution quality and computational time. However, the result from the proposed algorithm cannot performed better than 2ST-PSO in some cases because of the number of particles. Also, when the proposed algorithm use the same number of particles with 2ST-PSO, then the result performed better than 2ST-PSO in term of solution quality but take longer computational time.
Thesis (M.Eng.) - Asian Institute of Technology, 2009
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