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Solving the bi-objective personnel assignment problem using particle swarm optimization |
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| Authors: | Shih-Ying Lin Shi-Jinn Horng Tzong-Wann Kao Chin-Shyurng Fahn Deng-Kui Huang Ray-Shine Run Yuh-Rau Wang I.-Hong Kuo |
| Affiliation: | 1. Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung, Taiwan;2. Department of Mathematics, Graduate Institute of Statistics and Information Science, National Changhua University of Education, Changhua, Taiwan;3. Department of Industrial Education and Technology, National Changhua University of Education, Changhua, Taiwan;4. Department of Operation, Visitor Service, Collection and Information Management, National Museum of Natural Science, Taichung, Taiwan;1. Department of Mathematics and Computer Science, TU Eindhoven, P.O. Box 513, 5600 MB Eindhoven, Netherlands;2. Bergische Universität Wuppertal, Rainer-Gruenter-Street 21, Wuppertal 42119, Germany;3. France Telecom/R&D/BIZZ/DIAM 905, rue Albert Einstein, Sophia-Antipolis Cedex F-06921, France |
| Abstract: | A particle swarm optimization (PSO) algorithm combined with the random-key (RK) encoding scheme (named as PSORK) for solving a bi-objective personnel assignment problem (BOPAP) is presented. The main contribution of this work is to improve the f1_f2 heuristic algorithm which was proposed by Huang et al. [3]. The objective of the f1_f2 heuristic algorithm is to get a satisfaction level (SL) value which is satisfied to the bi-objective values f1, and f2 for the personnel assignment problem. In this paper, PSORK algorithm searches the solution of BOPAP space thoroughly. The experimental results show that the solution quality of BOPAP based on the proposed method is far better than that of the f1_f2 heuristic algorithm. |
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