An efficient bi-objective personnel assignment algorithm based on a hybrid particle swarm optimization model

摘要

A hybrid particle swarm optimization (HPSO) algorithm which utilizes random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle swarm optimization (PSO) for solving a bi-objective personnel assignment problem (BOPAP) is presented. The HPSO algorithm which was proposed by Kuo, Horng, Kao, Lin, and Fan (2007) and Kuo et al. (2009b) is used to solve the flow-shop scheduling problem (FSSP). In the research of BOPAP, the main contribution of the work is to improve the f_1_f_2 heu-ristic algorithm which was proposed by Huang, Chiu, Yeh, and Chang (2009). The objective of the f_1_f_2 heuristic algorithm is to get the satisfaction level (SL) value which is satisfied the bi-objective values f_1, andf_2 for the personnel assignment problem. In this paper, PSO is used to search the solution of the input problem in the BOPAP space. Then, with the RK encoding scheme in the virtual space, we can exploit the global search ability of PSO thoroughly. Based on the IE scheme, we can enhance the local search ability of particles. The experimental results show that the solution quality of BOPAP based on the proposed HPSO algorithm for the first objective f_1(i.e., total score), the second objectivef_2 (i.e., stan-dard deviation), the coefficient of variance (CV), and the time cost is far better than that of the/,f_1_f_2 heu-ristic algorithm. To the best our knowledge, this presented result of the BOPAP is the best bi-objective algorithm known.