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

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 et al., 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 f1_f2 heuristic algorithm which was proposed by Huang, Chiu, Yeh, and Chang (2009). The objective of the f1_f2 heuristic algorithm is to get the satisfaction level (SL) value which is satisfied the bi-objective values f1, and f2 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 f1 (i.e., total score), the second objective f2 (i.e., standard deviation), the coefficient of variance (CV), and the time cost is far better than that of the f1_f2 heuristic algorithm. To the best our knowledge, this presented result of the BOPAP is the best bi-objective algorithm known.