Solving the bi-objective personnel assignment problem using particle swarm optimization

被引：13

作者：

Lin, Shih-Ying ^{[2
]}

Horng, Shi-Jinn ^{[1
,2
]}

Kao, Tzong-Wann ^{[6
]}

Fahn, Chin-Shyurng ^{[2
]}

Huang, Deng-Kui ^{[4
]}

Run, Ray-Shine ^{[3
]}

Wang, Yuh-Rau ^{[7
]}

Kuo, I. -Hong ^{[5
]}

机构：

[1] SW Jiaotong Univ, Inst Mobile Commun, Chengdu, Peoples R China

[2] Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan

[3] Natl United Univ, Dept Elect Engn, Miaoli 36003, Taiwan

[4] Lan Yang Inst Technol, Ilan 261, Taiwan

[5] St Marys Coll, Dept Informat Management, Ilan, Taiwan

[6] Taipei Chengshih Univ Sci & Technol, Dept Elect Engn, Taipei, Taiwan

[7] St Johns Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan

来源：

APPLIED SOFT COMPUTING | 2012年 / 12卷 / 09期

关键词：

Bi-objective personnel assignment problem; Particle swarm optimization; Random-key encoding scheme; ALGORITHM;

D O I：

10.1016/j.asoc.2012.03.031

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 f(1)-f(2) heuristic algorithm which was proposed by Huang et al. [3]. The objective of the f(1)-f(2) heuristic algorithm is to get a satisfaction level (SL) value which is satisfied to the bi-objective values f(1), and f(2) 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 f(1)-f(2) heuristic algorithm. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：2840 / 2845

页数：6

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