Genetic algorithm for multi-item inventory control problem


Chie-Bein Chen

Department of Logistics Management

Takming University of Science and Technology

56, Sec. 1, Huan Shan Rd.

Neihu, Taipei 11451

Taiwan, R.O.C.




Department of International Business

National Dong Hwa University

1, Sec. 2, Da-Hsueh Rd.

Shou-Feng, Hualien, 97401

Taiwan, R.O.C.


Chin Tsai Lin y


Ying-Chan Ting z


Graduate School of Management

Ming Chuan University

250, Chung Shan N. Rd.

Sec. 5, Taipei 11103

Taiwan, R.O.C.


Fan-Kai Hsu


Department of International Business

National Dong Hwa University

Taiwan, R.O.C.




The main purpose of this research is to apply an approximation approach – genetic algorithm to resolve the inventory control problems, which maturely developed during 1960s to 1990s. However, it is still a tough work to deal with the multi-item inventory control optimization problems. Under the constraints on inventory space or budget limitations, to solve the multi-item inventory control problem by traditional approach, it is certainly in difficulty to collect the inventory data and in complexity to compute. Fortunately, an approach is applied into this study without the constraints on multi-item inventory system. It is so-called “optimal inventory policy surface.” This study utilizes the model of “optimal inventory policy surface” and the genetic algorithms (GAs), because of easiness, to resolve the multi-item inventory control optimization problems. In this research, a systematically experimental design of Taguchi method is used to analyze the different settings of both parameters and different ranges of variables of “optimal inventory policy surface” model using GAs as the calculation approach. In addition, the response effects (i.e., the percentage of requisitions short (or inventory shortages)) analysis of “optimal inventory policy surface” is obtained by applying in different settings of GAs program running conditions through a serial analysis.


Keywords: Multi-item inventory control problem, genetic algorithms, Taguchi methods.