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.
And
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.