An application of real-coded genetic algorithm (for mixed integer non-linear programming in an optimal two-warehouse inventory policy for deteriorating items with a linear trend in demand and a fixed planning horizon)

The purpose of this research is to discuss an application of real-coded Genetic Algorithm (RCGA) for mixed integer non-linear programming in a two-warehouses inventory control problem. Our objective is to determine an optimal replenishment number, lot-size of a two-warehouse (owned and rented wareho...

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Veröffentlicht in:International journal of computer mathematics Jg. 82; H. 2; S. 163 - 175
Hauptverfasser: Pal, P., Das, C. B., Panda, A., Bhunia, A. K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 01.02.2005
Schlagworte:
Deterioration
Finite planning horizon
Genetic algorithm
Partial backlogging
Two-warehouses inventory
ISSN:0020-7160, 1029-0265
Online-Zugang:Volltext
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Zusammenfassung:The purpose of this research is to discuss an application of real-coded Genetic Algorithm (RCGA) for mixed integer non-linear programming in a two-warehouses inventory control problem. Our objective is to determine an optimal replenishment number, lot-size of a two-warehouse (owned and rented warehouse (RW)) inventory system for deteriorating items removing the impractical assumption regarding the storage capacity of RW. The model is formulated with infinite replenishment, finite planning horizon, linearly time dependent demand (increasing) and partially backlogged shortages. The mathematical formulation of the problem indicates that the model is a constrained non-linear mixed integer problem with one integer and one non-integer variables. To solve this problem, we develop a RCGA with ranking selection, whole arithmetic crossover and mutation (uniform mutation for integer variable and non-uniform for non-integer variable). The proposed model has been solved using this RCGA and illustrated with four numerical examples.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0020-7160
1029-0265
DOI:10.1080/00207160412331296733