@Article{10798587.2017.1293891,
AUTHOR = {Achin Srivastav, Sunil Agrawal},
TITLE = {Multi-Objective Optimization of Slow Moving Inventory System Using Cuckoo  Search},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {2},
PAGES = {343--350},
URL = {http://www.techscience.com/iasc/v24n2/39760},
ISSN = {2326-005X},
ABSTRACT = {This paper focuses on the development of a multi-objective lot size–reorder point backorder inventory 
model for a slow moving item. The three objectives are the minimization of (1) the total annual relevant 
cost, (2) the expected number of stocked out units incurred annually and (3) the expected frequency of 
stockout occasions annually. Laplace distribution is used to model the variability of lead time demand. 
The multi-objective Cuckoo Search (MOCS) algorithm is proposed to solve the model. Pareto curves are 
generated between cost and service levels for decision-makers. A numerical problem is considered on 
a slow moving item to illustrate the results. Furthermore, the performance of the MOCS algorithm is 
evaluated in comparison to multi-objective particle swarm optimization (MOPSO) using metrics, such 
as error ratio, maximum spread and spacing.},
DOI = {10.1080/10798587.2017.1293891}
}