TY - EJOU
AU - Srivastav, Achin
AU - Agrawal, Sunil
TI - Multi-Objective Optimization of Slow Moving Inventory System Using Cuckoo Search
T2 - Intelligent Automation \& Soft Computing
PY - 2018
VL - 24
IS - 2
SN - 2326-005X
AB - This paper focuses on the development of a multi-objective lot sizeâ€“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.
KW - MOCS; Slow moving; Lead time; Inventory; Laplace
DO - 10.1080/10798587.2017.1293891