TY - JOUR

T1 - Development of a fuzzy economic order quantity model of deteriorating items with promotional effort and learning in fuzziness with a finite time horizon

AU - Mahapatra, Amalendu Singha

AU - Sarkar, Biswajit

AU - Mahapatra, Maheswar Singha

AU - Soni, Hardik N.

AU - Mazumder, Sanat Kumar

N1 - Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2019/9

Y1 - 2019/9

N2 - This study investigates an economic order quantity model of deteriorating items, where demand is fuzzy in nature and depends on promotional effort with full backorder for a given time horizon. The learning effect in the fuzzy environment is added in this model. A constant deterioration rate is assumed. Under these circumstances, a mathematical model is developed to curtail the total cost over a finite time horizon by determining the replenishment order quantity, number of replenishments, and the fraction of the replenishment cycle when inventory is positive. A solution algorithm is developed to find the optimal solutions. The applicability of the proposed model is illustrated through numerical examples. To get further insights, sensitivity analysis is carried out for the main parameters in crisp, fuzzy, and fuzzy-learning environments.

AB - This study investigates an economic order quantity model of deteriorating items, where demand is fuzzy in nature and depends on promotional effort with full backorder for a given time horizon. The learning effect in the fuzzy environment is added in this model. A constant deterioration rate is assumed. Under these circumstances, a mathematical model is developed to curtail the total cost over a finite time horizon by determining the replenishment order quantity, number of replenishments, and the fraction of the replenishment cycle when inventory is positive. A solution algorithm is developed to find the optimal solutions. The applicability of the proposed model is illustrated through numerical examples. To get further insights, sensitivity analysis is carried out for the main parameters in crisp, fuzzy, and fuzzy-learning environments.

UR - http://www.scopus.com/inward/record.url?scp=85073366209&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073366209&partnerID=8YFLogxK

U2 - 10.3390/inventions4030036

DO - 10.3390/inventions4030036

M3 - Article

AN - SCOPUS:85073366209

SN - 2411-5134

VL - 4

JO - Inventions

JF - Inventions

IS - 3

M1 - 36

ER -