Development of an EOQ Model for Single Source to Multi Destination: Multi Deteriorating Products under Fuzzy Environment

Development of an EOQ Model for Single Source to Multi Destination: Multi Deteriorating Products under Fuzzy Environment

Kanika Gandhi (Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi, Delhi, India), P. C. Jha (Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi, Delhi, India) and M. Mathirajan (Department of Management Studies, Faculty of Engineering, Indian Institute of Science Bangalore, Bangalore, India)
Copyright: © 2012 |Pages: 20
DOI: 10.4018/jaec.2012100104
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Industry environment has become competitive because of product’s short life cycle. Competition reaches to extreme, when products are deteriorating which further makes demand uncertain. Generally, in deriving the solution of economic order quantity (EOQ) inventory model, the authors consider the demand rate as constant quantity. But in real life, demand cannot be forecasted precisely which causes fuzziness in related constraints and cost functions. Managing inventory, procurement, and transportation of deteriorating natured products with fuzzy demand, and holding cost at source and destination becomes very crucial in supply chain management (SCM). The objective of the current research is to develop a fuzzy optimization model for minimizing cost of holding, procurement, and transportation of goods from single source point to multi demand points with discount policies at the time of ordering and transporting goods in bulk quantity. A real life case study is produced to validate the model.
Article Preview

Review Of Literature

Transportation costs may be influenced by decisions regarding choice of model, size and type of shipments. In fact, transportation decisions may influence inventory decisions. Inventory management and transportation policy interact, particularly, “when alternatives exist for transporting replacement inventory from a vendor or a plant, and each alternative necessitates different parameters for the management of inventories” (Constable & Whybark, 1978). Russell and Krajewski (1991) presented a simple analytical approach for finding the order quantity that minimizes total purchase costs which reflects both transportation economies and quantity discounts. Chung and Tsai (2001) derive an inventory model for deteriorating items with the demand of linear trend and shortages during the finite planning horizon considering the time value of money. It is also seen that the demand of a consumer product usually varies with time and hence, the demand rate should be taken as time-dependent. Weiguo and Xue (2011) establishes inventory control model of deteriorating items based on time under the VMI mode, it introduced the fuzzy membership function of the decay rate based on the model in the past. Hou, Huang, and Lin (2011) presents an inventory model for deteriorating items with stock-dependent selling rate under inflation and time value of money over a finite planning horizon. In the model, shortages are allowed and the unsatisfied demand is partially backlogged at the exponential rate with respect to the waiting time. Tu, Lo, and Yang (2011) develops a two-echelon inventory model with mutual beneficial pricing strategy with considering fuzzy annual demand; single vendor and multiple buyers in this model. This pricing strategy can benefit the vendor more than multiple buyers in the integrated system, when price reduction is incorporated to entice the buyers to accept the minimum total cost. Xu and Zhai (2008) develop an optimal technique for dealing with the fuzziness aspect of demand uncertainties. Triangular fuzzy numbers are used to model external demand and decision models in both non-coordination and coordination situations are constructed.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 8: 4 Issues (2017): 3 Released, 1 Forthcoming
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing