A FLUID EOQ MODEL WITH A TWO-STATE RANDOM ENVIRONMENT

2006 ◽  
Vol 20 (2) ◽  
pp. 329-349 ◽  
Author(s):  
Oded Berman ◽  
David Perry ◽  
Wolfgang Stadje
Keyword(s):  
Random Environment ◽  
Classical Problem ◽  
Type Model ◽  
Expected Number ◽  
Setup Cost ◽  
Eoq Model ◽  
Demand Rate ◽  
Markovian Random Environment ◽  
Holding Cost ◽  
Stochastic Fluid

We study a stochastic fluid EOQ-type model operating in a Markovian random environment of alternating good and bad periods determining the demand rate. We deal with the classical problem of “when to place an order” and “how big it should be,” leading to the trade-off between the setup cost and the holding cost. The key functionals are the steady-state mean of the content level, the expected cycle length (which is the time between two large orders), and the expected number of orders in a cycle. These performance measures are derived in closed form by using the level crossing approach in an intricate way. We also present numerical examples and carry out a sensitivity analysis.

Development of an EOQ Model for Single Source to Multi Destination

2012 ◽  
Vol 3 (4) ◽  
pp. 51-70
Author(s):  
Kanika Gandhi ◽  
P. C. Jha ◽  
M. Mathirajan
Keyword(s):  
Real Life ◽  
Fuzzy Optimization ◽  
Single Source ◽  
Short Life ◽  
Eoq Model ◽  
Industry Environment ◽  
Constant Quantity ◽  
Demand Rate ◽  
Holding Cost

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.

scholarly journals Inventory Model with Demand Dependent on Unit Price under Fuzzy Parameters and Decision Variables

2019 ◽  
Vol 8 (3) ◽  
pp. 784-788
Keyword(s):  
Numerical Solutions ◽  
Unit Cost ◽  
Optimal Solution ◽  
Unit Price ◽  
Lot Size ◽  
Setup Cost ◽  
Eoq Model ◽  
Decision Variables ◽  
Holding Cost ◽  
Cost Parameters

An EOQ model with demand dependent on unit price is considered and a new approach of finding optimal demand value is done from the optimal unit cost price after defuzzification. Here the cost parameters like setup cost, holding cost and shortage cost and also the decision variables like unit price, lot size and the maximum inventory are taken under fuzzy environment. Triangular fuzzy numbers are used to fuzzify these input parameters and unknown variables. For the proposed model an optimal solution has been determined using Karush Kuhn-Tucker conditions method. Graded Mean Integration (GMI) method is used for defuzzification. Numerical solutions are obtained and sensitivity analysis is done for the chosen model

scholarly journals An EOQ Model for Phase Inventory with Induced Demand and Periodic Cycle Time

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Sujit Kumar De ◽  
Shib Sankar Sana ◽  
Adrijit Goswami
Keyword(s):  
Cycle Time ◽  
Optimal Order ◽  
Inventory Problem ◽  
Setup Cost ◽  
Inventory Cost ◽  
Induced Demand ◽  
Eoq Model ◽  
Demand Rate ◽  
Second Shift ◽  
Stock Flow

This paper deals with a stock flow of an inventory problem over induced demand. The inventory is consumed through “core customer” or chain marketing system in an induced environment (inductance) to exhaust all the items of the stock inventory in an indefinite time. The demand rate is depicted due to induced factor which is generated from the same inventory presented nearby. The inventory cycle time is split into several periodic times due to oscillatory feature of the inventory which is called phase inventory. Considering uniform demand, this cycle time splits into two basic parts, namely, “first shift” (phase) and “second shift” (phase). Since the process dampens over time, so the whole inventory will exhaust after few periods. A cost function consisted of inventory cost, setup cost, and loss for induced items is minimized to obtain optimal order quantity and replenishment time. The multivariate lagrange interpolation (MLI) over the average values of the postsensitivity analysis is developed here. Finally, graphical illustrations are made to justify the model.

scholarly journals A Revision on Cost Elements of the EOQ Model

2016 ◽  
Vol 11 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Mehdi Rajabi Asadabadi
Keyword(s):  
Capital Cost ◽  
Decision Makers ◽  
Economic Order Quantity ◽  
New Order ◽  
Order Quantity ◽  
Setup Cost ◽  
Total Cost ◽  
Eoq Model ◽  
Proposed Model ◽  
Holding Cost

AbstractThe overall objective of this paper is to investigate the fundamental cost elements of the traditional EOQ model and develop the model by expiring some of its unrealistic assumptions. Over the last few decades, there have been numerous studies developing the EOQ model, but the basic cost elements of the EOQ model have not been investigated efficiently. On the other hand, the capital cost of buying inventories seems to be important to be investigated separately as well as holding cost and ordering cost in the model. In this paper, the capital cost of the inventory and possible stepwise increases in holding and setup cost are taken into account to make a revised formula to compute the economic order quantity. The proposed model involves explicitly the capital cost of buying the inventories in the EOQ model to ensure the decision makers that their financial concerns are considered in the revised model and the new order quantity results the minimum total cost.

EOQ Model with Time Dependent Demand Rate and Time Dependent Holding Cost Function

2013 ◽  
pp. 381-392
Author(s):  
R. P. Tripathi
Keyword(s):  
Infinite Horizon ◽  
Decision Rules ◽  
Time Dependent ◽  
Single Item ◽  
Eoq Model ◽  
Optimal Policies ◽  
Demand Rate ◽  
Holding Cost ◽  
Dependent Demand Rate ◽  
Dependent Demand

In this paper the authors consider the continuous deterministic, infinite horizon, single item inventory system within the setting of a retailer sector in which the demand rate for an item is time dependent. The parameter of the replenishment cost is kept constant, but the carrying cost per unit is allowed to vary. The optimal policies are found, and decision rules and classical EOQ model have been obtained by considering two different models. Numerical examples are given to illustrate the proposed models.

AN EOQ MODEL WITH GENERALIZED RAMP-TYPE DEMAND AND WEIBULL DISTRIBUTION DETERIORATION

2007 ◽  
Vol 24 (01) ◽  
pp. 93-109 ◽  
Author(s):  
S. PANDA ◽  
S. SAHA ◽  
M. BASU
Keyword(s):  
Weibull Distribution ◽  
Time Horizon ◽  
Infinite Time ◽  
Setup Cost ◽  
Total Cost ◽  
Eoq Model ◽  
Replenishment Policy ◽  
Holding Cost ◽  
Ramp Type Demand ◽  
Weibull Distribution Deterioration

An inventory model is discussed with generalized ramp-type demand where the time to deterioration follows Weibull distribution. Shortages of inventories are allowed and completely backlogged. Total cost is derived by trading off setup cost, holding cost, deterioration cost, and shortage cost. The optimal replenishment policy for a single period is derived by minimizing the total cost per unit time over infinite time horizon. A numerical example is presented and sensitivity analysis is also carried out. The rationale for generalized ramp-type demand is discussed.

scholarly journals Inventory Level Improvement in Pharmacy Company Using Probabilistic EOQ Model and Two Echelon Inventory: A Case Study

2020 ◽  
Vol 13 (3) ◽  
pp. 229-242
Author(s):  
Desy Anisya Farmaciawaty ◽  
◽  
Mursyid Hasan Basri ◽  
Akbar Adhi Utama ◽  
Fransisca Budyanto Widjaja ◽  
...  
Keyword(s):  
Inventory Management ◽  
Lead Time ◽  
Inventory Level ◽  
Setup Cost ◽  
Inventory Cost ◽  
Decentralized System ◽  
Class A ◽  
Eoq Model ◽  
Holding Cost

Abstract. This research is aimed to maintain the inventory level in a two-echelon pharmacy company. The company is a pharmacy company that has 16 branches that operate in Bandung and the surrounding area. The company has a problem with its high inventory cost. To solve the problem, the authors compare two methods that suit the company condition, i.e., the decentralized system using probabilistic EOQ model and the centralization system using the multi-echelon inventory technique. We analyzed sales data and on-hand inventory data acquired from the company information system to perform the study. We limit the scope to the class A items only. We also assume the lead time, setup cost, and holding cost used in this study with the company's owner's consent. To conclude, using the decentralized system, the company will save 31% of their inventory cost, while using the centralization system with the multi-echelon technique, the company will be able to save 61% of their inventory cost. We recommend the company to refer to its competitive strategy before deciding which model it would be implemented. Keywords: Centralization, Decentralization, Probabilistic Economic Order Quantity (EOQ), Multi-Echelon Inventory, Pharmaceutical Inventory Management

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