Abstract:In todays global market every body want to buy products of high level quality and to achieve a high level product quality supplier have to invest in improving reliability of production process. In present article we have studies reliable production process with stock dependent unit production and holding cost. Demand is exponential function of time and infinite production process with non-instantaneous deterioration rate are considered in this paper. Whole study has been done under the effect of trade credit. … Show more
“…Dash (2014) developed a deteriorated EPQ policy for decayed quadratic demand with time value of money and shortages. Osagiede and Osagiede (2007), Mohan and Venkateswarlu (2013), Santosh (2013), Ravish and Amit (2014), Singh and Rathore (2014), Umakanta (2016), , Malik et al (2017), Sahoo and Tripathy (2018), Tripathi and Tomar (2018), Palanivel and Gowri (2018) and similarly worked with inventory model with quadratic demand. Leung (2007) established a more general results by means of arithmetic-geometric mean inequality in which a general power function is projected to model the connection between production set-up cost and quality assurance.…”
In this paper, an EPQ model for items that exhibit delay in deterioration is developed. It is assumed that there is no demand and no deterioration during production buildup period. Demand starts immediately after production but no deterioration. Then a period of deterioration sets in until the stock finishes. It is also supposed that the cost of a unit product is inversely related to the rate of demand and directly related to the process reliability (as assumed by Tripathy et al. (2015) and modified by Dari and Sani (2015)). The demand before deterioration sets in is quadratic time dependent while demand after deterioration sets in is a constant. Shortages are allowed and partially backordered. A numerical model is given to compare the simulation model and the statistical analysis conducted on the model to see the effect of measurement changes in other system parameters.
“…Dash (2014) developed a deteriorated EPQ policy for decayed quadratic demand with time value of money and shortages. Osagiede and Osagiede (2007), Mohan and Venkateswarlu (2013), Santosh (2013), Ravish and Amit (2014), Singh and Rathore (2014), Umakanta (2016), , Malik et al (2017), Sahoo and Tripathy (2018), Tripathi and Tomar (2018), Palanivel and Gowri (2018) and similarly worked with inventory model with quadratic demand. Leung (2007) established a more general results by means of arithmetic-geometric mean inequality in which a general power function is projected to model the connection between production set-up cost and quality assurance.…”
In this paper, an EPQ model for items that exhibit delay in deterioration is developed. It is assumed that there is no demand and no deterioration during production buildup period. Demand starts immediately after production but no deterioration. Then a period of deterioration sets in until the stock finishes. It is also supposed that the cost of a unit product is inversely related to the rate of demand and directly related to the process reliability (as assumed by Tripathy et al. (2015) and modified by Dari and Sani (2015)). The demand before deterioration sets in is quadratic time dependent while demand after deterioration sets in is a constant. Shortages are allowed and partially backordered. A numerical model is given to compare the simulation model and the statistical analysis conducted on the model to see the effect of measurement changes in other system parameters.
“…Jaggi et al introduced a fuzzy inventory model for deteriorating items with time varying demand and shortages [8]. Singh and Rathore studied an inventory model for deteriorating items with reliability consideration and trade credit [7].…”
In real life situation, demand may be increase, decrease or constant. But, in our present study demand is assumed to be an increasing function of time which depends on reliability. Shortages are allowed and excess demand is backlogged. The economic production lot size and the reliability of the production process along with the production period are the decision variables and total cost per cycle is the objective function which is to be minimized. Further the parameters involved in the business may likely to be changed due to the fast growing marketing system. Therefore; it will be more realistic and market friendly to deal with a fuzzy model rather than a crisp model. Both crisp and fuzzy models have been proposed to determine the optimal solution. The demand, shortage cost, holding cost and deterioration rate and reliability are considered as pentagonal fuzzy numbers. Defuzzification of the total cost has been carried out by Graded Mean Representation Method and Signed Distance Method. Sensitivity analysis is also incorporated to investigate the effect of different system parameters in enhancing the cost.
“…also introduced an inventory model for deteriorating item with space constraints. Singh and Rathore (2014) developed an inventory model for deteriorating item with reliability consideration and trade credit. Jaggi et al (2015) come forward with two-warehouse inventory model for deteriorating items with price sensitive demand and partially backlogged shortages under inflationary environment conditions.…”
The objective of this study is to develop of an inventory policy for deteriorating items, in which demand for the products is stock dependent and the retailer invests in preservation technology to reduce the rate of product deterioration. In many real-life situations, for certain types of consumer goods, the consumption rate is sometimes influenced by the stock-level. It is usually observed that a large pile of goods on a shelf in a supermarket will lead the customer to buy more and then generate higher demand. The consumption rate may go up or down with the onhand stock level. This paper is developed with the realistic conditions of demand, allowable credit period, partial backlogging and variable ordering cost. A solution procedure is given to find the optimal preservation technology cost and total cost of the system. A numerical example and sensitivity analysis are presented to illustrate the model.
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