Simulation of Economic Production Quantity Model for Deteriorating Item

Uttam Kumar Khedlekar, Diwakar Shukla

  Open Access OPEN ACCESS  Peer Reviewed PEER-REVIEWED

Simulation of Economic Production Quantity Model for Deteriorating Item

Uttam Kumar Khedlekar1,, Diwakar Shukla1

1Department of Mathematics and Statistics Dr. Hari Singh Gour University Sagar, M. P., India


A manufacturing business may be affected due to disruption produced either in production system or in raw material supply. The disruptions in production system occur due to appearance of uncertainty and unplanned events. For example, skilled labor problem, machine failure problem, system repair problem etc. Inventory managers need to monitor the raw material supply and anticipate the shortage before the system gets disrupted. The deterioration among produced items is also a worry factor for managers. In this paper, we suggest a production inventory model subject to condition of occurrence of disruption at input level and deterioration at output level. A comparative approach has been adopted between models with and without disruption. The main focus is on specific type disruption like sudden reduction in supply of raw material. Its effect has been examined in comparative approach. Parametric simulation is used to generate graphs and useful suggestions are made for inventory managers.

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Cite this article:

  • Khedlekar, Uttam Kumar, and Diwakar Shukla. "Simulation of Economic Production Quantity Model for Deteriorating Item." American Journal of Modeling and Optimization 1.3 (2013): 25-30.
  • Khedlekar, U. K. , & Shukla, D. (2013). Simulation of Economic Production Quantity Model for Deteriorating Item. American Journal of Modeling and Optimization, 1(3), 25-30.
  • Khedlekar, Uttam Kumar, and Diwakar Shukla. "Simulation of Economic Production Quantity Model for Deteriorating Item." American Journal of Modeling and Optimization 1, no. 3 (2013): 25-30.

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1. Introduction

There are many reasons that a production system gets disrupted like machine breakdown, unexpected events or emergency crises. An oil-drilling company may be disrupted due to failure in electricity supply, failure of drilling machines whereas an oil refining company faces problems of uneven crude oil supply, non-availability of other raw materials or earthquake and strike etc. It is assumed that a process is in control at the beginning of a production run. Suddenly the supply of raw material reduction by a constant factor, in the whole time cycle, production rate reduced. The modified production run ends at the same time as was before disruption. The change in optimal parameters before and after disruption has been examined in this paper.

Lin and Kroll (2006) one pioneer to solve the production problem under an imperfect production system subject to random breakdowns. Liao (2007) established an EPQ model by giving permission to delay in payment for the buyer to manufacturers.

A single vendor and multi-buyer inventory policy for a deteriorating item was due to Yang and Wee (2002). Teng and Chang (2005) presented an economic production quantity model for deteriorating items when the demand rate depends on not only on-display stock, but also on the selling price per unit of an item which may influence by economic policy, political scenario or agriculture productivity or both get affected. A similar approach has been followed by Hou and Lin (2006) on the deterministic economic order quantity model by taking into account the inflation and the time value of money for the deteriorating items with price and stock-dependent selling rate. By dividing the demand rate into multiple segments, Shukla and Khedlekar (2010a) have introduced three-component demand rate for the newly launched deteriorating item.

Joglekar (2003) used a linear demand function with price sensitiveness and allowed retailers to use a continuous increasing price strategy in an inventory cycle. He derived the retailer’s optimal profit by ignoring all the inventory costs. His results are restricted to growing market only, neither for stable market nor for a declining market. Expenditure sources like ordering cost, safety features, lead time and numbers of lots are the integral parts of decision making.

A number of structural properties of the inventory system are studied analytically by Samanta and Roy (2004) by determination of production cycle time and backlog for deteriorating item, which follows an exponential distribution. Qi et al. (2004) analyzed the supply chain-coordination with demand disruption in a deterministic scenario. Giri et al. (1996) who computed the optimal policy of an EOQ model with dynamic costs. The model they proposed is very basic though, since they have considered the very special case where the holding and ordering costs are linear functions of time. The other shortcoming of that paper is that the deterioration rate is also a linear function of time, and the algorithm they proposed in order to solve the problem is only valid as long as the demand rate is a linear function of time.

A central policy presented by Benjaafar EIHafsi (2006) specify a single product assemble-to-order system for my components, an end–product to serve and customer classes and problem solved as a Markov decision process and characterize the structure of an optimal policy.

We refer some useful contribution to reader Shukla and Khedlekar (2012), Lo (2007) and Khedlekar and Shukla (2013), Shukla et al. (2009, 2010b, 2010c). He et al. (2010) obtained optimal production time to facilitate the manufacturer sell the item in multiple markets by considering constant demand rate, but they do not readjust the production system. Due to above contribution we incorporate the deteriorating factor with constant demand and adjust the disrupted production system with shortages and when it occurs an optimal time of placing an order is obtained along with order quantity from the spot market.

2. Model Description

Suppose a deteriorating item, manufactured by a manufacturer, sold to customers. It has demand rate production rate p>µ in each cycle. Since p - µ>0 so inventory accumulates at manufacture’s level and stops production at time Tp due to excess stock of that item. Now inventory reduces constantly until entire stuck vanishes at time H (Tp<H), it is normal phenomenon for any production cum storage system.

Now assume that disruption occurs in this process at time Td (Td < Tp) due to assignable causes (like strike, lake of raw material) and due to random causes (like failure of power, fatique) etc. We consider the disruption in production as sudden cut off in the supply of raw material by a fixed amount say. This reduces p into p+ ( may be positive or negative) at time Td.

Now this cut off remain maintained constantly throughout the production cycle time H. the inventory curve (Figure 2) bears a shift in shape due to this region of disruption. The time of maximum inventory shifts to (>Tp) and maximum level of inventory reduces accordingly so as to finish up the production cycle at same time H (or H). This disruption may minor or major. In case of minor level, the inventory vanishes at time H, but in case of major level, the stock condition (or supply) may be lesser to the demand rate µ and so the inventory reduces to zero before the time H.

3. Production System Without Disruption

To compare the model output first, management optimizes the production system run without disruption with production rate p (per unit time) stopped at production time Tp and there after till time H, inventory depicted due to demand rate µ and deterioration rate θ of items (see Figure 1). The presentations in differential equations for two periods [0, Tp] and [Tp, H] satisfy throughout its domain.

Figure 1. Production System without disruption

,boundary condition


,boundary condition


On solving Equation (1) and Equation (2) with boundary conditions we get


As per Figure 1 inventory level I1(t) and I2(t) are equal at time Tp.

i.e. I1(Tp) = I2(Tp) yields


If θ <<1, then


Proposition 1. If θ <<1 then Tp is in increasing in θ.

By Equation (6) one can write


This proved the result*

As θ increases optimal production time Tp increases that is more product required to producing. One can conclude that to keep low deterioration is effective way to keep lower cost of production of items.

4. Production System with Disruption

As previous section production rate remains unchanged but in practice production system is always disruption due to unplanned and thus we consider the production system little changed by and disruption time is Td . If, then production rate decreases and, if then production rate increases.

Proposition 2.

then manufacturer still satisfies the demand even production system has been disrupted, otherwise If then production system unable to satisfy demand that is there will be shortages due to production disruption.

Proof: Suppose the production system disrupted at time Td as (see Figure 2) and there after the production rate will be thus presentations of two differential equations for intervals [0, Td] and [Td, H] are


with boundary condition

On solving Equation (9) with boundary condition we get


If this means production system satisfy the demand of items.

That isthen still satisfy the demand.

If this means production system does not satisfy the demand of items.

That is then there will be shortages in the system.

This proved the lemma*

Again if then we find optimal production time (with disruption) such that at time H entire stock will be sold-out and inventory level will be zero.

If there will be shortages in the system and in this situation we will find the optimum time Tr of placing the order and respective order quantity Qr.

Proposition 3. If then production time with disruption is obtained by.

Proof: If

or that is on hand inventory is .

Therefore we will find out the optimal time(see Figure 2) when we stopped the production after disruption in such a manner that stock remains zero at time H. the presentations of two differential equations for intervals [Td ,Tpd] and [Tpd, H] are


Boundary condition

, boundary condition


On solving (11) with boundary condition we get


Using condition



By Equation (10),


and thus by Equation (14), order quantity will be


This proved the result*

4.1. An Application with Simulation

For application we assumed a particular case when p = 350, µ = 300, Δp = -100, θ = 0.01, H = 15 and Td =1, on applying then we get I2(H)>0 and thus by Equation (6) and Equation (15) gives Tp=0.14, = 13.71 days. We simulate the application on same data in which other parameters are invariant.

Figure 6. (Comparison between and Tp with and without disruption w.r.t. θ)

Table 1. (Variations in parameters)

4.2. Discussion

Time horizon H linearly increases the time Tp and both (see Table 1). Production timeafter disruption is linearly decreases as disruption Td decreases (see Figure 3) that is system get disrupted later is in favor of management. Also the production time is not longer if it gets disrupted later. If deterioration increase thenis in linearly increases (see Figure 4), the same result followed by Tp with respect to deteriorations (see Figure 5). There is a shift of stop time of production before and after disruption (see Figure 6), and both are highly sensitive on deterioration.

5. Conclusion

The deterioration factor affects negatively when disruption is present in the production system. In beginning, if production rate is higher than demand rate, then inventory managers are benefitted. It provides a little accumulation of stock to the managers. But, due to specific type of disruption, the reserve inventory helps to manage the market only up to a shorter period. It is interesting fact that when disruption appears its early occurrence may be dangerous but later occurrence is manageable.

Production model with disruption is quite different from the production model without disruption. The stop time for production after disruption is always greater than the stop time of production time without disruptions. If system get disrupted later it goes to favour of production system so, management should delay the disruptions as well as possible.

If production disrupted time is longer, then it is difficult to manage and it need to order more quantity from the spot market. The demand parameter highly affected the policy. Thus, the performance of any production system will be robust to demand variations and model uncertainty. The proposed model can be further extended by considering the more realistic assumption like time dependent production along with time dependent demand even production system gets disrupted, and deterioration may follow a probability distribution function.


We are thankful to referee for his very helpful comments and suggestions for the overall improvement of the paper.


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