An inventory problem in which annual demand is normally distributed with known means and standard deviations is considered. A purchase price increase is imminent before the next order is placed. Volume discounts are also given in accordance to the size of the order. A model to compute an optimal order quantity and an optimal delivery point is presented. This model can also account for any price change that may occur from time to time

Ali, M.M.

Jekot, T.

Masinga, L.C.

English

Mathematics in Industry Information Service Eprints Archive

OPTIMAL ORDER QUANTITIES WITH
VOLUME DISCOUNTS BEFORE AND AFTER
PRICE INCREASE
M.M. Ali∗, L.C. Masinga† and T. Jekot‡
Abstract
An inventory problem in which annual demand is normally distributed
with known means and standard deviations is considered. A purchase
price increase is imminent before the next order is placed. Volume
discounts are also given in accordance to the size of the order. A
model to compute an optimal order quantity and an optimal delivery
point is presented. This model can also account for any price change
that may occur from time to time.
1 Introduction
Models involving price increase have been discussed (Tersine (1976), Nad-
dor(1966), Huang and Kulkarni (2003) ). In these models constant annual
demand rate is assumed.
Tersine (1976) assumes no stock when the order needs to be made and
the special order coincides with the end of a cycle. Naddor (1966) on the
other hand assumes that the special order is a multiple of the economic
order quantity (EOQ) after the price increase. Huang and Kulkarni (2003)
discussed an infinite horizon model in which the special order size is not
necessarily a multiple of the new EOQ. In all these models, the approach
is to determine the special order size by minimising some cost difference
function of when the special order is made and when it is not. Naddor
∗School of Computational and Applied Mathematics, University of the Witwatersrand,
Private Bag 3, Wits 2050, South Africa. e-mail: mali@cam.wits.ac.za
†School of Computational and Applied Mathematics, University of the Witwatersrand,
Private Bag 3, Wits 2050, South Africa. e-mail: londiwe@cam.wits.ac.za
‡Super Group, Supply Chain Management, 24 Autumn Street, Rivonia 2128, South
Africa. e-mail: Tomasz.Jekot@supergroup.com
95
96 M.M. Ali, L.C. Masinga and T. Jekot
(1966) and Huang and Kulkarni (2003) achieve this by using a so called
Cesaro limit and conclude by finding a (s, S) type strategy.
The problem of volume discounts has also been discussed separately in
the literature (Taha (1992), Naddor (1966) and Winston (1997)). These
models are based on an assumption of no price increases and hence no con-
sideration of special order quantities. Algorithms for minimising total an-
nual costs have been proposed for use in such a case to determine an optimal
order quantity that takes advantage of the volume discounts.
Here we propose a model that can incorporate both volume discounts
and price changes. Annual demand is assumed to be normally distributed
with known mean and standard deviation. The order size and cycle length
between delivery points are also assumed to be variable. Central to this
model is that the demand rate in one cycle is not equal to that of the others.
This allows for such practical cases as seasonal demand, about which the
retailers would have an idea of how the stock can deplete in a particular
cycle. Shortages are allowed in any cycle but no backlogging is assumed.
The optimal order quantity is dependent on the price.
2 The model
2.1 Model assumptions
We assume a finite planning period of one year, during which a predeter-
mined number of orders are to be made.Annual demand is stochastic with
a known mean annual demand (D). The order quantity, qi, and the length,
Ti, of each cycle are variable. Order quantity q1 is placed at t = 0. For the
i-th cycle the retailer has a good knowledge of how the stock is depleted and
can assign a mean demand rate, which may vary from cycle to cycle. The
inventory level is stochastic with constant variance over a time cycle. Back-
logging is not allowed. Hence all shortages account for lost sales. On time
delivery of an order is assumed. Ordering cost, Oi, has two components: the
fixed ordering cost and variable ordering costs. The variable cost depends
on the quantity ordered, so that Oi = F + cqi. This is in addition to the
purchase cost, Pi = piqi, associated with the unit price of the stock. The
times when an order is delivered are allowed to vary within certain ranges;
i.e. ti ∈ [li, ui], i ≥ 1. The sum of the periodic order quantities equals annual
mean demand; i.e.
∑
qi = D.
Optimal Order Quantities 97
t
q
0 t1 t2 t3
q 1 q 2
q 3
T1 T2 T3
q = q1 − µ1t
q = c3 − µ3t
Figure 1: Operational structure (n = 3).
2.2 Description
Let the mean demand rate µi for each cycle be known. Then the inventory
level in each cycle will be determined by
q = ci − µit+ νi , (2.1)
where νi ∼ N(0, σ
2
i ) and ci = ci(qi, νi−1). Therefore the inventory
q ∼ N(ci − µit, σ
2
i ).
We normalise the demand rate so that if µi = 0, then νi = Ziσi.
We consider the case where σi is proportional to the order quantity, i.e.
σi = qi,  small. Thus νi = νi(qi) and in every reference thereto such
dependence shall be understood.
Figure 1 shows some of the features of the operation for a planning
horizon involving 3 orders/cycles. The initial order quantity, q1, is delivered
at t0 = 0. At any stage, the next delivery point occurs at ti = ti−1 +
qi
µi
,
i = 1, . . . , n − 1, with tn = 1 marking the end of the planning horizon and
hence no delivery at that point.
98 M.M. Ali, L.C. Masinga and T. Jekot
q
ti−1 ti
ti +
νi
µi
, νi > 0
q i
x
i−
1
Q
i
Ti
q = ci − µit
q = ci − µit+ νi
Figure 2: Interval Ti
A more detailed representation of a typical cycle i is shown in Figure 2.
Each cycle begins with a delivery of an order quantity qi, i = 1, 2, . . . , n,
which is depleted at a mean demand rate µi, i = 1, 2, . . . , n, over a period
Ti =
qi
µi
. The stock level during the i-th cycle is given by the stochastic
function q = q(t) = ci − µit+ νi.
Also, the admissible range for the delivery point is li ≤ ti ≤ ui, where li
and ui are respectively the lower and upper bounds for the variation.
The costs likely to be incurred in cycle Ti are:
Holding cost:
If νi−1 > 0, then there will be stock, xi−1, on hand when the next delivery
qi occurs. The holding cost is taken up to ti−1 and the rest is incorporated
into the next cycle. If we let Qi = xi−1 + qi be the total stock at the
beginning of the i-th cycle, then the average inventory during Ti is
Ii =
µi
qi + νi
∫ ti+ νiµi
ti−1
(Qi − µit+ νi) dt . (2.2)
Optimal Order Quantities 99
The holding costs incurred between ti−1 and ti = ti−1 +
qi
µi
are therefore
given by
Hi =
(
qi + νi
µi
)
hIi , (2.3)
where h is the cost of holding a unit in stock for a year. The last inventory
holding cost incurred if νn > 0 is
H ′n = h
∫ tn+ νnµn
tn
[Qn − µnt+ νn] dt . (2.4)
Shortage cost:
If νi < 0, then a shortage cost is incurred between ti+
νi
µi
and ti and is given
by
Si = s
∫ qi
µi
qi+νi
µi
(Qi − µit+ νi) dt , (2.5)
where s is the shortage cost per unit per year.
Ordering cost:
This is given by
Oi = F + cqi . (2.6)
Purchasing cost:
This is the cost of the stock given by
Pi = piqi . (2.7)
2.2.1 Total inventory cost:
The total cost in interval Ti is therefore
Ci =
{
Pi +Oi +Hi if νi > 0 ,
Pi +Oi +Hi + |Si| if νi < 0 .
(2.8)
100 M.M. Ali, L.C. Masinga and T. Jekot
2.3 Total annual cost
The main objective is to minimise the total annual cost,
C =
n∑
i=1
Ci = f(q1, q2, . . . , qn) .
The optimisation problem can be stated as
Minimise C = f(q1, q2, . . . , qn) (2.9)
subject to l1 ≤ ti ≤ ui, i = 1, 2, . . . , n (2.10)
q1 + q2 + . . . + qn = D (2.11)
where
t1 =
q1
µ1
, ti = ti−1 +
qi
µi
, i = 2, . . . , n .
The total annual costs depends on a number of cases. Let the total cost in
cycle Ti be
Ci = Pi +Oi +Hi, if Si = 0 , (2.12)
and
Csi = Pi + Ii +Hi + |Si|, if Si 6= 0 . (2.13)
For example the following total annual costs for n = 3 arise ( see Ali and
Masinga [2004] for details):
Case 1: ν1, ν2, ν3 < 0
C = Cs1 + C
s
2 + C
s
3 , (2.14)
Case 2: ν1, ν2 < 0, ν3 > 0
C = Cs1 + C
s
2 + C3 +H
′
3 , (2.15)
Case 3: ν1, ν3 > 0, ν2 < 0
C = C1 +C
s
2 + C3 +H
′
3 , (2.16)
Case 4: ν1, ν3 < 0, ν2 > 0
C = Cs1 +C2 + C
s
3 , (2.17)
Case 5: ν1, ν2 > 0, ν3 < 0
C = C1 + C2 + C
s
3 , (2.18)
Optimal Order Quantities 101
Run 1 Run 2 Run 3
Inputs
p1 50 50 90
p2 50 60 80
p3 50 70 70
Output
q1 269 979 267
q2 99 415 99
q3 1132 105 1133∑
qi 1500 1499 1499
Total costs 8850 9186 118835
t1 0.1571 0.5498 0.1506
t2 0.2013 0.7612 0.2007
t3 0.7403 0.8113 0.7406
Table 1: Results for three runs for a set of data.
Case 6: ν1 < 0, ν2, ν3 > 0
C = Cs1 + C2 +C3 +H
′
3 , (2.19)
Case 7: ν1 > 0, ν2, ν3 < 0
C = C1 + C
s
2 + C
s
3 , (2.20)
Case 8: ν1, ν2, ν3 > 0
C = C1 + C2 + C3 +H
′
3 . (2.21)
The model is elaborated further in Ali and Masinga (2004), where a
detailed breakdown of the computation is given.
3 Conclusions and recommendations
The model was tried for the case n = 3, using some data previously used for
other models for different values of the unit price in different intervals. Some
data taken from Yong-Wu (2004), with F = 500, h = 10, s = 15, c = 4
and µ1 = 1780, µ2 = 1970, µ3 = 2100, D = 1500 were used. The results
obtained for different runs are shown in Table 1.
The results show that the model adapts well to variations in the mean
periodic demand µi and the respective prices pi. For example, we can deduce
102 M.M. Ali, L.C. Masinga and T. Jekot
from Run 1, where the price is fixed, that the optimal strategy would be to
leave the highest order quantity to the last, which is in line with the fact
that the mean demand is highest in cycle three. In Run 2 mean demand
values are the same, and the price increases from cycle to cycle. The optimal
order quantities are q1 = 979, q2 = 415, q3 = 105. Thus when the price is
highest, the order quantity is lowest.
With such results, it seems possible that the model can be modified to
accommodate the case of quantity discounts. This and other such consider-
ations are presented in Ali and Masinga (2004).
Acknowledgement
M.M. Ali acknowledges support of this work under the National Research
Foundation of South Africa grant number 2053240.
References
Ali, M.M. and Masinga, L.C. (2004). A nonlinear optimisation model with
stochastic demand for optimal order quantities with price change. In prepa-
ration
Huang, W. and Kulkarni, V.G. (2003). Optimal EOQ for announced price
increases in infinite horizon. Operations Research 51, 336-339.
Naddor, E. (1966). Inventory Systems, John Wiley, New York.
Taha,H.A. (1992). Operations research: An Introduction, MacMillan Pub-
lishing Co., New York.
Taylor, S.G. and Bradley, C.E. (1985). Optimal ordering strategies for an-
nounced price increases. Operations Research, 33, 312-325.
Tersine, R.J. (1982). Principles of Inventory and Materials Management,
2nd ed., North Holland, New York.
Winston, W.L. (1997). Operations Research: Applications and Algorithms.
3rd ed., Pacific Grove, Duxbury.
Yong-Wu, Z., Hon-Shiang, L. and Shan-Lin, Y. (2004). A finite horizon
lot-sizing problem with time-varying deterministic demand and waiting-
time-dependent partial backlogging. International Journal of Production
Economics, 91, 109-119.


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