Open access peer-reviewed chapter

Density Estimation in Inventory Control Systems under a Discounted Optimality Criterion

By Jesús Adolfo Minjárez-Sosa

Submitted: October 1st 2018Reviewed: July 4th 2019Published: August 7th 2019

DOI: 10.5772/intechopen.88392

Downloaded: 300


This chapter deals with a class of discrete-time inventory control systems where the demand process D t is formed by independent and identically distributed random variables with unknown density. Our objective is to introduce a suitable density estimation method which, combined with optimal control schemes, defines a procedure to construct optimal policies under a discounted optimality criterion.


  • discounted optimality
  • density estimation
  • inventory systems
  • optimal policies
  • Markov decision processes
  • AMS 2010 subject classifications: 93E20
  • 62G07
  • 90B05

1. Introduction

Inventory systems are one of the most studied sequential decision problems in the fields of operation research and operation management. Its origin lies in the problem of determining how much inventory of a certain product should be kept in existence to meet the demand of buyers, at a cost as low as possible. Specifically, the question is: How much should be ordered, or produced, to satisfy the demand that will be presented during a certain period? Clearly, the behavior of the inventory over time depends on the ordered quantities and the demand of the product in successive periods. Indeed, let Itand qtbe the inventory level and the order quantity at the beginning of period t,respectively, and Dtbe the random demand during period t.Then Itt0is a stochastic process whose evolution in time is given as


Schematically, this process is illustrated in the following figure.

(Standard inventory system)

In this case, the inventory manager (IM) observes the inventory level Itand then selects the order quantity qtas a function of It.The order quantity process causes costs in the operation of the inventory system. For instance, if the quantity ordered is relatively small, then the items are very likely to be sold out, but there will be unmet demand. In this case the holding cost is reduced, but there is a significant cost due to shortage. Otherwise, if the size of the order is large, there is a risk of having surpluses with a high holding cost. These facts give rise to a stochastic optimization problem, which can be modeled as a Markov decision process (MDP). That is, the inventory system can be analyzed as a stochastic optimal control problem whose objective is to find the optimal ordering policy that minimizes a total expected cost.

The analysis of the control problem associated to inventory systems has been done under several scenarios: discrete-time and continuous-time systems with finite or infinite capacity, inventory systems considering bounded and unbounded one-stage cost, as well as partially observable models, among others (see, e.g., [1, 2, 3, 4, 5, 7]). Moreover, such scenarios have their own methods and techniques to solve the corresponding control problem. However, in most cases, it has been assumed that all the components that define the behavior of the inventory system are known to the IM, which, in certain situations, can be too strong and unrealistic. Hence it is necessary to implement schemes that allow learning or collecting information about the unknown components during the evolution of the system to choose a decision with as much information as possible.

In this chapter we study a class of inventory control systems where the density of the demand is unknown by the IM. In this sense, our objective is to propose a procedure that combines density estimation methods and control schemes to construct optimal policies under a total expected discounted cost criterion. The estimation and control procedure is illustrated in the following figure:

(Estimation and control procedure)

In this case, unlike the standard inventory system, before choosing the order quantity qt, the IM implements a density estimation method to get an estimate ρt,and, possibly, combines this with the history of the system ht=I0q0D0It1qt1Dt1Itto select qt=qthtρt.Specifically, the density of the demand is estimated by the projection of an arbitrary estimator on an appropriate set, and its convergence is stated with respect to a norm which depends on the components of the inventory control model.

In general terms, our approach consists in to show that the inventory system can be studied under the weighted-norm approach, widely studied by several authors in the field of Markov decision processes (see, e.g., [11] and references therein) and in adaptive control (see, e.g. [9, 12, 13, 14]). That is, we prove the existence of a weighted function Wwhich imposes a growth condition on the cost functions. Then, applying the dynamic programming algorithm, the density estimation method is adapted to such a condition to define an estimation and control procedure for the construction of optimal policies.

The chapter is organized as follows. In Section 2 we describe the inventory model and define the corresponding optimal control problem. In Section 3 we introduce the dynamic programming approach under the true density. Next, in Section 4 we present the density estimation method which will be used to state, in Section 5, an estimation and control procedure for the construction of optimal policies. The proofs of the main results are given in Section 6. Finally, in Section 7, we present some concluding remarks.


2. The inventory model

We consider an inventory system evolving according to the difference equation


where Itand qtare the inventory level and the order quantity at the beginning of period t,taking values in I0and Q0, respectively, and Dtrepresents the random demand during period t.We assume that Dtis an observable sequence of nonnegative independent and identically distributed (i.i.d.) random variables with a common density ρL10which is unknown by the inventory manager. In addition, we assume finite expectation


Moreover, there exists a measurable function ρ¯L10such that


almost everywhere with respect to the Lebesgue measure. In addition


For example, if ρ¯sKmin11/s1+r,s[0,),for some positive constants Kand r,then there are plenty of densities that satisfy (3)(4).

The one-stage cost function is defined as


where h,c,and bare, respectively, the holding cost per unit, the ordering cost per unit, and the shortage cost per unit, satisfying b>c.

The order quantities applied by the IM are selected according to rules known as ordering control policies defined as follows. Let Htbe the space of histories of the inventory system up to time t.That is, a typical element of Htis written as


An ordering policy (or simply a policy) γ=γtis a sequence of measurable functions γt:HtQ,such that γtht=qt,t0. We denote by Γthe set of all policies. A feedback policy or Markov policy is a sequence γ=gtof functions gt:IQ,such that gtIt=qt.A feedback policy γ=gtis stationary if there exists a function g:IQsuch that gt=gfor all t0.

When using a policy γΓ,given the initial inventory level I0=I, we define the total expected discounted cost as


where α01is the so-called discount factor. The inventory control problem is then to find an optimal feedback policy γsuch that VγI=VIfor all II, where


is the optimal discounted cost, which we call value function.

We define the mean one-stage cost as


Then, by using properties of conditional expectation, we can rewrite the total expected discounted cost (6) as


where EIγdenotes the expectation operator with respect to the probability PIγinduced by the policy γ,given the initial inventory level I0=I(see, e.g., [8, 10]).

The sequence of events in our model is as follows. Since the density ρis unknown, the one-stage cost (8) is also unknown by the IM. Then if at stage tthe inventory level is It=II,the IM implements a suitable density estimation method to get an estimate ρtof ρ.Next, he/she combines this with the history of the system to select an order quantity qt=q=γtρthtQ.Then a cost cIqis incurred, and the system moves to a new inventory level It+1=I'Iaccording to the transition law


where 1B.denotes the indicator function of the set BBI,and BIis the Borel σalgebra on I. Once the transition to the inventory level I'occurs, the process is repeated. Furthermore, the costs are accumulated according to the discounted cost criterion (9).

3. Dynamic programming equation under the true density ρ

The study of the inventory control problem will be done by means of the well-known dynamic programming (DP) approach, which we now introduce in terms of the unknown density ρ.In order to establish precisely the ideas, we first present some preliminary and useful facts.

The set of order quantities in which we can find the optimal ordering policy should be Q=0QQ,



Thus, we can restrict the range of qso that qQ.Specifically we have the following result.

Lemma 3.1Letγ0Γbe the policy defined asγ0=00, and letγ¯=γ¯tbe a policy such thatγ¯khk=q¯k>Q,for at least ak=0,1,.Then


That is,γ0is a better solution thanγ¯.

Proof.Let It0,t=0,1,,be the inventory levels generated by the application of γ0,and I¯tq¯tbe the sequence of inventory levels and order quantities generated by γ¯,where I00=I¯0=I,It+10=It0Dt+, and I¯t+1=I¯t+q¯tDt+,t0.Without loss of generality, we suppose that for a q¯>Qwe have q¯0=q¯.Note that It0I¯t,for all t0.Then observing that cq¯>bD¯/1α,


Remark 3.2Observe that forIqI×Qwe have


where, by writingy=I+q,


In addition, observe that for any fixeds[0,),the functionsyys+andysy+are convex, which implies thatLyis convex. Moreover


The following lemma provides a growth property of the one-stage cost function (8).

Lemma 3.3There exist a numberβand a functionW:I[1,)such that0<αβ<1,


and for allIqI×Q


In addition, for any densityμon0such that0s<,


The proof of Lemma 3.3 is given in Section 6.

We denote by BWthe normed linear space of all measurable functions u:Iwith finite weighted-norm (Wnorm) Wdefined as


Essentially, Lemma 3.3 proves that the inventory system (1) falls within of the weighted-norm approach used to study general Markov decision processes (see, e.g., [11]). Hence, we can formulate, on the space BW, important results as existence of solutions of the DP-equation, convergence of the value iteration algorithm, as well as existence of optimal policies, in the context of the inventory system (1). Indeed, let


be the n-stage discounted cost under the policy γΓand the initial inventory level II,and


the corresponding value function. Then, for all n0and II,(see, e.g., [6, 10, 11]),


Moreover, from [11, Theorem 8.3.6], by making the appropriate changes, we have the following result.

Theorem 3.4 (Dynamic programming)(a) The functionsVnandVbelong toBW.Moreover


  1. (b) Asn,VnVW0.

  2. (c)Vis convex.

  3. (d)Vsatisfies the dynamic programming equation:


  1. (e) There exists a functiong:IQsuch thatgIQand, for eachII,


Moreover,γ=gis an optimal control policy.

4. Density estimation

As the density ρis unknown, the results in Theorem 3.4 are not applicable, and therefore they are not accessible to the IM. In this section we introduce a suitable density estimation method with which we can obtain an estimated DP-equation. This will allow us to define a scheme for the construction of optimal policies. To this end, let D0,D1,,Dt,be independent realizations of the demand whose density is ρ.

Theorem 4.1There exists an estimatorρtsρtsD0D1Dt1,s0,), ofρ, such that (see(2) and(3)):

  1. D.1.ρtL10is a density.

  2. D.2.ρtρ¯a.e. with respect to the Lebesgue measure.

  3. D.3.0sρtsdsD¯.

  4. D.4.E0ρtρsds0,ast.

  5. D.5.Eρtρ0,ast,where


for measurable functions μon 0.

It is worth noting that for any density μon 0satisfying (14), the norm μis finite. The remainder of the section is devoted to prove Theorem 4.1.

We define the set DL1([0,))as:


Observe that ρD.

Lemma 4.2The setDis closed and convex inL1([0,)).

Proof.The convexity of Dfollows directly. To prove that Dis closed, let μtDbe a sequence in Dsuch that μtL1μL1([0,)).First, we prove


We assume that there is A[0,)with mA>0such that μs>ρ¯s,sA,mbeing the Lebesgue measure on . Then, for some ε>0and AAwith mA>0,


Now, since μtD,t0,there exists Bt0,)with mBt=0,such that


Combining (21) and (22) we have


Using the fact that m(A([0,)\Bt)=mA>0, we obtain that μtdoes not converge to μin measure, which is a contradiction to the convergence in L1.Therefore μsρ¯sa.e.

On the other hand, applying Holder’s inequality and using the fact that ρ¯L10, from (20),


which implies 0μsds=1.Now, as μ0a.e.,we have that μis a density. Similarly, from (4),


for some constant M<.Letting twe obtain


which, in turn, implies that


This proves that Dis closed.∎

Let ρ̂tsρ̂tsD0D1Dt,s[0,),be an arbitrary estimator of ρsuch that


Lemma 4.2 ensures the existence of the estimator ρtwhich is defined by the projection of ρ̂ton the set of densities D.That is, the density ρtD, expressed as


is the “best approximation” of the estimator ρ̂ton the set D,that is,


Now observe that ρtsatisfies the properties D.1, D.2, and D.3. Hence, Theorem 4.1 will be proved if we show that ρtsatisfies D.4 and D.5. To this end, since ρD, from (26) observe that


which implies that, from (25),


That is, ρtsatisfies Property D.4. In fact, since 0ρsρtsds2a.s., from (27) it is easy to see that


Now, to obtain property D.5, observe that from (12)


Therefore, property D.4 yields


which proves the property D.5.

5. Estimation and control

Having defined the estimator ρt,we will now introduce an estimate dynamic programming procedure with which we can construct optimal policies for the inventory systems.

Observe that for each t0,from (14),


Now, we define the estimate one-stage cost function:


where (see Remark 3.2) for y=I+q,


In addition, observe that for each t0,Ltyis convex and


We define the sequence of functions Vtas V00,and for t1


We can state our main results as follows:

Theorem 5.1(a) Fort0andII,


Therefore, VtBW.

  1. (b) Ast,EsupIqI×QctIqc(Iq)WI0.

  2. (c) Ast,EVtVW0.

  3. (d) For eacht0,there existsKt0such that the selectorgt:IQdefined as


attains the minimum in(34).

Remark 5.2From [10, Proposition D.7], for eachII, there is an accumulation pointgIQof the sequencegtI. Hence, there exists a constantKsuch that


Theorem 5.3Letgbe the selector defined in(36). Then the stationary policyγgis an optimal base stock policy for the inventory problem.


6. Proofs

6.1 Proof of Lemma 3.3

Note that, for each IqI×Q,


where Mmaxc+hQ+bD¯hand GI=I+1.Moreover, for every density function μon 0and IqI×Q,


On the other hand, we define the sequence of functions wt,wt:I, as


and for t1and any density function μon 0


Observe that, for each II,




In general, it is easy to see that for each II,


Let α0α1be arbitrary, and define


Then, from (40),


Therefore, WI<for each II, and since w0>1,from (41),


Furthermore, using (42) and the fact that Ww0,a straightforward calculation shows that


Now, from (37) and (39), cIqw0I,which yields, for all IqI×Q,


In addition, for every density function μon 0and IqI×Q,


Therefore, defining βα01,we have that 0<αβ<1,and


which, together with (43), (44), and (45), proves Lemma 3.3.∎

6.2 Proof of Theorem 5.1

  1. (a)Since 0sρtsdsD¯, from (32) (see (37)) ctIqMGIfor each t0,IqI×Q.Hence, it is easy to see that ctIqWIfor each IqI×Q(see (45)). Then we have V1IWI,and from (31), and by applying induction arguments, we get


  1. (b)Observe that from (39), for each II,


  1. which implies that (see (43))


  1. In addition, from (37),


  1. On the other hand, similarly as (24), from (4), it is easy to see that


  1. for some constant M<.Hence, combining (47)(49), from the definition of ctIqand cIq, we have


  1. Finally, taking expectation, (28) and Property D.4 prove the result.

  2. (c)For each IIand t0,by adding and subtracting the term α0Vt1I+qs+ρsds, we have


where the last inequality is due to (35), (17), (14), and (15). Therefore, from (15) and (19) and by taking expectation,


Finally, from (17) and (35), ηlimsuptEVVtW<. Hence, taking limsup in both sides of (50), from part (a) and property D.5 in Theorem 4.1, we get ηαβη, which yields η=0(since 0<αβ<1). This proves (c).

  1. (d)For each t0,let Ht:Ibe the function defined as


Hence, (34) is equivalent to


Moreover (see (33)), observe that Htis convex and limyHty=.Thus, there exist a constant Kt0such that




attains the minimum in (51).∎

6.3 Proof of Theorem 5.3

We fix an arbitrary II. Since gIis an accumulation point of gtI(see Remark 5.2), there exists a subsequence tmIof t(tm=tmI)such that


Moreover, from (34) and Theorem 5.1(d), letting tm=m,we have


On the other hand, following similar arguments as the proof of Theorem 5.1(c), for each m0and IqI×Q,we have


Then, for each II,




Taking expectation and liminf as mon both sides of (54), from (53) we obtain


where the last inequality follows by applying Fatou’s Lemma and because the function qI+qs+is continuous. Hence, taking expectation and liminf in (52), we obtain


As Iwas arbitrary, by (18), the equality holds in (55) for all II. To conclude, standard arguments on stochastic control literature (see, e.g., [10]) show that the policy γ=gis optimal.∎

7. Concluding remarks

In this chapter we have introduced an estimation and control procedure in inventory systems when the density of the demand is unknown by the inventory manager. Specifically we have proposed a density estimation method defined by the projection to a suitable set of densities, which, combined with control schemes relative to the inventory systems, defines a procedure to construct optimal ordering policies.

A point to highlight is that our results include the most general scenarios of an inventory system, e.g., state and control spaces either countable or uncountable, possibly unbounded costs, finite or infinite inventory capacity. This generality entailed the need to develop new estimation and control techniques, accompanied by a suitable mathematical analysis. For example, the simple fact of considering possibly unbounded costs led us to formulate a density estimation method that was related to the weight function W, which, in turn, defines the normed linear space BW(see (15)), all this through the projection estimator. Observe that if the cost function cis bounded, we can take W1and we have =L1(see (19) and (25)). Thus, any L1consistent density estimator ρtcan be used for the construction of optimal ordering policies.

Finally, the theory presented in this chapter lays the foundations to develop estimation and control algorithms in inventory systems considering other optimality criteria, for instance, the average cost or discounted criteria with random state-action-dependent discount factors (see [14, 15] and references therein).

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Jesús Adolfo Minjárez-Sosa (August 7th 2019). Density Estimation in Inventory Control Systems under a Discounted Optimality Criterion, Statistical Methodologies, Jan Peter Hessling, IntechOpen, DOI: 10.5772/intechopen.88392. Available from:

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