1. Introduction and Proposal of the Paper
We consider a factory producing types of economic goods that stores them in an inventory-designated place. The model is described mathematically in the next.
Let
,
be a complete filtered probability space, where
P is the historical probability and
is generated by an
-valued Brownian motion denoted by
with respect to the probability
P.
In the production planning problem, the regime switching is captured by a continuous-time homogeneous Markov chain
adapted to
that can take
k different values, modeling
k regimes, which should be noted by
. The Markov chain’s rate matrix that denotes the strongly ireductible generator of
, is denoted by
where
and the diagonal elements
may be expressed as
In this case, if
, then
Moreover,
s explicitly described by the integral form
where
is a martingale with respect to
. Here and hereafter, we use the notation from other papers to keep the applicative character of the problem,
which represents the production rate at time
t (control variable)
adjusted for the demand rate.
These adjusted-for-demand inventory levels are modeled by the following system of stochastic differential equations
where
is an Itô process in
(i.e., the inventory level of good
i, at times
adjusted for demand),
is the deterministic part,
is a random regime-dependent constant (non-zero) diffusion coefficient taking on the values
,
, …,
, and
is the initial condition (i.e., initial inventory level of goods
i).
The stochasticity here is due to demand adjustment, which is random and dependent on the regime. This is the most commonly used process when the demand is more volatile in some periods (e.g., some states of the Markov chain) and less volatile in other periods.
The performance over time of a demand-adjusted production
is measured by means of its cost. At this point, we introduce the cost functional, which yields the cost
which measures the quadratic loss.
We measure deviations from the demand, from what place the loss. Here, is a regime-dependent, taking on the values , , …, , constant psychological rate of time discount from what place the exponential discounting.
At the moment, we are ready to frame our objective, which is to minimize the cost functional, i.e.,
Subject to the Itô Equation (
4), the cost functional involves adjusted-for-demand inventory levels
y whose dynamic is given by (
4), and it depends on the choice of the demand-adjusted production
p. Minimizing the cost functional in (
6) means selecting the demand-adjusted production
p so that it minimizes
J (of (
5)). Notice that
J involves both
y and
p.
This model problem was proposed by Bensoussan, Sethi, Vickson, and Derzko [
1] in the context of no regime switching in the economy and for the case of a factory producing one type of economic goods. Later, many other authors were concerned with regime switching.
In production management, Cadenillas, Lakner, and Pinedo [
2] adapted the model problem in [
1] to study the optimal production stochastic control planning problem of a company within an economy characterized by two-state regime switching with limited/unlimited information. Later, Dong, Malikopoulos, Djouadi, and Kuruganti [
3] applied in civil engineering the model described by [
2] to the study of the optimal stochastic control problem for home energy systems with solar and energy storage devices when the demand is subject to Brownian motion; the two switching regimes are the peak and off peak energy demand.
A good deal of attention to this subject has been also devoted by Pirvu and Zhang [
4], where the authors studied the effect of high versus low discount rates to a consumption-investment decision problem.
After that, there have been numerous applications of regime switching in many important problems in economics, operations research, actuarial science, finance, reinsurance, and other fields, for example, the portfolio optimization problem in a defaultable market with finitely-many economical regimes is considered by Capponi and Figueroa-López in [
5]; the pricing of derivatives using a stochastic discount factor modeled as a regime-switching geometric Brownian motion is discussed by Elliott and Hamada in [
6]; the production control in a manufacturing system with multiple machines, which are subject to breakdowns and repairs, is considered by Gharbi and Kenne in [
7]; the problem of the pricing of European-style options with switches among a finite number of states is discussed by Yao, Zhang, and Zhou in [
8]; and no later, Wang, Chang, and Fang [
9] considered the optimal portfolio and consumption rule with a Cox–Ingersoll–Ross (CIR) model in a general utility framework.
There are of course other research studies that may also serve to better explain the importance of regime switching in the real world.
In a precursor to this article, Covei and Pirvu [
10] formulate and analyze the production-planning problem in the continuous-time case, with no regime switching in the economy over an infinite time. In [
11], the author improved the results of [
10], in the sense that the value function in the production model is given in the closed form. Related works that deal with no regime switching in the economy are Sheng-Zhu-Wang [
12] and Qin-Bai-Ralescu [
13].
Recently, Canepa, Covei, and Pirvu [
14] considered the production planning problem with regime switching in the economy over a finite horizon time. Here, the solution is obtained through numerical approaches. However, a closed-form expression for the corresponding case of regime switching on a particular state space consisting of two regimes over an infinite horizon time is available in the paper of [
15]. So, at least one question suggested by the paper of [
16] has some nice features: can we obtain a closed-form solution when the state space consists of several numbers of states? Our present paper fills the gap in the literature by proving a closed-form solution to the stochastic production planning problem with regime switching in the economy over an infinite horizon in a general state space.
To conclude this introduction, our paper is structured as follows. In
Section 2, we give the relationship of our model with a system of partial differential equations (PDE) system.
Section 3 presents a closed-form solution and the uniqueness of the solution for our production planning problem. A numerical approximation of the solution for the production planning problem is also given in
Section 4. In
Section 5, we present a verification result. We introduce in
Section 6 the equilibrium production rates as the subgame perfect production rates. They are the output of an interpersonal game between the present self and future selves. The equilibrium production rates are time consistent, meaning there is no incentive to deviate from them. It turns out that in our setting the optimal production rates are among the equilibrium ones so they are time consistent. In
Section 7, we give some applications. Finally, in
Section 8, we discuss our strategy.
The technique presented in this paper makes a methodological contribution that is of independent interest in other considerable numbers of works on regime switching.
Having presented the model that we want to solve, now we provide our means to tackle it.
2. Reduction of the Model to a PDE System
Our approach is based on the value function and dynamic programming, which leads to the Hamilton–Jacobi–Bellman (HJB) system of equations.
To characterize the value function, we apply the probabilistic approach. We search for functions
, …,
such that the stochastic process
defined below
is supermartingale for all
And martingale for the optimal control
As shown by [
10], if this is achieved, with the following transversality condition
some estimates on the value function yield that
where
assumes values
Once such a function is found, it turns out that
with
is the value function. We search for
, the functions in
, and the supermartingale/martingale requirement yields by using Itô’s Lemma for Markov-modulated diffusion, the HJB system of equations, which characterizes the value function
where
For the transformation of the HJB system, it is essential to observe that
Thus, the HJB system (
10) can be written as a PDE system
To perform the verification, i.e., show that the HJB system gives the solution to the optimization problem, one should write (
12) with the following boundary condition
The value function will give us in turn the candidate optimal control. The first-order optimality conditions on the left-hand side of (
11) are sufficient for optimality since we deal with a quadratic (convex) function, and they produce the candidate optimal control as follows:
and
The production rate is allowed to be negative. A negative production rate would correspond to a write-off or disposal of inventory (for example, due to obsolescence or perishability).
Our next goal of this paper is to determine the candidate optimal control in closed form.
5. Verification
Next, we show that the control of (
14) obtained in our reduction strategy is indeed optimal. We apply the supermartingale and martingale approaches.
Repeating the same argument in [
14], as the first step, we can show that the stochastic process
defined below
is supermartingale for all
And martingale for the optimal control
Owing to the well-known Itô Lemma for Markov-modulated diffusion (see [
8] for more on this), we have
for some martingale
, and
. Therefore,
Then, the claim yields considering HJB Equations (
10) and (
12), which says that
is martingale for the optimal control and supermartingale otherwise. This last fact combined with the transversality condition yields the claim.
In the second step, let us establish the optimality of
. Consider the quadratic estimate on the value function
where
and
are the solutions of (
16).
Let us provide a lower-bound estimate for
so that the transversality condition (
8) is met and
holds true. The SDE system (
4) in this case becomes
Using Itô’s Lemma, one obtains
We introduce
By taking expectations in the above equation, we obtain
Then, in the light of the above equation, we obtain
Therefore, one must choose
for the transversality condition to hold true, and the proof is completed. Finally, a simple system of nonlinear Equations (
16) remains to be solved.
7. Applications
We offer some applications, which also are inspired by the paper of Ghosh, Arapostathis, and Marcus [
16].
Application 1. Suppose there is one machine producing two products, and let
be the machine state that can take values in two regimes, 1 = good and 2 = bad, i.e., for every
, we have
. We consider
a continuous-time Markov chain with generator
And the inventory
, which is governed by the Itô system of stochastic differential Equations (
4) with the diffusion
, and let
be the discount factor. Under these assumptions, the system (
17) becomes
or, with our data
which has a unique positive solution
On the other hand, System (
19) becomes
or, with our data
which has a unique positive solution
Then,
and furthermore, the production rate is
We also give the approximate of
and
, and
and
, by using the Newton–Raphson method. Denote
and
Using the standard computation, approximations to four digits are
Clearly, the approximations for
and
are
Application 2. Suppose there is one machine producing three products, and let
be the machine state that can take values in three regimes 1, 2, and 3, i.e., for every
, we have
. We consider
a continuous-time Markov chain with generator
and the inventory
, which is governed by (
4) with
, and let
be the discount factor. Under these assumptions, System (
17) becomes
or, with our data
which has a unique positive solution
On the other hand, System (
19) becomes
or, with our data
from where
has a unique positive solution
Then,
and furthermore, the production rate is
We also point out that the numerical approximations for
,
, and
, using the Newton–Raphson method described, are
when
,
, and
. Clearly,
.