Mathematical Problems in Engineering

Volume 2014 (2014), Article ID 464256, 8 pages

http://dx.doi.org/10.1155/2014/464256

Research Article

Control of Supply Chain Based on Switched Model of Stock Level

Junzhi Luo and Wanly Yang

School of Department of Fundamental Courses, Academy of Armored Force Engineering, Beijing 100072, China

Received 21 December 2013; Accepted 27 February 2014; Published 6 April 2014

Academic Editor: Huaicheng Yan

Copyright © 2014 Junzhi Luo and Wanly Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This paper is concerned with the problem of control for a class of discrete supply chain systems. A new method based on network control technique is presented to address this issue. Supply chain systems are modeled as networked systems with stochastic time delay. Sufficient conditions for controller design are given in terms of a set of linear matrix inequalities, based on which the mean-square asymptotic stability as well as performance is satisfied for such systems. Simulation results are provided to demonstrate the effectiveness of the proposed method.

1. Introduction

A supply chain is a network of facilities and distribution entities (suppliers, manufacturers, distributors, and retailers) that manufactures raw materials into intermediate and finished products and distributes finished products to customers [1]. Nowadays, with the globalization of business, the phenomena of increasing competition among entities and more customer demanding lead to more complex dynamic behaviors of supply chain, such as demand fluctuation and lead-time delay. The static model is insufficient to model such systems with the dynamic characteristics; as a result, the dynamic analysis and control of supply chain systems have attracted more and more interests and attentions; see, for example, [2–13].

Various dynamic models have been employed for modeling and analyzing supply chain systems, which are mainly classified into continuous differential equations and discrete difference equations. For example, the piecewise-linear controller was designed based on continuous differential equation for a production system in [5], the discrete difference models were provided to analyze system performance in [6–8, 10, 11], and the robust control strategy based on discrete models was given with uncertainty in [12, 13]. It should be noted that the information exchanging of production schedule, inventory positions, and procurement plan and customer demand, making decision, and implementing decision are usually realized by network. In essence, supply chain systems are network control systems; the introduction of network facilitates the analysis and synthesis of supply chain systems.

As far as the network control systems (NCS) are concerned, they are spatially distributed systems in which the communication between sensors, actuators, and controllers occurs through a shared band-limited digital communication network. In the past decades, due to attractive features such as increased system flexibility, simple installation and maintenance, lower cost, and reduced weight and power, the study of NCSs has attracted considerable attention; see, for example, [14–18]. It is well recognized that the presence of network usually leads to the signal transmission delays. A common assumption in most of existing results on network-based control is that the network-induced delay has upper bounded or both lower and upper bounded. This assumption has been used in a great number of papers, such as [19, 20]. In fact, due to the unpredictability of the network environment, network-induced delay is random by nature. Furthermore, the disturbance of external factors or network congestion will make the induced delay become big, even bigger than upper bound of time delay. For these reasons, some researchers are devoted to the study of the problem of random delays in stochastic control systems [15, 21–25]. On the other hand, the switching among the different agents is the important feature of a supply chain system. It is essential to consider the switching behavior when modeling a supply chain system. The great advance in the study of switched delay systems [26, 27] helps us to the modeling, analysis, and synthesis of a supply chain system. However, to the authors’ best knowledge, the influence of the network factors and the switching behavior on supply chain systems is scarcely investigated in the existing literature. This motivates the present study.

In this paper, we are interested in the study of control for a class of discrete supply chain systems. The following aspects are well addressed. Firstly, supply chain systems are modeled as switched network model with stochastic delay when considering the existence of switching and the random of network-induced delay. Discrete difference equations are utilized to describe the supply chain system, which is distinct from existed results that are concerned with the models of such systems. Secondly, sufficient conditions are obtained based on Lyapunov stability theory, which guarantee mean-square stability, and the bullwhip effect of supply chain system is studied in the sense of the stochastic performance. Lastly, simulation results are provided to demonstrate the effectiveness of the proposed method.

The organization of this paper is as follows. In Section 2, the switched network model with stochastic delay is given to describe supply chain. In Section 3, the mean-square stability with performance for supply chain systems is analyzed based on network model. Then, an example is provided to illustrate the effectiveness of the proposed method in Section 4. Finally, Section 5 concludes this paper.

Notations. In this paper, we use to denote a positive definite (semidefinite, negative definite, and seminegative definite) matrix . “” stands for mathematic expectation of random vector. The superscript “” stands for matrix transpose and the symmetric terms in a matrix are denoted by “”; “” denotes the -norm which is given by .

2. Problem Formulating

Consider the following supply chain system described by

where , , and denote the stock level, manufactured level, and customers demand, respectively. From the point view of control system, they are state variable, control input, and exogenous disturbance. is output. is a switching signal which takes its value in the finite set. Moreover,denotes that theth subsystem is activated.

Before giving the switching rules, we define the switching function

Now, the switched systems are given as follows.

(i)If , where is a positive scalar, that is, the stock level is more than the value of warning, some of the produced parts are being stored, the first subsystem is active.(ii)If , that is, the stock level is between 0 and the value of warning, the second subsystem is active; otherwise, the third subsystem is active if .

In this paper, state feedback controller based on networked control will be designed to stabilize the system (1), which takes the following form:

where is network-induced delay.

Substituting the controllers (3) with system (1), the augmented system (1) with delays gives rise to the following system:

Here, we assume that the network-induced delay takes values in a finite set, that is, , and the occurrence probability of the delaysis, that is, Prob, whereis a positive scalar and .

Due to the random of , the random vector is defined as follows:

Then, it is easy to get

By introducing random delay parameters, the supply chain system is transformed into a multiple delay system. The closed-loop supply chain system (1) can be rewritten as

Before proceeding further, some definitions are given in the following.

Definition 1. The closed-loop supply chain system (7) with is said to be mean-square stable if, for any , there is , such that , when .

In addition, if

for any initial conditions, the closed-loop system (7) is said to be globally mean-square asymptotically stable (GMSAS).

Definition 2. The closed-loop supply chain system (7) is said to satisfy the performance, if

(1)the augmented closed-loop system (7) is mean-square asymptotically stable with ,(2)the closed-loop system (7) satisfies for all nonzero under the zero initial condition.

Definition 3. The bullwhip effect is the amplification phenomenon which is described by the sum of the radio of to the customer’s demand fluctuation ; that is,

where refers to production and inventory vector, refers to demand fluctuation vector, time serial is , and refers to the bullwhip effect.

Remark 4. For the existence of , the effect of to is also transformed into the problem of performance analysis for the closed-loop supply chain systems; that is, the effect of bullwhip can be studied by the problem of performance analysis.

Our purpose is to design a controller (3) such that the effect of to the output is under a desired level in the performance.

3. Analysis on Performance of Supply Chain

In this section, we will give the sufficient conditions such that the performance can be guaranteed for system (7). The following theorem will play an important role in the controller design.

Theorem 5. For given scalars , if there exist scholar and symmetric positive definite matrices , , such that the following matrix inequalities simultaneously hold

where

then system (7) is mean-square stable and satisfies performance.

Proof. First, we consider system (7) with .

Construct a Lyapunov function in the form of

in which

It is easy to see that

where

According to Schur complement, we can derive that which is equivalent to , where

At the same time, we can know if .

Let ,be switching times over the interval,be the switching numbers of switching signalover the interval.

If , there exists , such that the following inequality holds:

Then

where . Since ,, it can be given that

Furthermore, we can get

Based on Definition 1, we can derive that system (7) is mean-square asymptotically stable.

Second, we consider system (7) with :

where

By Schur complement, we can obtain

which is equivalent to

Let , for ; then we have

that is,

According to the definition of performance, we can know that system (7) with satisfies performance.

It is important to note that the conditions of Theorem 5 are not linear matrix inequalities; the following Theorem 6 will give another equivalent condition.

Theorem 6. For given scalars , if there exist scholar and symmetric positive definite matrices , ,,, such that the following linear matrix inequalities simultaneously hold

where

then, system (7) satisfies performance. Furthermore, the gain of controller can be given by .

Proof. Left and right multiplying (10) by the following matrix

and letting

we have

Meanwhile, left and right multiplying (11) by and gives rise to . According to condition (28) and (29) of Theorem 6, it is obvious that system (7) maintains performance. The proof is completed.

Remark 7. Let ; the stochastic model of system (7) can be transformed into deterministic model. The stochastic model of system (7) is more universal than deterministic model. Moreover, the conditions of Theorems 5 and 6 have more applications than the one given based on the deterministic model.

4. Numerical Example

In this section, we will give a numerical example to demonstrate the effectiveness of the provided method for supply chain system modeled as network control system.

Example 8. We take the coefficient matrixes of system as follows:

Let ,, and ;,, and ; and . Solving the LMIs (28) with the help of the Matlab LMI Toolbox, we can obtain

By further calculating, we obtain the following gain matrices for the controller (3):

Choosing the initial state as and the customer demand disturbance with , the state response and the control input are illustrated in Figures 1 and 2. We find that the state is maintained at zero when the time is trending to infinity from Figure 1. That is to say, the stock level of supply chain can be maintained at the state of equilibrium and the bullwhip effect is limited by scholar ; that is, the effect of on is limited by scholar .

Figure 1: The state variable of system (7).

Figure 2: The control variable of system (7).

5. Conclusion

The problem of control for a class of discrete supply chain systems is addressed in this paper. With the help of network control technique, supply chain systems are modeled as network control systems with stochastic time delay. The mean-square stability and performance are studied based on the theory of switched and network control system. Sufficient conditions for the controllers design are given in terms of a set of linear matrix inequalities. Finally, simulation examples are provided to illustrate the effectiveness of the developed results. If network-induced delay is random with probability distribution function, the problem of analysis and synthesis of such system will be considered in future work.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

H. Sarimveis, P. Patrinos, C. D. Tarantilis, and C. T. Kiranoudis, “Dynamic modeling and control of supply chain systems: a review,” Computers and Operations Research, vol. 35, no. 11, pp. 3530–3561, 2008.

B. M. Beamon, “Supply chain design and analysis: models and methods,” International Journal of Production Economics, vol. 55, no. 3, pp. 281–294, 1998.

M. Boccadoro, F. Martinelli, and P. Valigi, “Supply chain management by control,” IEEE Transactions on Automation Science and Engineering, vol. 5, no. 4, pp. 703–707, 2008.

C. E. Riddalls and S. Bennett, “Production-inventory system controller design and supply chain dynamics,” International Journal of Systems Science, vol. 33, no. 3, pp. 181–195, 2002.

L. Rodrigues and E. K. Boukas, “Piecewise-linear controller synthesis with applications to inventory control of switched production systems,” Automatica, vol. 42, no. 8, pp. 1245–1254, 2006.

X. Y. Huang, N. N. Yan, and H. F. Guo, “An control method of the bullwhip effect for a class of supply chain system,” International Journal of Production Research, vol. 45, no. 1, pp. 207–226, 2007.

X. Y. Huang, N. N. Yan, and R. Z. Qiu, “Dynamic models of closed-loop supply chain and robust control strategies,” International Journal of Production Research, vol. 47, no. 9, pp. 2279–2300, 2009.

M. Boccadoro, F. Martinelli, and P. Valigi, “ control of a supply chain model,” in Proceedings of the 45th IEEE Conference on Decision and Control (CDC '06), pp. 4387–4392, San Diego, Calif, USA, December 2006.

S. Srivathsan and M. Kamath, “An analytical performance modeling approach for supply chain networks,” IEEE Transactions on Automation Science and Engineering, vol. 9, no. 2, pp. 265–275, 2012.

H. F. Guo, Y. L. Zhu, C. G. Chang, and X. M. Zhou, “ control of bullwhip effects in closed-loop supply chain networks with two chains cooperation,” in Proceedings of the 7th World Congress on Intelligent Control and Automation (WCICA '08), pp. 2206–2211, Chong Qing, China, June 2008.

R. Z. Qiu and X. Y. Huang, “Two-stage supply chain dynamic model and robust control,” Control Engineering of China, vol. 15, no. 1, pp. 9–12, 2008.

R. G. Ge and X. Y. Huang, “Uncertain supply chain model based on switched inventory and robust control,” Systems Engineering, vol. 27, no. 3, pp. 119–122, 2009.

R. G. Ge and X. Huang, “Closed-loop supply chain switching model with outsourcing selection and its robust control,” Computer Integrated Manufacturing Systems, vol. 15, no. 10, pp. 2012–2016, 2009.

L. Zhang, H. Gao, and O. Kaynak, “Network-induced constraints in networked control systems—a survey,” IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 403–416, 2013.

H. J. Gao, T. W. Chen, and J. Lam, “A new delay system approach to network-based control,” Automatica, vol. 44, no. 1, pp. 39–52, 2008.

H. Zhang, H. C. Yan, F. W. Yang, and Q. J. Chen, “Distributed average filtering for sensor networks with sensor saturation,” IET Control Theory and Applications, vol. 7, no. 6, pp. 887–893, 2013.

X. M. Sun, G. P. Liu, W. Wang, and D. Rees, “-gain of systems with input delays and controller temporary failure: zero-order hold model,” IEEE Transactions on Control Systems Technology, vol. 19, no. 3, pp. 699–706, 2011.

H. C. Yan, H. B. Shi, H. Zhang, and F. W. Yang, “Quantized control for networked systems with communication constraints,” Asian Journal of Control, vol. 15, no. 5, pp. 1468–1476, 2013.

D. Yue, Q. L. Han, and J. Lam, “Network-based robust control of systems with uncertainty,” Automatica, vol. 41, no. 6, pp. 999–1007, 2005.

W. Zhang, M. S. Branicky, and S. M. Phillips, “Stability of networked control systems,” IEEE Control Systems Magazine, vol. 21, no. 1, pp. 84–99, 2001.

L. Schenato, “Kalman filtering for networked control systems with random delay and packet loss,” in Proceedings of the 17th International Symposium of Mathematical Theory of Networks and Systems, Kyoto, Japan, 2006.

H. J. Gao and T. W. Chen, “New results on stability of discrete-time systems with time-varying state delay,” IEEE Transactions on Automatic Control, vol. 52, no. 2, pp. 328–334, 2007.

H. C. Yan, Z. Z. Su, H. Zhang, and F. W. Yang, “Observer-based control for discrete-time stochastic systems with quantisation and random communication delays,” IET Control Theory and Applications, vol. 7, no. 3, pp. 372–379, 2013.

H. Zhang, Q. J. Chen, H. C. Yan, and J. H. Liu, “Robust filtering for switched stochastic system with missing measurements,” IEEE Transactions on Signal Processing, vol. 57, no. 9, pp. 3466–3474, 2009.

H. Zhang, Z. H. Guan, and G. Feng, “Reliable dissipative control for stochastic impulsive systems,” Automatica, vol. 44, no. 4, pp. 1004–1010, 2008.

Q. K. Li, J. Zhao, X. J. Liu, and G. M. Dimirovski, “Observer-based tracking control for switched linear systems with time-varying delay,” International Journal of Robust and Nonlinear Control, vol. 21, no. 3, pp. 309–327, 2011.

X. M. Sun, J. Zhao, and D. J. Hill, “Stability and -gain analysis for switched delay systems: a delay-dependent method,” Automatica, vol. 42, no. 10, pp. 1769–1774, 2006.