Complexity
Volume 2017 (2017), Article ID 2017634, 11 pages
https://doi.org/10.1155/2017/2017634

Research Article

Fuzzy Control Model and Simulation for Nonlinear Supply Chain System with Lead Times

Songtao Zhang,¹ Yanting Hou,² Siqi Zhang,³ and Min Zhang⁴

¹School of Logistics, Linyi University, Linyi 276005, China
²School of Economics and Management, Tongji University, Shanghai 200092, China
³Faculty of Business and Economics, The University of Melbourne, Melbourne, VIC 3010, Australia
⁴Library, Linyi University, Linyi 276005, China

Correspondence should be addressed to Songtao Zhang; zst0626@163.com

Received 18 February 2017; Revised 4 June 2017; Accepted 13 August 2017; Published 14 September 2017

Academic Editor: Pietro De Lellis

Copyright © 2017 Songtao Zhang et al. 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.

A new fuzzy robust control strategy for the nonlinear supply chain system in the presence of lead times is proposed. Based on Takagi-Sugeno fuzzy control system, the fuzzy control model of the nonlinear supply chain system with lead times is constructed. Additionally, we design a fuzzy robust control strategy taking the definition of maximal overlapped-rules group into consideration to restrain the impacts such as those caused by lead times, switching actions among submodels, and customers’ stochastic demands. This control strategy can not only guarantee that the nonlinear supply chain system is robustly asymptotically stable but also realize soft switching among subsystems of the nonlinear supply chain to make the less fluctuation of the system variables by introducing the membership function of fuzzy system. The comparisons between the proposed fuzzy robust control strategy and the robust control strategy are finally illustrated through numerical simulations on a two-stage nonlinear supply chain with lead times.

1. Introduction

Over the recent years, a large number of companies realize the value-added importance of supply chain (SC) system and have cooperated as a part of it [1]. Efficient management of distribution, production, and supply in the SC has critical influence on business success [2]. However, SC system is more sensitive to the existence of lead time. Lead time, which is affected by the physical distance between the seller and the buyer, transportation mode, manufacturer’s production capability, and technology in practice [3], can result in oscillation and instability of the SC system directly. Therefore, effectively restraining the impact of lead time on the SC system can be one of the major challenging issues to be resolved for the node companies in competition [4].

For the controllable lead time, Mahajan and Venugopal [5] studied the impacts of the reduction of lead time on the retailer and manufacturer’s costs. For a two-stage SC consisting of a manufacturer and a retailer, Leng and Parlar [6] investigated game-theoretic models of the reduction of lead time. According to the reduction of lead time caused by the added crashing cost, Li et al. [7] studied the coordination problem of a decentralized SC. Glock [8] proposed alternative approaches on the reduction of lead time and their impacts on the safety inventory and the expected total cost of the integrated inventory system. Further, a model of the divergent SC to study how to minimize the expected total cost and reduce lead times to find the optimal production, inventory, and routing decisions has been described by Jha and Shanker [9]. With the help of lead time variation control, Heydari [10] developed an incentive scheme to realize the service level coordination in a two-stage SC.

On the other hand, for the uncontrollable lead time, Garcia et al. [11] proposed an Internal Model Control (IMC) approach to control the production inventory in a SC with lead times. By utilizing the multimodel scheme, the IMC control approach can realize the online identification of lead times. In addition, Xu and Rong [12] utilized the minimum variance control theory to derive the order-up-to policy for the SC with time-varying lead time. Taleizadeh et al. [13] performed a particle swarm optimization to access the inventory problem of the chance-constraint joint single vendor-single buyer with changeable lead time. To the aim of restraining the bullwhip effect of uncertain SCs with vendor order placement lead time, a robust optimization strategy has been highlighted by Li and Liu [14]. Garcia et al. [15] incorporated an IMC scheme in production inventory control system of a complete SC to online identify lead times. Further, Han et al. [16] analyzed the approximate optimal inventory control problem of SC networks with lead time and proposed a suboptimal inventory replenishment strategy to effectively reduce bullwhip effect and improve the performance of SC networks system. Movahed and Zhang [17] formulated the inventory system of a single-product three-level multiperiod SC with uncertain demands and lead times as a robust mixed-integer linear program with minimized expected cost and total cost variation to determine the optimal s, values of the inventory parameters. Using the proportional control approach, Wang and Disney [18] mitigated the amplification of order and inventory fluctuations in a state-space SC model with stochastic lead time.

The SC system is only considered as a linear system whether with the controllable lead time or with the uncontrollable lead time. However, it is worth noting that the SC system is dynamic in the operational process due to the influences of the uncertain customers’ demands and lead times. In this perspective, this leads to multiple possible strategies in manufacturing, delivering, and ordering products measured by the relation between upstream company’s inventory level and downstream company’s demand state. That is to say, the node companies of manufacturing or ordering can implement multiple strategies instead of one in different scenarios. In such a situation, a linear switching system with many modes can be performed instead of a unique SC model as well. Therefore, the SC system is nonlinear dynamic with piecewise linear characteristics. Nevertheless, the SC as a nonlinear dynamic system has been rarely addressed in the related literature.

Robust fuzzy control strategies for controlling nonlinear dynamic systems have been addressed broadly. For the nonlinear systems with uncertainties, Lee et al. [19] studied the fuzzy robust control problem for the continuous-time and discrete-time nonlinear systems with parametric uncertainties based on Takagi-Sugeno (T-S) fuzzy model and derived the sufficient conditions of robust stabilization in the sense of Lyapunov asymptotic stability; Yang and Zhao [20] presented a robust control approach for uncertain switched fuzzy system and designed a continuous state feedback controller to ensure the relevant closed-loop system is asymptotically stable for all allowable uncertainties. The nonlinear systems with time delay are also mentioned in a few literatures. Cui et al. [21] discussed the problem of robust control for a class of uncertain switched fuzzy time-delay systems described by T-S fuzzy model and derived a sufficient condition to guarantee the stability of the closed-loop systems. Further, Teng et al. [22] investigated the robust model predictive control of a class of nonlinear discrete system subjected to time delays and persistent disturbances. However, the robust control approaches in [19–22] cause higher conservative to guarantee the stability of the system by finding the common positive definite matrices.

In this paper, we will propose a fuzzy robust control strategy to restrain the impacts of lead times, switching actions among subsystems, and customers’ stochastic demands on the nonlinear dynamic SC system. By utilizing the concept of maximal overlapped-rules group (MORG), the control strategy can be obtained from T-S fuzzy system associated with robust control method, which can guarantee the stability of the system if the local common positive definite matrices in each MORG can be found. Therefore, the proposed control strategy can (i) reduce the conservatism as compared with the existing control approaches; (ii) make SC system robustly asymptotically stable; and (iii) realize soft switching among subsystems of the nonlinear dynamic SC. We make some comparisons with the robust control strategy to demonstrate the effectiveness of our proposed strategy.

The rest of this paper is arranged as follows. The fuzzy model of the nonlinear dynamic SC system with lead times is formulated in Section 2. Then Section 3 proposes a new fuzzy robust control strategy. Finally, Section 4 provides an illustrative example of a two-stage nonlinear SC system with the production lead time and the ordering lead time to verify the advantage of the proposed control strategy. Our conclusions are presented in Section 5.

2. Model Construction and Preliminaries

2.1. Nonlinear Dynamic SC Fuzzy System

The formulation of a basic model of two-stage SC system with lead times (i.e., production lead time and ordering lead time) can be illustrated in Figure 1.

Figure 1: Basic dynamic model of two-stage SC system.

In Figure 1, and denote manufacturer inventory level and retailer inventory level at period , respectively, and denote the productions manufactured by manufacturer at period and with the production lead time , respectively, and are the numbers of products ordered by retailer at period and with the ordering lead time , respectively, and is the customers’ demands at period .

Remark 1. Figure 1 can describe 4 kinds of SC systems: when and , Figure 1 denotes the chain-type SC system; when and , Figure 1 denotes the distribution-type SC system; when and , Figure 1 denotes retailers-centered multistage SC system, like supermarket. when and , Figure 1 denotes the SC network system.
The basic dynamic mathematical model of the SC system is presented as follows:

In the operational process of the SC system, the node companies will adopt different production or ordering strategies according to their own different inventory levels, which results in many different basic dynamic models, and the basic dynamic models can be called subsystems. Moreover, to reduce the total cost of this SC system, there exist switching actions among subsystems at different period . Therefore, the dynamic SC system is a piecewise linear system, which can be also called a nonlinear system.

By utilizing the matrix theory and considering the total cost of the nonlinear SC system, the ith subsystem of (1) can be described as follows:

where the subscript corresponds to the SC being in the ith mode, () is the inventory state variable, is the control variable, is the control variable with lead times, is the customers’ demands variable, is the total cost of the SC system, is coefficients matrix of the inventory strategy implementation, is coefficients matrix of the productivity and ordering placement, is coefficients matrix of the productivity and ordering placement during lead times, is coefficients matrix of customers’ demands, is coefficients matrix of the inventory cost, is coefficients matrix of the manufacturing and ordering cost, and is coefficients matrix of the manufacturing and ordering cost with lead times.

This nonlinear SC system (2) is described with deviation values which are the differences between the actual values and the nominal values.

T-S fuzzy system is a powerful tool for processing nonlinear systems [23]. T-S fuzzy system consists of fuzzy rules that express local linear relationship between inputs and outputs of a system. Hence, based on T-S fuzzy system, we will establish a nonlinear SC fuzzy system.

For the nonlinear SC system (2), the th fuzzy control rule can be described as follows:

: if is , and, and is , and is , then

where () is the th fuzzy rule, is the number of if-then rules, () is the fuzzy set of the inventory level, and is the initial condition.

By singleton fuzzification, product inference, and center-average defuzzification, (3) can be inferred as

where the membership function , in which . represents the grade of membership of in . For all , and . For simplicity, we omit in .

2.2. Fuzzy Control Strategy

To restrain the impacts caused by lead times, switching actions among subsystems, and customers’ stochastic demands on the nonlinear SC system, this paper will design a fuzzy robust control strategy. This strategy can incorporate the membership function of T-S fuzzy system into the robust control method to realize soft switching and make the system robustly asymptotically stable.

Based on the parallel distributed compensation scheme, the control law of the nonlinear SC system is formulated as follows.

Controller rule is as follows: if is and, and is , and is , then

where and denote the inventory feedback gains matrices of the th local model. is the coefficients matrix of production plan and ordering delivery; is the coefficients matrix of production plan and ordering delivery during lead time.

Using the fuzzy controller (5), this paper intends to make the following system robustly asymptotically stable during lead times:

The inhibitory degree of this controller (5) can be described as the parameter ; namely,

where is norm [24]. The smaller the parameter is, the better the performance of this SC fuzzy system (6) is.

2.3. Preliminaries

Before proceeding, we will introduce our theorem by recalling the following definitions, proposition, and lemmas.

Definition 2 (see [25]). For a given scalar which denotes the disturbance attenuation level for a system, the nonlinear SC fuzzy system (6) is said to be robustly asymptotically stable with the constraint under the norm if two conditions as below are satisfied.
The nonlinear SC fuzzy system (6) is robustly asymptotically stable when .
Under zero-initial condition, the total cost of the nonlinear SC fuzzy system (6) satisfies for any nonzero and all admissible uncertainties.
Accordingly (5) is called a -suboptimal robust control law of the SC fuzzy system (6).

Definition 3 (see [26]). A cluster of fuzzy sets are said to be a standard fuzzy partition (SFP) in the universe if each is a normal fuzzy set and are full-overlapped in the universe . is said to be the number of fuzzy partitions of the jth input variable on .

Definition 4 (see [26]). For a given fuzzy system, an overlapped-rules group with the largest amount of rules is said to be a maximal overlapped-rules group (MORG).

Proposition 5 (see [26]). If the input variables of a fuzzy system adopt SFPs, then all the rules in an overlapped-rules group must be included in a MORG.

Lemma 6 (see [27]). For any real matrices , for , and with appropriate dimensions, we have

where are defined as , .

Lemma 7. For any real matrices , and with appropriate dimensions, the following inequality holds:

Proof. For Lemma 6, let ; then we have

Therefore, can be obtained.

3. Fuzzy Robust Control of Nonlinear SC

A fuzzy robust output-feedback controller for the T-S fuzzy system with uncertainties was recently introduced by [28]. It also came to use in [29] to restraint of the bullwhip effect for uncertain closed-loop SC system.

In this section, we also apply this idea of the fuzzy controller for the nonlinear SC fuzzy system (6) with lead times.

Theorem 8. For a given scalar , if there exist local common positive definite matrices and in such that the following linear matrix inequalities (LMIs) (11) and (12) are satisfied, then the supply chain fuzzy system (6) with lead times and SFP inputs is robustly asymptotically stable and the norm is less than a given bound :

where , , , , , , , , denotes the zero matrix, denotes the identity matrix, is a set of the rule numbers included in , denotes the th MORG, , and is the number of the fuzzy partitions of the th input variable.

Proof. Consider two scenarios: first, if state input variables and are in the same overlapped-rules group, the fuzzy system (6) will be proved to be robustly asymptotically stable. Then if state input variables and are in the different overlapped-rules groups, the same result will be obtained.
Assume that the fuzzy system (6) contains overlapped-rules groups; is the operating region of the dth overlapped-rules group and .
In the first scenario, the local model of the dth overlapped-rules group can be described as

where is the state feedback control gain of production lead time and ordering lead time in the cth MORG.
Equation (13) can be represented as follows:

where

Consider the discrete Lyapunov function:

And using Lemma 7 will supply

where and .
Thus satisfies the relation

Assume the customers’ demands ; performance index function can be presented by

The above equation can be rewritten as

Substituting (18) into (20), we have

Applying Schur complement to (11) and (12) results in and . Then, can be obtained; that is, ; moreover, let ; then we have . As a result, the SC system (14) is proved to be asymptotically stable in the case of .
On the other hand, if , it is obvious that (18) is equivalent to the following inequality:

According to (11) and (12) we can obtain and , respectively. Accordingly, we can conclude that . As a result, the state feedback controller can ensure the local system (6) robustly asymptotically stable in the dth overlapped-rules group.
In the second scenario: a characteristic function in any overlapped-rules group is constructed as follows:

where ; then the global model of the discrete fuzzy system in the input universe of the discourse can be described as follows:

In the following, based on the definitions of and , a piecewise Lyapunov function can be introduced in the input universe of the discourse as follows:

We first assume that the customers’ demands . For (24) we can obtain after the performance index function is considered. can be obtained through a similar procedure; that is, ; moreover, let ; then we have . As a result, (24) is proved to be asymptotically stable in the case of .
On the other hand, if , we have

Hence, in any overlapped-rules group, (24) with is asymptotically stable by the fuzzy controller (5).
Therefore, according to Proposition 5, we can conclude that the fuzzy system (6) is robustly asymptotically stable with Condition (11) and Condition (12) by resorting to find local common positive definite matrices and in . This completes the proof of the theorem.

In Theorem 8, for a given performance index , we can obtain the robust stabilization conditions of (6), which are represented by a set of matrix inequalities in (11) and (12). Subsequently we will show that such inequalities can be transformed into LMIs when designing the actual controllers. Note that the feasibility of LMIs can be easily achieved by using the LMI toolbox in MATLAB.

Theorem 8 can be recast as the LMI problem by the following Theorem 9.

Theorem 9. For the supply chain fuzzy system (6) with lead times and SFP inputs, if there exist a given scalar , local common positive definite matrices and , and matrices , , , in , such that the following LMIs are satisfied, then the supply chain fuzzy system (6) is robustly asymptotically stable under the performance :

where

is a set of the rule numbers included in , denotes the cth MORG, , , and is the number of the fuzzy partitions of the jth input variable.

Proof. The proof is analogous to that of Theorem 8. For simplicity, the similar sections are truncated. Theorem 9 can be easily demonstrated by using matrix transformations and the Schur complement. The main procedure is as follows.
In order to solve the LMIs, (11) can be expressed further as follows:

Taking the congruence transformation with easily verifies

Clearly, (12) is equivalent to

Taking the congruence transformation with results in

Remark 10. Compared with the common robust control approach, the proposed fuzzy robust control strategy is less sensitive to the variations of system parameters and can realize soft switching and make the system robustly asymptotically stable. The proposed fuzzy robust control strategy requires only finding local common positive definite matrices in each MORG to check the robust stability of T-S fuzzy system. Therefore, the new control strategy can reduce the conservation and difficulty of the common Lyapunov function approach. The performance and validity of the controller (5) depend on the parameter , which will play an important role in the existence of some matrices satisfying a series of LMIs. The smaller the parameter is, the better the performance of the system will be. Therefore, when is set too small in order to obtain the better performance of the system, some matrices satisfying a series of LMIs may not exist. For a smaller , if some matrices satisfying a series of LMIs do not exist, will be constantly set to a bigger value little by little until some matrices satisfying a series of LMIs exist.

Remark 11. The numbers of LMIs satisfied to check the stability of a system for Theorem 9 in this paper and for Theorem  3 in [30] are and , respectively.

Table 1 shows the comparison results of the numbers of LMIs satisfied between Theorem  3 in [30] and Theorem 9 in this paper, where denotes the number of state variables and and denote the numbers of LMIs satisfied for Theorem  3 in [30] and Theorem 9 in this paper, respectively.

Table 1: The comparison results of the numbers of LMIs satisfied between Theorem  3 in [30] and Theorem 9 in this paper.

1
3
3
29
18

1
4
4
53
27

2
3
9
428
64

2
4
16
2213
144

From Table 1, we know that the difference of and becomes more and more large along with the increase of .

4. Simulation Research

4.1. Modeling of Two-Stage Chain-Type SC Fuzzy System

We illustrate the effectiveness of the fuzzy robust control strategy described in Section 3, which is based on Chinese compressors manufacturing industry. This industry can be considered as a two-stage chain-type nonlinear SC system with production lead time and ordering lead time.

The manufacturer’s production strategies are set as follows. When the manufacturer’s inventory level , the manufacturer manufactures the products according to the JIT (Just In Time) mode in order to meet the retailer’s order needs. When ( is the manufacturer’s expected inventory), the manufacturer normally manufactures the products to satisfy the retailer’s demand.

The retailer’s order strategies are set as follows. When the retailer’s inventory level , the retailer asks for help from other SC. When ( is the retailer’s expected inventory), the retailer normally orders the products from the manufacturer.

As shown in Figure 2, the fuzzy partitions of and are () and , respectively, and meet the conditions of SFP. Set , , , and . In Figure 2, ( sets), ( sets), and and denote the manufacturer’s maximal inventory level and the retailer’s maximal inventory level, respectively.

Figure 2: Fuzzy membership functions of inventory input variables.

In Figure 2, there is one MORG named that includes 4 rules in this system.

We can obtain this SC system with the production lead time and the ordering lead time as follows.

: if is and is , then

where , , , is the production lead time, is the ordering lead time, , denotes the customers’ demands, and the system parameters are set as follows: , , , , , , , , , , , , , , , , , , , , , where and are the manufacturer’s unit inventory cost and the retailer’s unit inventory cost, respectively, ( RMB) and ( RMB), is the unit manufacturing cost when , ( RMB), is the unit manufacturing cost under JIT condition, ( RMB), is the retailer’s unit ordering cost when , ( RMB), is the retailer’s unit ordering cost from the manufacturers in the different SCs, ( RMB), and is the supplying rate from other SCs, , .

Then the fuzzy controller is designed as follows:

4.2. Simulation Analysis

When , this SC fuzzy system is robust stable by being found the following local matrices in MORG:

In verifying the advantages of the proposed fuzzy robust control strategy for this nonlinear SC fuzzy system, the simulation experiments will be performed.

The initial values of states are ( sets), ( sets), and the normal values are set as ( sets), ( sets), ( sets), ( sets), ( RMB), the production lead time (in weeks) and the ordering lead time (in weeks), and the customers’ demands . The simulation results are expressed as the actual values.

To show the superiority of our proposed control strategy, the robust control strategy and our control strategy are provided for comparison. The robust control strategy can be described as follows:

Figures 3, 5, and 7 illustrate the evolution processes of inventory levels, production and ordering quantity, and total cost under the robust control strategy.

Figure 3: Evolution processes of inventory levels under the robust control strategy.

By using our proposed control strategy, that is to say, fuzzy robust control strategy, the simulation results are shown in Figures 4, 6, and 8.

Figure 4: Evolution processes of inventory level under the fuzzy robust control strategy.

Figure 5: Evolution processes of production and ordering quantity under the robust control strategy.

Figure 6: Evolution processes of production and ordering quantity under the fuzzy robust control strategy.

Figure 7: Evolution process of total cost under the robust control strategy.

Figure 8: Evolution process of total cost under the fuzzy robust control strategy.

Figures 3 and 4 are selected as an example to analyze the control effects of two control approaches. As shown in Figures 3 and 4, the differences of the crests and the troughs for the manufacturer’s inventory level under the robust control strategy and the fuzzy robust control strategy are sets-15    sets and sets-   sets, respectively.

5. Conclusion

The robust stabilization problem of the operational process of the dynamic SC system with lead times has been studied. Utilizing T-S fuzzy system, we establish the nonlinear dynamic SC fuzzy system. To restrain the disturbances of lead times, switching actions among subsystems, and customers’ stochastic demands, a new fuzzy robust control strategy has been proposed by utilizing the definition of MORG. In addition, this strategy can guarantee that the nonlinear SC system with lead times is robustly asymptotically stable. In the simulation research, we have discussed the operational process of two-stage nonlinear SC system with the production lead time and the ordering lead time, and the simulation tests have verified the advantage of the proposed fuzzy robust control strategy. Our designed strategy can be applied to some node companies in supply chain system.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the National Social Science Foundation of China (17BGL222).

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