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Research article

Design of reasonable initialization weighted enhanced Karnik-Mendel algorithms for centroid type-reduction of interval type-2 fuzzy logic systems

  • Received: 22 December 2021 Revised: 01 March 2022 Accepted: 04 March 2022 Published: 18 March 2022
  • MSC : 68XX, 68Uxx

  • Interval type-2 fuzzy logic systems (IT2 FLSs) already become an emerging technology in recent years. As the most popular type-reduction (TR) algorithms, Karnik-Mendel (KM) algorithms own the advantage of maintaining the uncertainties flow in systems. This paper analyzes the initialization for KM types of algorithms. Furthermore, the weighting approaches of them are also given by means of the Newton-Cotes quadrature formulas. Importantly, the reasonable initialization weighted enhanced Karnik-Mendel (RIWEKM) algorithms are provided to complete the centroid type-reduction of IT2 FLSs. Three computer simulation experiments illustrate that, the proposed RIWEKM algorithms own both smaller absolute errors and faster convergence speeds in contrast to the EKM and RIEKM algorithms.

    Citation: Yang Chen, Jiaxiu Yang, Chenxi Li. Design of reasonable initialization weighted enhanced Karnik-Mendel algorithms for centroid type-reduction of interval type-2 fuzzy logic systems[J]. AIMS Mathematics, 2022, 7(6): 9846-9870. doi: 10.3934/math.2022549

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  • Interval type-2 fuzzy logic systems (IT2 FLSs) already become an emerging technology in recent years. As the most popular type-reduction (TR) algorithms, Karnik-Mendel (KM) algorithms own the advantage of maintaining the uncertainties flow in systems. This paper analyzes the initialization for KM types of algorithms. Furthermore, the weighting approaches of them are also given by means of the Newton-Cotes quadrature formulas. Importantly, the reasonable initialization weighted enhanced Karnik-Mendel (RIWEKM) algorithms are provided to complete the centroid type-reduction of IT2 FLSs. Three computer simulation experiments illustrate that, the proposed RIWEKM algorithms own both smaller absolute errors and faster convergence speeds in contrast to the EKM and RIEKM algorithms.



    Retrial queues (RQs) with unreliable servers have been extensively investigated due to their wide range of applications in domains such as consumer service centers, computer and communications networks, and systems for production. Retrial queues can serve as an indication of consumer service demands. When consumers find an inaccessible server, they have the option to join a retry group, referred to as an orbit, and make a request for their desired services at another moment in time. To access survey papers, bibliographic information, and books, readers are referred to Falin [1], Artalejo [2], Falin and Templeton [3], and the references provided in these sources. Boussaha et al. [4] explored feedback retrial queues and orbit search with the M/G/1 queueing system. Atencia et al. [5] examined a non-Markovian retrial queue and discussed the customer's sojourn time in the server, system, and orbit. Jeganathan et al. [6] conducted an analysis of asynchronous multiple vacations using a multi-server retrial queueing inventory system. Micheal and Indhira [7] have analyzed a retrial queueing model with two-phase service under Bernoulli working vacations (BWV). They introduced adaptive neuro-fuzzy inference system (ANFIS) computation and cost optimization of nonlinear metaheuristics to validate their model and also compared the results with other methods, such as artificial bee colonies, genetic algorithms, and particle swarm optimization.

    Queuing situations where idle servers may engage in vacations can be discovered in IT networks, machinery, and manufacturing systems, among several other domains. During a working vacation (WV) time, the server delivers its service to consumers at a slow rate, but during an ordinary vacation period, the server completely stops its service to consumers. Servi and Finn [8] presented a single-srever Markovian queueing system with WV. Wu and Takagi [9] expanded the Markovian queue with working vacation into the non-Markovian queue with WV. Gao et al. [10] introduced an M/G/1 retrial queue model that takes into account retrial times of a general nature, WV, and vacation interruptions. Rajadurai [11] examined the non-Markovian retrial queue and considered the three consumer-type categories, which are positive, negative, and priority consumers under WV policy. Yang et al. [12] presented a retrial queue model with WV and examined the occurrence of server failures at the start of service. Li et al. [13] investigated the M/G/1 type retrial queueing model, which incorporates single working vacation under Bernoulli schedule. Bouchentouf et al. [14] explored Markovian queues with finite capacity and differentiated working vacation (DWV). Jain et al. performed a study on Markovian queues with disaster failure and MWVs [15]. Murugan and Keerthana [16] investigated a single-server retrial queueing model that incorporates G-queue and MWV concepts. Bharathy and Saravanarajan [17] examined the unreliable retrial queue and two essential services using WV. Chen et al. [18] investigated random working vacation and improved service efficiency vacation policies using an M/G/1 queueing model.

    In reality, we frequently encounter situations in which servers fail but may be repaired. Furthermore, it is thought that server failures are the most common reason for service interruptions. Similarly, there are numerous instances that take place in the domains of digital networks, manufacturing systems, production control, and other related areas. Limited maintenance capabilities and service station breakdowns can have a significant impact on system performance. Therefore, queueing systems with unreliable service stations are worth investigating from a performance prediction perspective. Rajadurai et al. [19] explored non-Markovian type retrial queue with multiple WV incorporating server breakdown. Varalakshmi et al. [20] analyzed immediate feedback single server queues with working vacations and server breakdowns and used the supplementary variable technique to find steady-state results. Rajadurai et al. [21] investigated the cost optimization technique of a retrial queue with K-phase optional type of service with multiple working vacations subject to server breakdown. Gao et al. [22] investigated a retrial queue with active and passive breakdowns and delayed repairs. Ke et al. [23] studied the feedback retrial queue and balking, including the server breakdown. In addition, the author used the Probabilistic Global Search Lausanne approach to solve the optimization problem. Liu et al. [24] presented a Markovian queue that incorporates preemptive priority and WV interruption. Recently, a multitude of authors have extensively examined the concept of delayed repair from various perspectives [25,26,27,28,29,30].

    Many queueing scenarios involve consumers being serviced repeatedly for a specific purpose. If a consumer is dissatisfied with the service, they can attempt it multiple times until it is successfully completed. These queueing models are used in stochastic modeling of various real-world, such as in data transmission and packet switching networks. Sharma [31] presented Bernoulli feedback retrial queue with modified vacation subject to random breakdowns. Chang et al. [32] investigated unreliable retrial queue, which included impatient and feedback customers. The author also examined the methods used to perform the optimization tasks, including the Nelder-Mead simplex direct search method, the pattern search method, and the quasi-Newton approach. Ayyappan et al. [33] analyzed the concept of a single server queue and considered the customer priority type, reneging, and immediate feedback under WV. Abdollahi et al. [34] investigated single-server retrial queues, first essential, and k-phase optional services that incorporate server vacation and feedback policies. Jain and Kaur [35] explored a bulk arrival retrial model incorporating optional service and Bernoulli feedback and utilized the maximum entropy principle (MEP) to find steady state probability and waiting time; the Quasi Newton method was also used to find the optimal cost. Wang et al. [36] studied the machine learning-based compressed sensing channel estimation method for wireless communications, which is important for industrial internet of things (IIoT) uses. They explored a distributed compressed sensing-based method for the sparse correlation between channels in multiple-input multiple-output filter bank multicarrier (MIMO-FBMC) with offset quadrature amplitude modulation (OQAM) systems.

    Supplementary variables, representing elapsed and remaining times, determine the forward and backward Chapman-Kolmogorov equations that govern the relevant model. To manage the elapsed time, we can introduce supplementary variables that match each non-exponential governing random variable. This procedure transforms the non-Markovian process into a Markovian process by acquiring all essential information, ensuring that its present state alone determines its future. Numerous queue theorists have employed the supplementary variable technique, a widely recognized methodology in queueing theory, to address non-Markovian congestion issues in both everyday and industrial contexts. Using the approach of SVTs, Cox (1955) has examined a non-Markovian approach [37]. Jain et al. [38] surveyed the supplemental variable approach for studying M/G/1 queues with service interruptions caused by vacation or server breakdowns. Deepa and Azhagappan [39] used the SVT method to analyze the bulk arrival queue with optional second-phase service, including the optional re-service concept. Huang [40] presented an analysis of a batch arrival queue and service pattern in an optional phase with a randomized vacation concept using the SVT method.

    This study presents a novel approach to modeling an unreliable server in a single arrival feedback retrial queue with reneging, and delayed repair under working vacation. The current study enhances the previous research conducted by Madhu Jain [41] by incorporating the novel concepts of (1) single arrival, (2) reneging, and (3) delayed repair during busy and WV periods. To the authors, best knowledge, there is no existing literature in the field of the queueing model that discusses non-Markovian queues incorporating the concepts of general retry times, single arrival, feedback, reneging, and delayed repair in both RS and WV circumstances. Our study specifically considers the problem of an unreliable service in the RS and WV modes. The fundamental motivation for developing the suggested generalized model is its potential application in real-world queueing scenarios such as communication networks and consumer care.

    This article's next sections are organized as follows: Section 2 provides a detailed explanation of the system model and includes a real-life example. Section 3 presents the steady-state probabilities. Section 4 discusses the performance characteristics of the model. Section 5 examines special cases. Section 6 presents an analysis of cost optimization. Section 7 presents numerical examples that demonstrate the effects of different system performance factors. Finally, Section 8 provides the article's conclusion.

    In this section, we consider an M/G/1 feedback retrial queue with reneging, delayed repair, and WV subject to server breakdown. The following is an explanation of our proposed model (Figure 1), provided by

    Consumers arrival process: The consumers arrive at the queueing system following a Poisson process, with rates of λ in the busy state and λv in the working vacation state.

    The retrial rule: If the arriving primary consumer discovers that the server is available, the consumer starts his service instantly. However, the consumer has the option to join the orbit's retry group if the server is busy, or working vacation, or breakdown. They attempt to make repeated service requests at random times based on the "First In-First Out" discipline; this indicates that only the single customer at the head of the orbit queue can access the server. In the event that the primary customer comes first, the retrial customer is able to cancel their request and proceed to either rejoin the retrial queue with probability (prob.) r or exit the system with prob. (1r). This is the behavior of the retrial consumer, also known as reneging. It was considered that the inter-retrial time is indicated by the distribution function A(t), while the Laplace Stieltjes Transform (LST) is denoted as A(ˆυ).

    Working vacation policy: When the orbit appears to be empty, the server commences its working vacation (WV), and the vacation time follows an exponential distribution with the characteristic parameter θ. When primary consumers arrive during a working vacation, the server delivers service at a slow rate. If there is any consumer present in the system during the slow speed service completion in the vacation state, the server will stop the vacation and resume the regular busy period, causing an interruption of the vacation. When a vacation period ends and there are still consumers in orbit, the server starts up normally. Otherwise, the server will take another vacation. The service time takes the form of a distribution function Nv(t) during the WV period and its LST is represented as Nv(ˆυ).

    Regular service process: Upon the arrival of a new consumer or a retry consumer at the service station, if the server is free, it immediately commences rendering regular service to the consumers. The service time is denoted by the general distribution and its function Nb(t) and LST are represented as Nb(ˆυ) and E(Nb), E(Nb)2 denote the first and second moments, respectively.

    Feedback rule: Following the completion of each consumer's service, dissatisfied consumers i.e., customers who are not satisfied with their service, may join the retry group with a prob. of f(0f1) or leave the system with a prob. of ¯f=1f.

    Server breakdown event: In fact, the server operating both in normal service mode and in working vacation mode is vulnerable to failures occurring at any moment, resulting in an unpredictable disruption of the service time. The durations of breakdowns in both scenarios, namely the regular and working vacation states, are followed by exponential distributions with rates δ and δv, respectively.

    Repair process: The server repair task commences instantly upon the server's failure, whether it occurs during peak activity or on a working vacation. While undergoing repairs, the server temporarily stop its services until the repair process is finished. Note that the consumer is waiting for the remaining services after receiving assistance from the server during a breakdown. When a server encounters a failure, it undergoes repair. During the breakdown time, the server is unable to serve the consumers and is waiting for the repair process to begin, which we refer to as the server's waiting time. We define the waiting period as the delay period. The delay timings are represented by the functions Db(t) and Dv(t), their Laplace-Stieltjes transforms are Db(y) and Dv(y), and its first two moments are assumed to be w(1), w(2), and wv(1), wv(2), depending on the busy and working periods, respectively. Similarly, the repair times are evaluated using the distribution functions Gb(t) and Gv(t), with their Laplace-Stieltjes transforms denoted as Gb(y) and Gv(y), and its two moments are assumed to be g(1) and g(2), whereas the subsequent two moments are represented as gv(1) and gv(2), which correspond to the busy and working vacation states, respectively.

    Figure 1.  Diagram of our proposed model.

    Our approach has practical application in various domains like data processing, internet access, manufacturing and industrial production systems, management of inventory systems, software, and experiments. Let's take a network of telecommunications as an example. Call centers have a significant impact on several industries and businesses in the field of telecommunications. Consumers are initiating contact with call centers by engaging in conversation with a consumer care representative (CCR). An incoming voice call is taken immediately by the idle CCR. During a voice call, if the CCR is unavailable because it is busy with other calls, the call will be temporarily held in a retrial buffer (orbit) with a finite capacity. If there is space available, the call will be handled at a later time (retrial time), according to the FCFS principle. The caller attempts to retry the call from orbit, but if they do not receive a response, they may choose to cancel their effort for service and exit the system, a process known as reneging. The service may experience electronic fails during the regular service mode (breakdown RS). Upon the end of call processing, the internet service may need the CCR to provide the same service again (feedback) in the event of any failures in the previous process. If the CCR does not detect any voice calls, it will carry out a series of maintenance tasks, such as doing virus scans (WV) on the system. During the repair time, the conventional center is equipped with various components, including an automatic call distributor (ACD) and an interactive voice response (IVR) unit (referred to as the WV server). These components are capable of handling calls at a slower rate during the working vacation period. However, it is possible that this server may experience technical issues during this period (referred to as Breakdown WV). When a server experiences a failure, it is taken out of service for repairs. This results in a temporary interruption of consumer service, termed the server's waiting time (referred to as delayed (RS, WV)). We define the duration of waiting as a period of delay. This type of retrial queue, which incorporates working vacations, serves as a reliable approximation of such for telecommunication processing systems. The suggested method can be used in modern culture, particularly in healthcare systems that use telephone consultations.

    The steady-state governing equations are derived using the SVT. The probability-generating function (PGF) was obtained for the server states and also for the number of consumers in the orbit and system.

    We consider that A(0)=0,A()=1,Nb(0)=0,Nb()=1,Nv(0)=0,Nv()=1,Wb(0)=0,Wb()=1,Wv(0)=0,Wv()=1, are continuous at ˆυ=0 Gb(0)=0,Gb()=1andGv(0)=0,Gv()=1 are continuous at y=0. We assume the hazard rate functions as ˙a (ˆυ),βb(ˆυ),βv(ˆυ),χb(y),χv(y),γb(y),andγv(y) for retrial, regular service, slow rate service, delay repair (RS, WV) and for the maintainance (RS, WV) in that order, respectively.

    ˙a(ˆυ)d(ˆυ)=d(A(ˆυ))(1A(ˆυ));βb(ˆυ)dˆυ=d(Nb(ˆυ))(1Nb(ˆυ));βv(ˆυ)dˆυ=d(Nv(ˆυ))(1Nv(ˆυ));χb(y)dy=d(Wb(y))(1Wb(y));χv(y)dy=d(Wv(y))(1Wv(y));γb(y)dy=d(Gb(y))(1G(y));γv(y)dy=d(Gv(y))(1G(y)).

    In addition, let A0,N0b,N0v,W0b,W0v,G0bandG0v be the expired retrial, busy, working vacation (WV), delay to repair, and repair times shown at period t. We also assume the random variable (RV),

    S(t)={0,  server is unoccupied1,  server is unoccupied and in RS mode2,  server is occupied and in RS mode3,  server is occupied and in lower-service mode4,  server is waiting for repair in WV mode5,  server is waiting for repair RS mode6,  the server is undergoing maintenance WV mode7,  the server is undergoing maintenance RS mode

    {S(t),C(t);t0} show the bivariate Markov process, where C(t) is the number of consumers in the orbit at time t. The function S(t) denotes the server states (0,1,2,3,4,5,6,7) depending on if the server is unoccupied, RS, WV, delayed repair (WV, RS), and under repair (WV, RS). Let us assume that the limiting probabilities P0(t)=Prob.S(t)=0,C(t)=0 and the prob. densities are

    In(ˆυ,t)dˆυ=limtProb.{S(t)=1,C(t)=n,ˆυA0(t)<ˆυ+dˆυ}Bb,n(ˆυ,t)dˆυ=limtProb.{S(t)=2,C(t)=n,ˆυN0b(t)<ˆυ+dˆυ}ϕv,n(ˆυ,t)dˆυ=limtProb.{S(t)=3,C(t)=n,ˆυN0v(t)<ˆυ+dˆυ}Db,n(ˆυ,y,t)dˆυ=limtProb.{S(t)=4,C(t)=n,yW0b(t)<y+dy/N0b(t)=ˆυ}Dv,n(ˆυ,y,t)dˆυ=limtProb.{S(t)=5,C(t)=n,yW0v(t)<y+dy/N0v(t)=ˆυ}ˇRb(ˆυ,y,t)dˆυ=limtProb.{S(t)=6,C(t)=n,yG0b(t)<y+dy/N0b(t)=ˆυ}ˇRv(ˆυ,y,t)dˆυ=limtProb.{S(t)=7,C(t)=n,yG0v(t)<y+dy/N0b(t)=ˆυ}                        t1,ˆυ1,n1.

    The time (tn;n=1,2,...) represents a sequence of epochs that correspond to the completion times of WV, or the point at which the delay is resolved and the repair period concludes. A Markov chain is created by a set of random vectors ψn={S(tn+),C(tn+)} forms a Markov chain that is embedded in the RQ system. It follows from Appendix A that {πn;nN} is ergodic if and only if Γ<1 for our system to be stable, where Γ=f+r(1A(λ)+E(Nb)(λ+λδ(w1+g1))).

    The governing equations are formulated using the supplementary variable approach.

    (λv+θ)P0=θP0+¯f(0Bb,0(ˆυ)βb(ˆυ)dˆυ+0ϕv,0(ˆυ)βv(ˆυ)dˆυ). (3.1)
    ddˆυIn(ˆυ)=[λ+˙a(ˆυ)]In(ˆυ),n1. (3.2)
    ddˆυBb,0(ˆυ)=[λ+δ+βb(ˆυ)]Bb,0(ˆυ),n=0. (3.3)
    ddˆυBb,n(ˆυ)=[λ+δ+βb(ˆυ)]Bb,n(ˆυ)+λBb,n1(ˆυ)+0ˇRb,n(ˆυ,y)γb(y)dy,n1. (3.4)
    ddˆυϕv,0(ˆυ)=[λv+δv+βv(ˆυ)]ϕv,0(ˆυ),n=0. (3.5)
    ddˆυϕv,n(ˆυ)=[λv+δv+βv(ˆυ)]ϕv,n(ˆυ)+λvϕv,n1(ˆυ)+0ˇRv,n(ˆυ,y)γv(y)dy,n1. (3.6)
    ddyDb,0(ˆυ,y)=[λ+χb(y)]Db,0(ˆυ,y),n=0. (3.7)
    ddyDb,n(ˆυ,y)=[λ+χb(y)]Db,n(ˆυ,y)+λvDb,n1(ˆυ,y),n1. (3.8)
    ddyDv,0(ˆυ,y)=[λv+χv(y)]Dv,0(ˆυ,y),n=0. (3.9)
    ddyDv,n(ˆυ,y)=[λv+χv(y)]Dn(ˆυ,y)+λvDv,n1(ˆυ,y),n1. (3.10)
    ddyˇRb,0(ˆυ,y)=(λ+γb(y))ˇRb,0(ˆυ,y),n=0. (3.11)
    ddyˇRb,n(ˆυ,y)=[λ+γb(y)]ˇRn(ˆυ,y)+λˆυb,n1(ˆυ,y),n1. (3.12)
    ddyˇRv,0(ˆυ,y)=(λv+γv(y))ˇRv,0(ˆυ,y),n=0. (3.13)
    ddyˇRv,n(ˆυ,y)=[λv+γv(y)]ˇRn(ˆυ,y)+λvˆυv,n1(ˆυ,y),n1. (3.14)

    When ˆυ=0 and y=0, the associated boundary conditions are stated as:

    In(0)=¯f(0Bb,n(ˆυ)βb(ˆυ)dˆυ+0ϕv,n(ˆυ)βv(ˆυ)dˆυ)+f(0Bb,n1(ˆυ)βb(ˆυ)dˆυ+0ϕv,n1(ˆυ)βv(ˆυ)dˆυ)n1. (3.15)
    Bb,0(0)=(0I1(ˆυ)˙a(ˆυ)dˆυ+λ(1r)0I1(ˆυ)dˆυ+θ0ϕv,0(ˆυ)dˆυ),n=0. (3.16)
    Bb,n(0)=0In+1(ˆυ)˙a(ˆυ)dˆυ+λr0I1(ˆυ)dˆυ+λ(1r)0In+1(ˆυ)dˆυ+θ0ϕv,n(ˆυ)dˆυ,n1. (3.17)
    ϕv,n(0)={λvP0,   n=0;0,        n1. (3.18)
    Db,n(ˆυ,0)=δ0Bb,n(ˆυ)dˆυ,n0. (3.19)
    Dv,n(ˆυ,0)=δv0ϕv,n(ˆυ)dˆυ,n0. (3.20)
    ˇRb,n(ˆυ,0)=0Db,n(ˆυ,y)χb(y)dy,n0. (3.21)
    ˇRv,n(ˆυ,0)=0Dv,n(ˆυ,y)χv(y)dy,n0. (3.22)

    The expression for normalizing condition is given as

    P0+n=10In(ˆυ)dˆυ+n=0(0Bb,n(ˆυ)dˆυ+0ϕv,n(ˆυ)dˆυ+00ˇRb,n(ˆυ,y)dˆυdy+00ˇRv,n(ˆυ,y)dˆυdy+00Db,n(ˆυ,y)dˆυdy+00Dv,n(ˆυ,y)dˆυdy)=1. (3.23)

    In this section, we derive the equation illustrating the steady-state of the RQ model by utilizing the prob. generating functions (PGFs) approach. To solve the above equations, the PGFs are defined for |ς|1 in the following manner:

    I(ˆυ,ς)=n=1In(ˆυ)ςn;I(0,ς)=n=1In(0)ςn;Bb(ˆυ,ς)=n=0Bb,n(ˆυ)ςn;Bb(0,ς)=n=0Bb,n(0)ςn;ϕv(ˆυ,ς)=n=0ϕv,n(ˆυ)ςn;ϕv(0,ς)=n=0ϕv,n(0)ςn;Db(ˆυ,y,ς)=n=0Db,n(ˆυ,y)ςn;Db(ˆυ,0,ς)=n=0Db,n(ˆυ,0)ςn;Dv(ˆυ,y,ς)=n=0Dv,n(ˆυ,y)ςn;Dv(ˆυ,0,ς)=n=0Dv,n(ˆυ,0)ςn;ˇRb(ˆυ,y,ς)=n=0ˇRb,n(ˆυ,y)ςn;ˇRb(ˆυ,0,ς)=n=0ˇRb,n(ˆυ,0)ςn;ˇRv(ˆυ,y,ς)=n=0ˇRv,n(ˆυ,y)ςn;ˇRv(ˆυ,0,ς)=n=0ˇRv,n(ˆυ,0)ςn.

    By multiplying equations Eqs (3.1)–(3.22) with ςn, and summing over n (where n=0,1,2,...), we obtain:

    ˆυI(ˆυ,ς)=[λ+˙a(ˆυ)]I(ˆυ,ς); (3.24)
    ˆυBb(ˆυ,ς)=[λ(1ς)+δ+βb(ˆυ)]Bb(ˆυ,ς); (3.25)
    ˆυϕv(ˆυ,ς)=[λv(1ς)+δv+βv(ˆυ)]ϕv(ˆυ,ς); (3.26)
    yDb(ˆυ,y,ς)=[λ(1ς)+χb(y)]Db(ˆυ,y,ς); (3.27)
    yDv(ˆυ,y,ς)=[λv(1ς)+χv(y)]Dv(ˆυ,y,ς); (3.28)
    yˇRb(ˆυ,y,ς)=[λ(1ς)+γb(y)]ˇRb(ˆυ,y,ς); (3.29)
    yˇRv(ˆυ,y,ς)=[λv(1ς)+γv(y)]ˇRv(ˆυ,y,ς); (3.30)
    I(0,ς)=(fς+¯f)(0Bb(ˆυ,ς)βb(ˆυ)dˆυ+0ϕv(ˆυ,ς)βv(ˆυ)dˆυ); (3.31)
    Bb(0,ς)=1ς0I(ˆυ,ς)˙a(ˆυ)dˆυ+λr0I(ˆυ,ς)dˆυ+λ(1r)ς0I(ˆυ,ς)dˆυ;                   +θ0ϕv(ˆυ,ς)dˆυ; (3.32)
    ϕv(0,ς)=λvP0; (3.33)
    Db,n(ˆυ,0,ς)=δ0Bb,n(ˆυ)dˆυ; (3.34)
    Dv,n(ˆυ,0,ς)=δv0ϕv,n(ˆυ)dˆυ; (3.35)
    ˇRb,n(ˆυ,0,ς)=0Db,n(y)γ(y)dy; (3.36)
    ˇRv,n(ˆυ,0,ς)=0Dv,n(y)γ(y)dy.        (3.37)

    Solving the partial differential Eqs (3.24)–(3.29), we get

    I(ˆυ,ς)=I(0,ς)[1A(ˆυ)]exp{λˆυ}; (3.38)
    Bb(ˆυ,ς)=Bb(0,ς)[1Nb(ˆυ)]exp{Ab(ς)ˆυ}; (3.39)
    ϕv(ˆυ,ς)=ϕv(0,ς)[1Nv(ˆυ)]exp{Av(ς)ˆυ}; (3.40)
    ˇRb(ˆυ,y,ς)=ˇR(ˆυ,0,ς)[1Gb(ˆυ)]exp{b(ς)y}; (3.41)
    ˇRv(ˆυ,y,ς)=ˇR(ˆυ,0,ς)[1Gv(ˆυ)]exp{bv(ς)y}; (3.42)
    Db(ˆυ,y,ς)=Dy(ˆυ,0,ς)[1Wb(ˆυ)]exp{b(ς)y}, (3.43)
    Dv(ˆυ,y,ς)=Dy(ˆυ,0,ς)[1Wv(ˆυ)]exp{bv(ς)y}. (3.44)

    where Ab(ς)=(λ(1ς)+δ(1Wb(b(ς))Gb(b(ς))),

    Av(ς)=(λv(1ς)+θ+δv(1Wv(bv(ς))Gv(bv(ς))), b(ς)=λ(1ς), and bv(ς)=λv(1ς).

    By substituting Eqs (3.38) and (3.40) into Eq (3.32), and subsequently applying certain modifications, we obtain the following expression:

    Bb(0,ς)=(I(0,ς)/ς)(A(λ)+(1r+rς)(1A(λ)))+λvP0V(ς), (3.45)

    where

    V(ς)=θ[1Nv(Av(ς))]Av(ς).

    Using Eqs (3.39) and (3.40) in Eq (3.31), gives

    I(0,ς)=(fς+¯f)(Bb(0,ς)Nb(Ab(ς))+ϕv(0,ς)Nv(Av(ς)))λvP0. (3.46)

    Using Eq (3.39) in Eq (3.34), we get

    Db(ˆυ,0,ς)=δBb(0,ς)(1Nb(Ab(ς))Ab(ς)). (3.47)

    Inserting Eq (3.40) in Eq (3.35), we obtain

    Dv(ˆυ,0,ς)=δvλvP0(1Nv(Av(ς))Av(ς)). (3.48)

    Inserting the Eq (3.43) in Eq (3.36), we obtain

    ˇRb(ˆυ,0,ς)=Db(ˆυ,0,ς)(χ(b(ς))). (3.49)

    Using the Eq (3.44) in Eq (3.37), gives

    ˇRv(ˆυ,0,ς)=Dv(ˆυ,0,ς)(χ(bv(ς))). (3.50)

    Using Eq (3.33) and (3.45) in Eq (3.46), we obtain

    I(0,ς)=Nu(ς)De(ς). (3.51)

    Where

    Nu(ς)=ςλvP0×{(fς+¯f)(Nb(Ab(ς))V(ς)+Nv(Av(ς)))1};De(ς)=[ς(fς+¯f)(A(λ)+(1r+rς)(1A(λ)))Nb(Ab(ς))].

    Using Eq (3.51) in Eq (3.45), we get

    Bb(0,ς)=P0De(ς)λv×{(fς+¯f)((Nv(Ab(ς))1)(A(λ)+(1r+rς)(1A(λ)))+ςV(ς))}. (3.52)

    Using Eq (3.52) in Eq (3.47), we get

    Db(ˆυ,0,ς)=δλvP0(1Nb(Ab(ς)))Ab(ς)×De(ς){(fς+¯f)((Nv(Ab(ς))1)(A(λ)+(1r+rς)(1A(λ))))+ςV(ς)}. (3.53)

    Using Eq (3.53) in Eq (3.49), we get

    ˇRb(ˆυ,0,ς)=δP0(1Nb(Ab(ς)))Wb(b(ς))Ab(ς)×De(ς){(fς+¯f)((Nv(Ab(ς))1)(A(λ)+(1r+rς)(1A(λ))))+ςV(ς)}.  (3.54)

    Using Eq (3.48) in Eq (3.50), we get

    ˇRv(ˆυ,0,ς)=δvλvP0(1Nv(Av(ς)))Wv(bv(ς))Av(ς). (3.55)

    Likewise, Eq (3.33), Eq (3.48), and Eqs (3.51)–(3.55) are inserted into Eqs (3.38)–(3.44). Next, we calculate the findings for the following PGFs: I(ˆυ,ς),Bb(ˆυ,ς),ϕv(ˆυ,ς) Db(ˆυ,0,ς),Dv(ˆυ,0,ς), ˇRb(ˆυ,0,ς),andˇRv(ˆυ,0,ς).

    Theorem 1. The prob. distributions of the number of consumers in orbit and the server states have the following PGFs.

    I(ς)=Nu(ς)De(ς); (3.56)
    Nu(ς)=ςλvP0(1A(λ)/λ)×{(fς+¯f)(Nb(Ab(ς))V(ς)+Nv(Av(ς)))1};De(ς)=[ς(fς+¯f)(A(λ)+(1r+rς)(1A(λ)))Nb(Ab(ς))].Bb(ς)=P0λv(1Nb(Ab(ς)))Ab(ς)De(ς){(fς+¯f)((Nv(Ab(ς))1)(A(λ)+(1r+rς)(1A(λ))))+ςV(ς)}; (3.57)
    ϕv(ς)={λPV0V(ς)/θ}; (3.58)
    Db(ς)=δλP0(1Nb(Ab(ς)))(1Wb(b(ς)))Ab(ς)×b(ς)×De(ς)×{(fς+¯f)((Nv(Ab(ς))1)(A(λ)+(1r+rς)(1A(λ))))+ςV(ς)}; (3.59)
    Dv(ς)=δvλvP0((1Wv(bv(ς)))(1NvAv(ς))bv(ς)Av(ς)); (3.60)
    ˇRb(ς)=δP0(1Nb(Ab(ς)))(1Gb(b(ς)))Wb(b(ς))b(ς)×Ab(ς)×De(ς)×{(fς+¯f)((Nv(Ab(ς))1)(A(λ)+(1r+rς)(1A(λ))))+ςV(ς)}; (3.61)
    ˇRv(ς)=δvλvP0(1Nv(Av(ς))(1Gv(Nv(ς))))Wv(Nv(ς))b(ς)Av(ς). (3.62)

    where

    P0=1fr(1A(λ)E(Nb)(λ+λδ(w1+g1))){1fr(1A(λ)E(Nb)(λ+λδ(w1+g1)))+λvE(Nb)(fNv(θ)+(Nv(θ)1)r(1A(λ))+1Nv(θ)(1Nv(θ))(λλδv(w1+(g1))/θ))×(1+δv(w1+g1))+λv(1fr(1A(λ)E(Nb)(λ+λδ(w1+g1))))(1Nb(θ)/θ)×(1+δv(w1v+g1v))+(f+(1Nv(θ))(λ+δ(w1λ+(g1λ))))×(E(Nb)+1/θ)} (3.63)

    Proof. By integrating equations (3.38) to (3.44) with respect to ˆυ, we may determine the PGFs.

    I(ς)=0I(ˆυ,ς)dˆυ,Bb(ς)=0Bb(ˆυ,ς)dˆυ,ϕv(ς)=0ϕv(ˆυ,ς)dˆυ,ˇRb(ς)=00ˇRb(ˆυ,y,ς)dˆυdy,ˇRv(ς)=00ˇRv(ˆυ,y,ς)dˆυdy,Db(ς)=00Db(ˆυ,y,ς)dˆυdy,Dv(ς)=00Dv(ˆυ,y,ς)dˆυdy

    The prob. of the server being idle, denoted as P0, can be calculated using the normalized condition. Therefore, by substituting ς=1 into Eqs (3.56) to (3.62) and following L'Hospital's rule when appropriate, we may derive P0+I(1)+Bb(1)+ϕv(1)+ˇRb(1)+ˇRv(1)+Db(1)+Dv(1)=1.

    Under the system stability condition (f+r(1A(λ)+E(Nb)(λ+λδ(w1+g1))))<1, the server is unoccupied, down, WV, delayed repair or under repair; then, the PGF for the number of consumers in the orbit and system is denoted as Ko(ς) and Ks(ς), respectively.

    Ko(ς)=Nuq(ς)Deq(ς)=P0+I(ς)+Bb(ς)+ϕv(ς)+ˇRb(ς)+ˇRv(ς)+Db(ς)+Dv(ς), (3.64)

    where

    Nu0(ς)={(1Nb(Ab(ς)))×{(fς+¯f)((Nv(Ab(ς))1)(A(λ)+(1r+rς)(1A(λ))))+ςV(ς)}+λ(1ς)(ς(1Aλ))((fς+(1f))Nb(Ab(ς))V(ς)+Nv(Av(ς))1)+λ(1ς)(ς(fς+(1f))(Aλ)+(1r+rς)(1A(λ)))NbAb(ς)+λ(1ς)((ς(fς+(1f))(Aλ)+(1r+rς)(1A(λ)))BbAb(ς)V(ς))×(λv/θ+δv/θ)(1Gvbv(ς)Wvbv(ς))}.Deq(ς)=b(ς)×+λ(1ς)(ς(fς+(1f))(Aλ)+(1r+rς)(1A(λ)))NbAb(ς).
    Ks(ς)=Nus(ς)Deq(ς)=P0+I(ς)+ς(Bb(ς)+ϕv(ς))+ˇRb(ς)+Db(ς)+ˇRv(ς)+Dv(ς). (3.65)
    Nus(ς)={ς(1Nb(Ab(ς)))×{(fς+¯f)((Nv(Ab(ς))1)(A(λ)+(1r+rς)(1A(λ))))+ςV(ς)}+λ(1ς)(ς(1Aλ))((fς+(1f))Nb(Ab(ς))V(ς)+Nv(Av(ς))1)+λ(1ς)(ς(fς+(1f))(Aλ)+(1r+rς)(1A(λ)))NbAb(ς)+ςλ(1ς)((ς(fς+(1f))(Aλ)+(1r+rς)(1A(λ)))NbAb(ς)V(ς))×(λv/θ)+λ(1ς)((ς(fς+(1f))(Aλ)+(1r+rς)(1A(λ)))NbAb(ς)V(ς))×(δv/θ)(1Gvbv(ς)Wvbv(ς))}.

    In this section, we examine the probabilities of the system states the server being unoccupied, RS, WV, delayed repair (RS, WV) and under maintenance (RS, WV). Also, we examine the average number of consumers in orbit Lq, the average number of consumers in the system Ls, mean availability SAv, system failure occur Failf, average busy time H(Tbs), and average busy cycle H(Tbc) of our model.

    The outcomes derived from Eqs (3.56)–(3.62) are obtained by substituting ς1 and thereafter applying L-Hospital's rule as appropriate.

    1) The prob. of the server remaining idle during the retrial period:

    I(1)=λvP0(1A(λ))×{(f+(1Nv(θ))(λ+δ(w1λ+(g1λ))))×(E(Nb)+1/θ)1fr(1A(λ)E(Nb)(λ+λδ(w1+g1)))}. (4.1)

    2) The prob. that the server is in RS period.

    Bb(1)=λvP0E(Nb){(fNv(θ)+(Nv(θ)1)r(1A(λ))+1Nv(θ)(1Nv(θ))(λλδv(w1+(g1))/θ))1fr(1A(λ)E(Nb)(λ+λδ(w1+g1)))}. (4.2)

    3) The prob. of the server operating at a reduced service rate:

    ϕv(1)={λvP0(1Nv(θ))/θ}. (4.3)

    4) The prob. that the server is delayed repair in RS:

    Db(1)=λvP0δE(Nb)w(1)×{(fNv(θ)+(Nv(θ)1)r(1A(λ))+1Nv(θ)(1Nv(θ))(λλδv(w1+(g1))/θ))1fr(1A(λ)E(Nb)(λ+λδ(w1+g1)))}. (4.4)

    5) The prob. that the server is delayed repair in WV:

    Dv(1)=λvP0δv×((1Nv(θ))θ)w(1)v. (4.5)

    6) The prob. that the server is undergoing maintenance in RS is determined by:

    ˇRb(1)=P0δλvE(Nb)g(1)×{(fNv(θ)+(Nv(θ)1)r(1A(λ))+1Nv(θ)(1Nv(θ))(λλδv(w1+(g1))/θ))1fr(1A(λ)E(Nb)(λ+λδ(w1+g1)))}. (4.6)

    7) The prob. that the server is undergoing maintenance in WV is determined by:

    ˇRb(1)=λvP0δv×((1Nv(θ))θ)g(1)v. (4.7)

    ⅰ) To get the number of consumers in the orbit Lq, we differentiate Eq (3.64) with respect to ς and evaluate it at ς=1.

    Lq=K0(1)=limς1K0(ς)=P0[Nuq (4.8)

    where

    \begin{equation*} \nonumber Nu_q^{''}(1) = -2\lambda\left\lbrace\begin{split} E(N_b)\left(\begin{split} f N_v^{*}(\theta)-N_v^{*'}(\theta)(\lambda_v+\delta(w^{1}\lambda_v+g^{1}\lambda_v))+(N_v^{*}(\theta)-1)r(1-A^{*}(\lambda))\\+V(1)+V^{'}(\varsigma)\end{split}\right)\\+ (1-f-r(1-A^{*}(\lambda)))(1+\frac{1-N_v^{*}\lambda_v}{\theta})\\+(1-A^{*}(\lambda))\left(\begin{split} f+E(N_b)(\lambda+\delta(w^{1}\lambda+g^{1}\lambda))V(1)+V^{'}(\varsigma)\\-(N_v^{*'}(\theta)(\lambda_v+\delta(w^{1}\lambda_v+g^{1}\lambda_v)))\end{split}\right) \end{split}\right\rbrace. \end{equation*}
    \begin{eqnarray*} \nonumber Nu_q^{'''}(1)& = &6\lambda\left\lbrace\begin{split} (1-f-r(1-A^{*}(\lambda)))E(N_b)\lambda+\delta(w^{1}\lambda+g^{1}\lambda)\\-(1-A^{*}(\lambda))f E(N_b)(\lambda+\delta\lambda(w^{1}+g^{1}))(1-N_v^{*}(\theta))+V^{'}(\varsigma)+N_v^{*'}(\lambda_v+\delta\lambda_{v}(w^{1}+g^{1}))+\\(1-A^{*}(\lambda))E(N_b)(\lambda+\delta\lambda(w^{1}+g^{1}))V^{'}(\varsigma)\\-(1-A^{*}(\lambda)) f+ E(N_b)(\lambda+\delta\lambda(w^{1}+g^{1}))(1-N_v^{*}(\theta))+V^{'}(\varsigma)+N_v^{*'}(\lambda_v+\delta\lambda_{v}(w^{1}+g^{1})) \\-E(N_b)\left(fN_v^{*'}(\theta)(\lambda_v+\delta\lambda_{v}(w^{1}+g^{1}))+\left(\begin{split} fN_v^{*}(\theta)\\-N_v^{*'}(\theta)(\lambda_v+\delta\lambda_{v}(w^{1}+g^{1}))\end{split}\right)\times r(1-A^{*}(\lambda)) \right)\\+(1-A^{*}(\lambda))V^{'}(\varsigma)+f r(1-A^{*}(\lambda))\left(1-\frac{\lambda_v (1-N_v^{*})(\theta)}{\theta}\right) \\- \lambda_v (1-f-r(1-A^{*}(\lambda)))\left(\begin{split} V^{'}-E(N_b)(\lambda+\delta\lambda(w^{1}+g^{1}))(1-N_v^{*}(\theta))\\-(1-N_v^{*}(\theta))\delta_v\lambda(w_v^{1}+g_v^{1})\end{split}\right) \end{split} \right\rbrace\\&& -3\lambda (1-A^{*}(\lambda))\left(\begin{split}E(N_b)^{2}(\lambda+\delta\lambda(w^{1}+g^{1}))(1-N_v^{*}(\theta))\\-E(N_b)\delta \lambda^{2}(w^{2}+g^{2}-2w^{1}g^{1})(1-N_v^{*}(\theta))\\+V^{''}(\varsigma)+N_v^{*''}(\theta)(\lambda_v+\delta \lambda_v(w_v^{1}+g_v^{1})^{2})+N_v^{*'}(\theta)\delta(\lambda_v^{2}(w_v^{1}+g_v^{1})-2(w^{1}g^{1})) \end{split} \right)\\&& -3\lambda^{2}E(N_b)^{2} \left(\begin{split}fN_v^{*}(\theta)-N_v^{*'}(\theta)(\lambda_v+\delta\lambda_{v}(w^{1}+g^{1}))+(1-N_v^{*}(\theta))\times r(1-A^{*})\\+(1-N_v^{*}(\theta))+V^{'}(\varsigma)\end{split}\right)\\&& -3E(N_b)\lambda\left(N_v^{*''}(\theta)(\lambda_v+\delta \lambda_v(w_v^{1}+g_v^{1})^{2})+N_v^{*'}(\theta)\delta(\lambda_v^{2}(w_v^{2}+g_v^{2})-2(w^{1}g^{1})) \right). \end{eqnarray*}
    \begin{align} De_q^{''}(1) = -2 \lambda \left((1-f-r(1-A^{*}(\lambda)))-E(N_b)\lambda+\delta(w^{1}\lambda+g^{1}\lambda) \right). \end{align}
    \begin{eqnarray*} De_q^{'''}(1) = 6\lambda f r(1-A^{*}(\lambda))+6\lambda E(N_b)(\lambda+\delta(w^{1}\lambda+g^{1}\lambda))[f+r(1-A^{*}(\lambda))].\\ -3\lambda E(N_b)^{2}(\lambda+\delta(w^{1}\lambda+g^{1}\lambda))+3\lambda\delta E(N_b)(\lambda^{2}(w^{2}+g^{2})-2w^{1}g^{1}), & \end{eqnarray*}

    where

    \begin{align} V^{'}(\varsigma) = B^{*'}(\theta)(\lambda_v+\delta_v(w^{1}\lambda_v+g^{1}\lambda_v))+(1-N_v^{*}(\theta))(\lambda_v+\delta_v(w^{1}\lambda_v+g^{1}\lambda_v))/\theta.\\ V^{''}(\varsigma) = -B^{*''}(\theta)(\lambda_{v}+\delta_v(w^{1}\lambda_v+g^{1}\lambda_v))^2-B^{*'}\delta_v((w^{2}+g^{2})-2(w^{1}g^{1}))+2B^{*'}(\lambda_v+\delta_v(w^{1}\lambda_v+g^{1}\lambda_v))^2/\theta\\ +2(1-B^{*}(\theta))(\lambda_v+\delta_v(w^{1}\lambda_v+g^{1}\lambda_v))^2/\theta^2-(1-B^{*}(\theta))\delta_v((w^{2}+g^{2})-2(w^{1}g^{1}))/\theta. \end{align}

    ⅱ) By differentiating Eq (3.65) with respect to \varsigma and evaluating it at \varsigma = 1 , we may find the number of consumers in the system, denoted as L_s .

    \begin{eqnarray} L_s = K_s^{'}(1) = \lim\limits_{{\varsigma}\rightarrow1}K_s^{'}(\varsigma) = P_0\left[\frac{Nu_s^{'''}(1)De_q^{''}(1)-De_q^{'''}(1)Nu_q^{''}(1)}{3(De_q^{''}(1))^2}\right]. \end{eqnarray} (4.9)

    Where

    \begin{align} Nu_s^{'''}(1) = Nu_q^{'''}(1)-\frac{6\lambda}{\theta}(1-f-r(1-A^{*}))(1-N_b^{*}(\theta))\lambda_v. \end{align}

    ⅲ) The average time of a consumer waiting in the system W_s = \frac{L_s}{\lambda_{eff}} and the average time of a consumer waiting in the orbit W_q = \frac{L_q}{\lambda_{eff}} is obtained by using the Little's formula.

    Where \lambda_{eff} = \lambda(I(1)+B_b(1)+D_b(1)+R_b(1))+\lambda_v(\phi_v(1)+D_v(1)+R_v(1)).

    To enhance the reliability of a system that is susceptible to breakdowns, it is important to decide on the reliability measurements of the model. These measures offer major details into the average server availability and other relevant indices.

    1) The server's mean availability (S_{Av}) is determined by

    \begin{eqnarray*} \nonumber S_{Av}& = &1-\lim\limits_{\varsigma\rightarrow 1}(D_{b}(\varsigma)+D_{v}(\varsigma)+\check{R}_{b}(\varsigma)+\check{R}_{v}(\varsigma)) = 1-(D_{b}(1)+D_{v}(1)+\check{R}_{b}(1)+\check{R}_{v}(1))\\ & = &1-\left\lbrace\begin{split} \nonumber \label{repair} &&P_{0}\delta\lambda_vE(N_b)(g^{(1)}+w^{1})\times \left\{\frac{\left(\begin{split} fN_v^{*}(\theta)+(N_v^{*}(\theta)-1)r(1-A^{*}(\lambda))+1-N_v^{*}(\theta)\\-(1-N_v^{*}(\theta))(-\lambda-\lambda\delta_v(w^{1}+(g^{1}))/\theta)\end{split}\right)}{1-f-r(1-A^{*}(\lambda)-E(N_b)(\lambda+\lambda\delta(w^{1}+g^{1})))}\right\} \\&&+\lambda_vP_0\delta_v\left( \frac{\left( 1-N_v^{*}(\theta)\right) }{\theta}\right) (w_v^{1}+g_v^{1})\end{split}\right\rbrace. \end{eqnarray*}

    2) The steady-state system failure occurrence in RS is as follows:

    \begin{eqnarray*} Fail_f = \delta*{\lambda_v P_{0}E(N_b)}\left\{\frac{\left(\begin{split} fN_v^{*}(\theta)+(N_v^{*}(\theta)-1)r(1-A^{*}(\lambda))+1-N_v^{*}(\theta)\\-(1-N_v^{*}(\theta))(-\lambda-\lambda\delta_v(w^{1}+(g^{1}))/\theta)\end{split}\right)}{1-f-r(1-A^{*}(\lambda)-E(N_b)(\lambda+\lambda\delta(w^{1}+g^{1})))}\right\}. \end{eqnarray*}

    Let the average length of the busy cycle and busy period H(T_{bs}) \; \text{and} H(T_{bc}) be taken using the following method:

    \begin{align} P_0 = \frac{H(T_0)}{H(T_0)+H(T_{bs})}, \; H(T_{bs}) = \frac{1}{\lambda}\bigg(\frac{1}{P_0}-1\bigg), \; \text{and}\; H(T_{bc})& = \frac{1}{(\lambda)P_0} = H(T_0)+H(T_{bs}), \end{align} (4.10)

    where the length of the system in the empty state is represented by T_0 , and H(T_{0}) = (1/\lambda) . By inserting Eq (3.53) in Eq (4.10), we obtain the anticipated outcome to be

    \begin{eqnarray*} H(T_{bs})& = &\frac{1}{\lambda}\left\lbrace\frac{ \begin{split} E(N_b)\lambda_v\left(\begin{split} fN_v^{*}(\theta)+(N_v^{*}(\theta)-1)r(1-A^{*}(\lambda))+1-N_v^{*}(\theta)\\-(1-N_v^{*}(\theta))(-\lambda-\lambda\delta_v(w^{1}+(g^{1}))/\theta)\end{split}\right) \times(1+\delta_v(w^{1}+g^{1}))\\+ \lambda_v\left(\begin{split} (1-f-r(1-A^{*}(\lambda)-E(N_b)(\lambda+\lambda\delta(w^{1}+g^{1}))))\\(1-N_v^{*}(\theta)/\theta)\end{split}\right) \times(1+\delta_v(w_v^{1}+g_v^{1})) \\+(f+(1-N_v^{*}(\theta))(\lambda+\delta(w^{1}\lambda+(g^{1}\lambda))))\times(E(N_b)+1/\theta)\\ \end{split} }{1-f-r(1-A^{*}(\lambda)-E(N_b)(\lambda+\lambda\delta(w^{1}+g^{1})))}\right\rbrace. \nonumber\\ H\left(T_{bc}\right)& = &\frac{\left\lbrace \begin{split} 1-f-r(1-A^{*}(\lambda)-E(N_b)(\lambda+\lambda\delta(w^{1}+g^{1})))\\+E(N_b)\lambda_v\left(\begin{split} fN_v^{*}(\theta)+(N_v^{*}(\theta)-1)r(1-A^{*}(\lambda))+1-N_v^{*}(\theta)\\-(1-N_v^{*}(\theta))(-\lambda-\lambda\delta_v(w^{1}+(g^{1}))/\theta)\end{split}\right) \times(1+\delta_v(w^{1}+g^{1}))\\+\lambda_v\left(\begin{split} (1-f-r(1-A^{*}(\lambda)-E(N_b)(\lambda+\lambda\delta(w^{1}+g^{1}))))\\(1-N_v^{*}(\theta)/\theta)\end{split}\right)\times(1+\delta_v(w_v^{1}+g_v^{1})) \\+(f+(1-N_v^{*}(\theta))(\lambda+\delta(w^{1}\lambda+(g^{1}\lambda))))\times(E(N_b)+1/\theta)\\ \end{split} \right\rbrace}{\left( 1-f-r(1-A^{*}(\lambda)-E(N_b)(\lambda+\lambda\delta(w^{1}+g^{1})))\right) }. \end{eqnarray*}

    In this section, we analyze certain specific cases of our model that coincide with the current research.

    Case (ⅰ) M/G/1 model with balking consumer in multiple WV mode. Choose \delta_v = \delta; \; r = 0;\; \lambda_v = \lambda, \; \text{and} \; \zeta = 0 . In this case, we get

    \begin{equation*} K_s(\varsigma) = \frac{P_0\left\{\begin{array}{r} b(1-\varsigma)\left[\left(\varsigma-((1-f)+f \varsigma)\left(A^*(\lambda)+\varsigma{1-A^*(\lambda)}\right) N_b^*\left(h_b(\varsigma)\right)\right)\left(1+\varsigma \lambda \theta^{-1} V(\varsigma)\right)\right. \\ \left.+\lambda_v \varsigma\left({(1-A^*(\lambda))}\left(-1+((1-f)+f \varsigma)\left(V(\varsigma) N_b^*\left(h_b(\varsigma)\right)+N_v^*\left(h_v(\varsigma)\right)\right)\right)\right)\right] \\ +\varsigma {(1-N_b^*\left(h_b(\varsigma)\right))}\left(\varsigma V(\varsigma)+\left(A^*(\lambda)+\varsigma (1-A^*(\lambda))\right)\left(-1+((1-f)+f \varsigma) N_v^*\left(h_v(\varsigma)\right)\right)\right) \end{array}\right\}}{b(1-\varsigma)\left(\varsigma-((1-f)+f \varsigma)\left(A^*(\lambda)+\varsigma {(1-A^*(\lambda))}\right) N_b^*\left(h_b(\varsigma)\right)\right)}. \end{equation*}

    This matches the outcome obtained by Rajadurai et al.[19].

    Case (ⅱ) No delayed repair and reneging. Let \zeta = r = 0, our approach can be simplified to a M^{X}/G/1 RQ with retrial feedback queue with Balking, WVs and VI. Here, K_{s}(\varsigma) was obtained as

    \begin{eqnarray*} K_s(\varsigma) = \frac{P_0\left\{\begin{split} b N_v \lambda (1-C(\varsigma))\left[\left(\varsigma-(f+(1-f)\varsigma)\left(A^*(\lambda)+V {(1-A^*(\lambda))}\right) N_b^*\left(h_b(\varsigma)\right)\right)\left(1+\varsigma \lambda_v V(\varsigma) \theta^{-1}\right)\right. \\ \left.+\lambda_v\left(\varsigma {(1-A^*(\lambda))}\left(-1+((1-f)+f\varsigma)\left(V(\varsigma) N_b^*\left(h_b(\varsigma)\right)+N_v^*\left(h_v(\varsigma)\right)\right)\right)\right)\right] \\ +\lambda_v N_v \varsigma {(1-N_b^*\left(h_b(\varsigma)\right))}\left(\varsigma V(\varsigma)+\left(A^*(\lambda)+\varsigma {(1-A^*(\lambda))}\right)\left(-1+((1-f)+f\varsigma) N_v^*\left(h_v(\varsigma)\right)\right)\right) \\ +\varsigma \alpha_v \lambda b V(\varsigma) \theta^{-1} {(1-N_v^*\left(b_v \lambda_v (1-C(\varsigma))\right))}\left(\begin{split}\varsigma-((1-f)\\+f\varsigma)\left(A^*(\lambda)+\varsigma {(1-A^*(\lambda))}\right) N_b^*\left(h_b(\varsigma)\right)\end{split}\right) \end{split}\right\}}{b b_v {\lambda (1-C(\varsigma))}\left(\varsigma-((1-f)+f\varsigma)\left(A^*(\lambda)+\varsigma {(1-A^*(\lambda))}\right) N_b^*\left(h_b(\varsigma)\right)\right)}. \end{eqnarray*}

    This matches the outcome obtained by Jain [41].

    Case (ⅲ) M/G/1 model without feedback, deleyed repair, and reneging. Choose \delta_v = 0, \; \delta = 0, \; r = 0, \; \text{and} \; f = 0 . In this case, we obtain:

    \begin{equation} \begin{aligned} K_s(\varsigma) = \frac{P_0\left\{\begin{array}{c} (1-\varsigma)\left[\left(\varsigma-((1-f)+f\varsigma)\left(A^*(\lambda)+\varsigma {(1-A^*(\lambda))}\right) N_b^*\left(h_b(\varsigma)\right)\right)\left(1+\frac{\varsigma V(\varsigma)}{\theta}\right)\right. \\ \left.+\varsigma\left((1-A^*(\lambda))\left(-1+((1-f)+f\varsigma)\left(V(\varsigma) N_b^*\left(h_b(\varsigma)\right)+N_v^*\left(h_v(\varsigma)\right)\right)\right)\right)\right] \\ +{(1-N_b^*\left(h_b(\varsigma)\right))}\left(\varsigma V(\varsigma)-\left(A^*(\lambda)+\varsigma {(1-A^*(\lambda))}\right) {(1-N_v^*\left(h_v(\varsigma)\right))}\right) \end{array}\right\}}{(1-\varsigma)\left(\varsigma-((1-f)+f \varsigma)\left(A^*(\lambda)+\varsigma {(1-A^*(\lambda))}\right) N_b^*\left(h_b(\varsigma)\right)\right)}. \end{aligned} \end{equation} (5.1)

    This matches the outcome obtained by Gao et al. [10].

    To design a retrial queueing system to perform cost analysis, the most effective course is to determine the optimal system parameters, such as the optimal mean service rate or the optimal number of servers. In this section, we discuss the optimal structure for the single-server feedback retrial queue with working vacations subject to server failure. We obtain the total expected cost function per unit time using the following definitions of cost elements ( C_{h}, C_{0}, C_{s}, \text{and}\; C_{a} ) and the cost structure:

    \begin{eqnarray*} TC& = &C_{h} L_{s}+C_{0} \frac{H(T_{bs})}{H(T_{bc})}+C_{s} \frac{1}{H(T_{bc})}+C_{a} \frac{H(T_{0})}{H(T_{bc})} \\& = &C_{h} L_{s}+C_{0} (1-P_{0})+C_{s} \lambda+C_{a} P_{0}. \end{eqnarray*}

    Where C_{h}, C_{0}, C_{s}, \text{and}\; C_{a} represent the holding costs per unit of time for each customer in the system, the cost per unit of time to keep the server in and on operations, the setup cost per busy cycle, and the startup cost per unit of time for setting up the server in order to begin providing the service, respectively. For the following values of the cost elements and other parameters, such as \lambda \& \lambda_{v} = 1, \dot{a} = 2, N_{b} = 6, N_{v} = 4, \theta = 2, \chi_v \& \chi_b = 5; \gamma_v \& \gamma_b = 3, f = 0.2, \delta_{v} \& \delta = 0.1, r = 0.2, C_{h} = 5, C_{o} = 80, C_{s} = 600, \text{and}\; C_{a} = 80 , we find the total expected cost per unit of time, TC = 440.4952 , assuming exponential retrial times, service times, working vacation times, and repair times. Furthermore, Tables 13 show the impacts of (C_h; C_o), (C_o; C_a), and (C_s; C_a) on the expected cost function, respectively. It is evident that when cost parameters increase, the expected cost function trends upward linearly. Similarly, we can conduct a sensitivity study on specific system parameters. After establishing the above-mentioned base values, one parameter can be changed at a time to calculate the appropriate objective function value. The graphs from Figures 24 display the effect of specific system parameters (\dot{a}, \lambda, N_v) on the overall expected cost per unit of time.

    Table 1.  The effect of (C_h, C_0) on the expected cost function TC with C_s = \$600 , and C_a = \$80 .
    ( C_h, C_{0} ) (5, 80) (5, 90) (5,100) (10, 80) (15, 80)
    TC 440.4952 444.3521 448.2090 440.9905 441.4857

     | Show Table
    DownLoad: CSV
    Table 2.  The effect of (C_0, C_a) on the expected cost function TC with C_h = \$5 , and C_s = \$600 .
    ( C_0, C_{a} ) (80, 80) (85, 80) (90, 80) (80, 90) (80, 95)
    TC 440.4952 442.4237 444.3521 446.6383 449.7099

     | Show Table
    DownLoad: CSV
    Table 3.  The effect of (C_a, C_s) on the expected cost function TC with C_h = \$5 , and C_0 = \$80 .
    ( C_a, C_{s} ) (80,600) (85,600) (90,600) (80,650) (80,700)
    TC 440.4952 443.5668 446.6383 470.4952 500.4952

     | Show Table
    DownLoad: CSV
    Figure 2.  TC \; \text{Versus} \; \dot{a} .
    Figure 3.  {TC \; \text{Versus} \; \dot{a}} .
    Figure 4.  TC Versus N_v .

    In this section, we will analyze the impact of numerous variables on the efficiency indicators of our system using various numerical demonstrations.

    The examples are predicated on the assumption that all instances of retrial, RS, periods of reduced service rate, delayed repair, and maintenance times follow an exponential distribution. Consequently, the parameters are chosen with arbitrary values satisfying the stability condition. The results are visually represented using MATLAB software. It is worth noting the equation of exponential distribution f(\hat{\upsilon}) = \nu e^{-\nu \hat{\upsilon}}, \hat{\upsilon} > 0 , Erlang-2 stage distribution f(\hat{\upsilon}) = \nu^{2} \hat{\upsilon} e^{-\nu \hat{\upsilon}}, \hat{\upsilon} > 0 , and the hyper exponential distribution f(\hat{\upsilon}) = c \nu e^{-\nu \hat{\upsilon}}+(1-c)\nu^{2}e^{-\nu^{2} \hat{\upsilon}}, \hat{\upsilon} > 0 .

    The data presented in Table 4 indicates that when the rate of repeated tries \dot{a} increases, there is a constant decrease in the prob. of the orbit size L_q and the prob. of the server being unoccupied, while the retrial time I decreases. Meanwhile, the prob. of the server being idle, denoted as P_0 , also rises. Regarding the given values \lambda_{v} \& \lambda = 1 ; \theta = 3 ; N_b = 8; r = 0.2 ; \chi_v \& \chi_b = 4 ; \gamma_v \& \gamma_b = 4 ; \delta_{v} \& \delta = 0.3 ; f = 0.2 ; N_v = 4 ; c = 0.5 . The impacts of the prob. on the system's performance metrics are described, and documented in Table 5 for the given values of \lambda_{v} \& \lambda = 2; \theta = 3; N_v = 4; N_b = 8; r = 0.2; \chi_v \& \chi_b = 4; \gamma_v \& \gamma_b = 4; \dot{a} = 5; f = 0.2 ; c = 0.5 . We have seen that the breakdown rate \delta increases consistently as the values of the orbit size prob. L_q , the server unoccupied rate P_0 , and the server idle rate during the retry period I increase. The patterns exhibited by the tables align with the expected assumptions.

    Table 4.  The impact of repeated attempt rate \dot{a} on P_O, L_q, I .
    Retrial rate Exponential Erlang 2 stage Hyper Exponential
    \dot{a} {{P_0}} {{L_q}} {{I}} {{P_0}} {{L_q}} {{I}} {{P_0}} {{L_q}} {{I}}
     
    3 0.5180 0.1588 0.0711 0.5786 0.6502 0.1042 0.5053 0.0914 0.0747
    4 0.5189 0.1377 0.0704 0.5799 0.5826 0.1027 0.5062 0.0736 0.0740
    5 0.5195 0.1240 0.0700 0.5807 0.5372 0.1017 0.5067 0.0633 0.0736
    6 0.5199 0.1143 0.0696 0.5814 0.5046 0.1009 0.507 0.0567 0.0733
    7 0.5202 0.1071 0.0694 0.5819 0.4801 0.1004 0.5072 0.0520 0.0732

     | Show Table
    DownLoad: CSV
    Table 5.  The impact of breakdown rate \delta on P_0, L_q, I. .
    Breakdown rate Exponential Erlang 2 stage Hyper Exponential
    \delta P_0 L_q I P_0 L_q I P_0 L_q I
     
    0.20 0.5206 0.1355 0.0813 0.5938 0.4257 0.0401 0.5359 0.0879 0.0761
    0.30 0.5180 0.1397 0.0780 0.5786 0.4478 0.0365 0.5341 0.0914 0.0747
    0.40 0.5154 0.1439 0.0748 0.5633 0.4636 0.0329 0.5323 0.0949 0.0733
    0.50 0.5128 0.1481 0.0716 0.5480 0.4744 0.0293 0.5305 0.0983 0.0719
    0.60 0.5102 0.1524 0.0684 0.5326 0.4809 0.0256 0.5287 0.1017 0.0705

     | Show Table
    DownLoad: CSV

    Specifically, in Table 6, when the lower service rate N_v escalates, the server idle rate P_0 and length of the orbit L_q also escalates, while the prob. that the server is idle during the retrial period I decreases, given the values of \lambda_{v} \& \lambda = 1; \delta_{v} \& \delta = 0.3; N_b = 8; r = 0.2; \chi_v \& \chi_b = 4; \gamma_v \& \gamma_b = 4; \dot a = 5; \theta = 3; f = 0.2 ; c = 0.5 . In Table 7, when the feedback rate f rises, then the average length of the orbit Lq, the server idle during retrial period I, and server idle P_0 declines, relating to the values \lambda_{v} \& \lambda = 2; \delta_{v} \& \delta = 0.3; N_b = 8; r = 0.2; \chi_v \& \chi_b = 4; \gamma_v \& \gamma_b = 4; \dot a = 5; \theta = 3 ; c = 0.5 . In Table 8, when the reneging rate r rises, the average length of the orbit Lq, the server idle during retrial period I, and the server idle P_0 decline, regarding the specified values \lambda_{v} \& \lambda = 1; \delta_{v} \& \delta = 0.3; N_b = 8; r = 0.2; \chi_v \& \chi_b = 4; \gamma_v \& \gamma_b = 4; \dot a = 5; \theta = 3 ; c = 0.5 .

    Table 6.  The impact of working vacation period N_v on P_0, L_q, I .
    Slower service rate Exponential Erlang 2 stage Hyper Exponential
    N_v P_0 L_q I P_0 L_q I P_0 L_q I
     
    4.5 0.5208 0.1479 0.0684 0.5798 0.6233 0.0353 0.5091 0.0834 0.0723
    5.5 0.5254 0.1309 0.0639 0.5821 0.5743 0.0332 0.5148 0.0755 0.0665
    6.5 0.5292 0.1184 0.0603 0.5840 0.5313 0.0314 0.5189 0.0702 0.0623
    7.5 0.5322 0.1088 0.0574 0.5856 0.4935 0.0298 0.5221 0.0664 0.0592
    8.5 0.5348 0.1013 0.0549 0.5870 0.4601 0.0285 0.5245 0.0636 0.0567

     | Show Table
    DownLoad: CSV
    Table 7.  The impact of feedback rate f on P_0, L_q, I. .
    Feedback rate Exponential Erlang 2 stage Hyper Exponential
    f P_0 L_q I P_0 L_q I P_0 L_q I
     
    0.2 0.5100 0.1397 0.0780 0.5786 0.4788 0.0365 0.5061 0.0914 0.0747
    0.3 0.4715 0.1541 0.0966 0.5372 0.5571 0.0439 0.4662 0.1069 0.0950
    0.4 0.4292 0.1703 0.1173 0.4919 0.6791 0.0520 0.4223 0.1256 0.1173
    0.5 0.3825 0.1891 0.1404 0.4418 0.9021 0.0609 0.3739 0.1494 0.1420
    0.6 0.3308 0.2114 0.1664 0.3864 1.4686 0.0708 0.3201 0.1821 0.1693

     | Show Table
    DownLoad: CSV
    Table 8.  The impact of reneging rate r on P_0, L_q, I .
    Reneging rate Exponential Erlang 2 stage Hyper Exponential
    r P_0 L_q I P_0 L_q I P_0 L_q I
     
    0.2 0.4499 0.1588 0.0711 0.5786 0.6502 0.0365 0.5053 0.0914 0.0747
    0.3 0.4489 0.1669 0.0729 0.5747 0.6894 0.0381 0.5036 0.0965 0.0761
    0.4 0.4478 0.1756 0.0748 0.5705 0.7361 0.0399 0.5019 0.1019 0.0775
    0.5 0.4467 0.1850 0.0768 0.5659 0.7927 0.0419 0.5001 0.1076 0.0789
    0.6 0.4454 0.1951 0.0790 0.5608 0.8627 0.0440 0.4982 0.1135 0.0804

     | Show Table
    DownLoad: CSV

    Figures 511 depict the influence of the variables \lambda_{P}, \; \lambda_{N}, \; r, \; \dot a, \; \theta, \; N_b, \; \text{and}\; N_v on the 3D graph based on system performance metrics.

    Figure 5.  P_0 \; \text{Versus} \; N_{b} \; \text{and} \; N_{v} .
    Figure 6.  L_q \; \text{Versus} \; \dot{a} \; \text{and} \; N_{v} .
    Figure 7.  L_q \; \text{Versus} \; \lambda\; \text{and} \; \delta .
    Figure 8.  L_q \; \text{Versus} \; N_b \; \text{and} \; \zeta .
    Figure 9.  P_0 \; \text{Versus} \; \theta \; \text{and} \; \dot{a} .
    Figure 10.  P_0 \; \text{Versus} \; r \; and \; f .
    Figure 11.  L_q \;\text{Versus} \; N_{b}\; \text{and} \; N_v .

    In Figure 5, the server's idle rate P_0 increases when both the lower service rate N_v and the rate of retrial N_b increase. Figure 6 demonstrate that the queue length L_q lowers when the retry rate \dot{a} and the rate of the working vacation N_v increase. In Figure 7, the length of the queue L_q increases as both the arrival rate \lambda and the breakdown rate with RS \delta increase. In Figure 8, the length of the queue L_q increases as the server's rate of service N_b increases and the rate of delay \zeta drops consistently. In Figure 9, the queue length L_q drops as the server's rate of service \theta grows and the rate of retry \dot{a} also increases. In Figure 10, the server idle rate P_0 drops as the feedback rate f and the reneging rate r grow. In Figure 11, the length of the queue L_q lowers as the server's rate of N_b and the rate of the slow service N_v constantly grow.

    Figures 1215 illustrate the impact of the variables \lambda_{P}, \; \lambda_{N}, \; r, \; \dot a, \; \theta, \; N_b, \; \text{and}\; N_v on the 2D graph based on system performance metrics. Note that the exponential distribution is f(\hat{\upsilon}) = \nu e^{-\nu \hat{\upsilon}}, \hat{\upsilon} > 0 , Erlang-2 stage distribution is f(\hat{\upsilon}) = \nu^{2} \hat{\upsilon} e^{-\nu \hat{\upsilon}}, \hat{\upsilon} > 0 , and the hyper-exponential distribution is f(\hat{\upsilon}) = c \nu e^{-\nu \hat{\upsilon}}+(1-c) \nu^{2} e^{-\nu^{2} \hat{\upsilon}}, \hat{\upsilon} > 0 . Figure 12 demonstrates a positive correlation between the increase in the slow service rate N_v and the growth of the prob. of server idle rate P_0 . The graph in Figure 13 illustrates that the prob. of the server being idle, denoted as P_0 , decreases as the feedback rate, represented by f , increases. Figure 14 illustrates that the average size of orbit L_q exhibits an upward trend as the reneging rate r increases. Figure 15 illustrates that the average size of orbit Lq decreases as the repair rate \zeta increases. The following data visualizations illustrate the influence of the attributes on the system.

    Figure 12.  P_0 \; \text{Versus}\; N_v.
    Figure 13.  P_0 \; \text{Versus} \; f.
    Figure 14.  L_q \; \text{Versus} \; r .
    Figure 15.  L_q \; \text{Versus} \; \zeta .

    The proposed retrial queueing model incorporating working vacations, breakdowns, and repairs offers a comprehensive framework for analyzing service systems. However, like any model, it possesses inherent limitations and underlying assumptions that can influence the interpretation of the results.

    The model frequently assumes that the retrial queue has an endless capacity. This makes analysis easier but might not accurately represent real-world situations. In reality, users may completely give up on the system if they believe that wait times are too lengthy. This may cause the model's average waiting times to increase. Usually, the model focuses on a system with just one server. This can be helpful for tractability, but in multi-server setups, complicated relationships and problems with resource allocation occur. The model may assume that service hours, arrival rates, and other related variables are fixed. However, demand and service requirements fluctuate in real-world systems. We can establish the suggested approach results in a more effective system with shorter wait times for customers, better server utilization, and less downtime from malfunctions by looking at these measures. In the proposed model, we can further extend the concepts of bulk arrival queue, optional phase service, and N-policy.

    In this study, we have analyzed a non-Markovian feedback retrial queue, reneging, delayed repair, and working vacation subject to server breakdown, along with server failures that occur during service time for consumers in both normal service mode and working vacation mode. The PGFs for the number of consumers in the system during various states such as server unoccupied, occupied, on WV, delayed repair, and under repair have been obtained by using the SVT approach. Furthermore, the explicit expression for the mean queue length of both the orbit and the system was derived. We examined the probabilities of the system states and discussed some significant special cases. The utilization of numerical results in sensitivity analysis can assist decision-makers in analyzing the functioning of the system in various circumstances. Also, decision-makers and system developers in the relevant industries and service organizations will use the results of the cost analysis to help them make the best and most economical choices regarding the upgrading and capacity expansion of the service systems in consideration. The inspiration for this model comes from its widespread use in real-life systems, such as computer and telephone networks, where a single server handles messages while utilizing the working vacation policy.

    Sundarapandiyan S.: Study design, conceptualization, methodology, formal analysis, visualization, software and writing-original draft. Nandhini S.: Study design, conceptualization, validation, methodology, formal analysis, visualization, software and supervision. All authors have read and approved the final version of the manuscript for publication.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    The authors declare that there is no conflict of interest regarding the publication of this manuscript.



    [1] J. M. Mendel, Uncertain rule-based fuzzy systems, Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-51370-6
    [2] H. Hagras, C. Wagner, Towards the wide spread use of type-2 fuzzy logic systems in real world applications, IEEE Comput. Intell. M., 7 (2012), 14–24. https://doi.org/10.1109/MCI.2012.2200621 doi: 10.1109/MCI.2012.2200621
    [3] J. M. Mendel, Type-2 fuzzy sets and systems: An overview, IEEE Comput. Intell. M., 2 (2007), 20–29. https://doi.org/10.1109/MCI.2007.380672 doi: 10.1109/MCI.2007.380672
    [4] M. H. F. Zarandi, B. Rezaee, I. B. Turksen, E. Neshat, A type-2 fuzzy rule-based expert system model for stock price analysis, Expert Syst. Appl., 36 (2009), 139–154. https://doi.org/10.1016/j.eswa.2007.09.034 doi: 10.1016/j.eswa.2007.09.034
    [5] Y. Chen, D. Z. Wang, S. C. Tong, Forecasting studies by designing Mamdani interval type-2 fuzzy logic systems: with combination of BP algorithms and KM algorithms, Neurocomputing, 174 (2016), 1133–1146. https://doi.org/10.1016/j.neucom.2015.10.032 doi: 10.1016/j.neucom.2015.10.032
    [6] A. Khosravi, S. Nahavandi, Load forecasting using interval type-2 fuzzy logic systems: Optimal type reduction, IEEE T. Ind. Electron., 10 (2014), 1055–1063. https://doi.org/10.1109/TII.2013.2285650 doi: 10.1109/TII.2013.2285650
    [7] M. Biglarbegian, W. W. Melek, J. M. Mendel, Design of novel interval type-2 fuzzy controllers for modular and reconfigurable robots: theory and experiments, IEEE T. Ind. Electron., 58 (2011), 1371–1384. https://doi.org/10.1109/TIE.2010.2049718 doi: 10.1109/TIE.2010.2049718
    [8] D. Z. Wang, Y. Chen, Study on permanent magnetic drive forecasting by designing Takagi Sugeno Kang type interval type-2 fuzzy logic systems, T. I. Meas. Control, 40 (2018), 2011–2023. https://doi.org/10.1177/0142331217694682 doi: 10.1177/0142331217694682
    [9] R. S. Rama, P. Latha, An effective torque ripple reduction for permanent magnet synchronous motor using ant colony optimization, Int. J. Fuzzy Syst., 17 (2015), 577–584. https://doi.org/10.1007/s40815-015-0077-5 doi: 10.1007/s40815-015-0077-5
    [10] O. Linda, M. Manic, Interval type-2 voter design for fault tolerant systems, Inform. Sci., 181 (2011), 2933–2950. https://doi.org/10.1016/j.ins.2011.03.008 doi: 10.1016/j.ins.2011.03.008
    [11] A. Niewiadomski, On finity, countability, cardinalities, and cylindric extensions of type-2 fuzzy sets in linguistic summarization of databases, IEEE T. Fuzzy Syst., 18 (2010), 532–545. https://doi.org/10.1109/TFUZZ.2010.2042719 doi: 10.1109/TFUZZ.2010.2042719
    [12] D. R. Wu, J. M. Mendel, Uncertainty measures for interval type-2 fuzzy sets, Inform. Sci., 177 (2007), 5378–2393. https://doi.org/10.1016/j.ins.2007.07.012 doi: 10.1016/j.ins.2007.07.012
    [13] A. Khosravi, S. Nahavandi, D. Creighton, D. Srinivasan, Interval type-2 fuzzy logic systems for load forecasting: a comparative study, IEEE T. Power Syst., 27 (2012), 1274–1282. https://doi.org/10.1109/TPWRS.2011.2181981 doi: 10.1109/TPWRS.2011.2181981
    [14] Y. Chen, Study on weighted Nagar-Bardini algorithms for centroid type-reduction of interval type-2 fuzzy logic systems, J. Intell. Fuzzy Syst., 34 (2018), 2417–2428. https://doi.org/10.3233/JIFS-171669 doi: 10.3233/JIFS-171669
    [15] J. M. Mendel, On KM algorithms for solving type-2 fuzzy sets problems, IEEE T. Fuzzy Syst., 21 (2013), 426–446. https://doi.org/10.1109/TFUZZ.2012.2227488 doi: 10.1109/TFUZZ.2012.2227488
    [16] T. Kumbasar, Revisiting Karnik-Mendel algorithms in the framework of linear fractional programming, Int. J. Approx. Reason., 82 (2017), 1–21. https://doi.org/10.1016/j.ijar.2016.11.019 doi: 10.1016/j.ijar.2016.11.019
    [17] J. M. Mendel, F. L. Liu, Super-exponential convergence of the Karnik-Mendel algorithms for computing the centroid of an interval type-2 fuzzy set, IEEE T. Fuzzy Syst., 15 (2007), 309–320. https://doi.org/10.1109/TFUZZ.2006.882463 doi: 10.1109/TFUZZ.2006.882463
    [18] D. R. Wu, J. M. Mendel, Perceptual reasoning for perceptual computing: a similarity based approach, IEEE T. Fuzzy Syst., 17 (2009), 1397–1411. https://doi.org/10.1109/TFUZZ.2009.2032652 doi: 10.1109/TFUZZ.2009.2032652
    [19] D. R. Wu, J. M. Mendel, Enhanced Karnik-Mendel algorithms, IEEE T. Fuzzy Syst., 17 (2009), 923–934. https://doi.org/10.1109/TFUZZ.2008.924329 doi: 10.1109/TFUZZ.2008.924329
    [20] X. W. Liu, J. M. Mendel, D. R. Wu, Study on enhanced Karnik-Mendel algorithms: initialization explanations and computation improvements, Inform. Sci., 184 (2012), 75–91. https://doi.org/10.1016/j.ins.2011.07.042 doi: 10.1016/j.ins.2011.07.042
    [21] Y. Chen, Study on sampling-based discrete noniterative algorithms for centroid type-reduction of interval type-2 fuzzy logic systems, Soft Comput., 24 (2020), 11819–11828. https://doi.org/10.1007/s00500-020-04998-2 doi: 10.1007/s00500-020-04998-2
    [22] O. Castillo, P. Melin, E. Ontiveros, C. Peraza, P. Ochoa, F. Valdez, et al., A high-speed interval type 2 fuzzy system approach for dynamic parameter adaptation in metaheuristics, Eng. Appl. Artif. Intell., 85 (2019), 666–680. https://doi.org/10.1016/j.engappai.2019.07.020 doi: 10.1016/j.engappai.2019.07.020
    [23] G. M. Méndez, M. D. L. A. Hernandez, Hybrid learning mechanism for interval A2-C1 type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems, Inform. Sci., 220 (2013), 149–169. https://doi.org/10.1016/j.ins.2012.01.024 doi: 10.1016/j.ins.2012.01.024
    [24] J. M. Mendel, Type-2 fuzzy sets and systems: an overview, IEEE Comput. Intell. Mag., 2 (2007), 20–29. https://doi.org/10.1109/MCI.2007.380672 doi: 10.1109/MCI.2007.380672
    [25] Y. Chen, D. Z. Wang, Study on centroid type-reduction of general type-2 fuzzy logic systems with weighted enhanced Karnik-Mendel algorithms, Soft Comput., 22 (2018), 1361–1380. https://doi.org/10.1007/s00500-017-2938-3 doi: 10.1007/s00500-017-2938-3
    [26] Y. Chen, D. Z. Wang, Study on centroid type-reduction of general type-2 fuzzy logic systems with weighted Nie-Tan algorithms, Soft Comput., 22 (2018), 7659–7678. https://doi.org/10.1007/s00500-018-3551-9 doi: 10.1007/s00500-018-3551-9
    [27] D. R. Wu, Approaches for reducing the computational cost of interval type-2 fuzzy logic systems: overview and comparisons, IEEE T. Fuzzy Syst., 21 (2013), 80–99. https://doi.org/10.1109/TFUZZ.2012.2201728 doi: 10.1109/TFUZZ.2012.2201728
    [28] T. Wang, Y. Chen, S. C. Tong, Fuzzy reasoning models and algorithms on type-2 fuzzy sets, Int. J. Innov. Comput. Inform. Control, 24 (2008), 2451–2460.
    [29] J. W. Li, R. John, S. Coupland, G. Kendall, On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets, IEEE T. Fuzzy Syst., 26 (2018), 1036–1039. https://doi.org/10.1109/TFUZZ.2017.2666842 doi: 10.1109/TFUZZ.2017.2666842
    [30] S. Greenfield, F. Chiclana, Accuracy and complexity evaluation of defuzzification strategies for the discretised interval type-2 fuzzy set, Int. J. Approx. Reason., 54 (2013), 1013–1033. https://doi.org/10.1016/j.ijar.2013.04.013 doi: 10.1016/j.ijar.2013.04.013
    [31] E. Ontiveros-Robles, P. Melin, O. Castillo, New methodology to approximate type-reduction based on a continuous root-finding karnik mendel algorithm, Algorithms, 10 (2017), 77–96. https://doi.org/10.3390/a10030077 doi: 10.3390/a10030077
    [32] Y. Chen, J. X. Wu, J. Lan, Study on reasonable initialization enhanced Karnik-Mendel algorithms for centroid type-reduction of interval type-2 fuzzy logic systems, AIMS Math., 5 (2020), 6149–6168. https://doi.org/10.3934/math.2020395 doi: 10.3934/math.2020395
    [33] J. M. Mendel, X. W. Liu, Simplified interval type-2 fuzzy logic systems, IEEE T. Fuzzy Syst., 21 (2013), 1056–1069. https://doi.org/10.1109/TFUZZ.2013.2241771 doi: 10.1109/TFUZZ.2013.2241771
    [34] M. A. Khanesar, A. Jalalian, O. Kaynak, H. Gao, Improving the speed of center of sets type reduction in interval type-2 fuzzy systems by eliminating the need for sorting, IEEE T. Fuzzy Syst., 25 (2017), 1193–1206. https://doi.org/10.1109/TFUZZ.2016.2602392 doi: 10.1109/TFUZZ.2016.2602392
    [35] J. M. Mendel, General type-2 fuzzy logic systems made simple: A tutorial, IEEE T. Fuzzy Syst., 22 (2014), 1162–1182. https://doi.org/10.1109/TFUZZ.2013.2286414 doi: 10.1109/TFUZZ.2013.2286414
    [36] C. H. Hsu, C. F. Juang, Evolutionary robot wall-following control using type-2 fuzzy controller with species-de-activated continuous ACO, IEEE T. Fuzzy Syst., 21 (2013), 100–112. https://doi.org/10.1109/TFUZZ.2012.2202665 doi: 10.1109/TFUZZ.2012.2202665
    [37] Y. Chen, D. Z. Wang, W. Ning, Forecasting by TSK general type-2 fuzzy logic systems optimized with genetic algorithms, Optim. Control Appl. Method., 39 (2018), 393–409. https://doi.org/10.1002/oca.2353 doi: 10.1002/oca.2353
    [38] F. Gaxiola, P. Melin, F. Valdez, J. R. Castro, O. Castillo, Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO, Appl. Soft Comput., 38 (2016), 860–871. https://doi.org/10.1016/j.asoc.2015.10.027 doi: 10.1016/j.asoc.2015.10.027
    [39] Y. Chen, D. Z. Wang, Forecasting by general type-2 fuzzy logic systems optimized with QPSO algorithms, Int. J. Control Autom. Syst., 15 (2017), 2950–2958. https://doi.org/10.1007/s12555-017-0793-0 doi: 10.1007/s12555-017-0793-0
    [40] Y. Maldonado, O. Castillo, P. Melin, Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications, Appl. Soft Comput., 13 (2013), 496–508. https://doi.org/10.1016/j.asoc.2012.08.032 doi: 10.1016/j.asoc.2012.08.032
    [41] E. Ontiveros-Robles, P. Melin, O. Castillo, Comparative analysis of noise robustness of type 2 fuzzy logic controllers, Kybernetika, 54 (2018), 175–201. https://doi.org/10.14736/kyb-2018-1-0175 doi: 10.14736/kyb-2018-1-0175
    [42] L. Cervantes, O. Castillo, Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control, Inform. Sci., 324 (2015), 247–256. https://doi.org/10.1016/j.ins.2015.06.047 doi: 10.1016/j.ins.2015.06.047
    [43] O. Castillo, L. Amador-Angulo, J. R. Castro, M. Garcia-Valdez, A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems, Inform. Sci., 354 (2016), 257–274. https://doi.org/10.1016/j.ins.2016.03.026 doi: 10.1016/j.ins.2016.03.026
    [44] C. W. Tao, J. S. Taur, C. W. Chang, Y. H. Chang, Simplified type-2 fuzzy sliding controller for wing rocket system, Fuzzy Set. Syst., 207 (2012), 111–129. https://doi.org/10.1016/j.fss.2012.02.015 doi: 10.1016/j.fss.2012.02.015
    [45] D. R. Wu, J. M. Mendel, Recommendations on designing practical interval type-2 fuzzy systems, Eng. Appl. Artif. Intell., 85 (2019), 182–193. https://doi.org/10.1016/j.engappai.2019.06.012 doi: 10.1016/j.engappai.2019.06.012
    [46] S. C. Tong, Y. M. Li, Observer-based adaptive fuzzy backstepping control of uncertain pure-feedback systems, Sci. China Inform. Sci., 57 (2014), 1–14. https://doi.org/10.1007/s11432-013-5043-y doi: 10.1007/s11432-013-5043-y
    [47] M. Deveci, I. Z. Akyurt, S. Yavuz, GIS-based interval type-2 fuzzy set for public bread factory site selection, J. Enterp. Inform. Manag., 31 (2018), 820–847. https://doi.org/10.1108/JEIM-02-2018-0029 doi: 10.1108/JEIM-02-2018-0029
    [48] S. C. Tong, Y. M. Li, Robust adaptive fuzzy backstepping output feedback tracking control for nonlinear system with dynamic uncertainties, Sci. China Inform. Sci., 53 (2010), 307–324. https://doi.org/10.1007/s11432-010-0031-y doi: 10.1007/s11432-010-0031-y
    [49] F. Y. Wang, H. Mo, Some fundamental issues on type-2 fuzzy sets, Acta Autom. Sin., 43 (2017), 1114–1141.
    [50] H. Mo, F. Y. Wang, M. Zhou, R. Li, Z. Xiao, Footprint of uncertainty for type-2 fuzzy sets, Inform. Sci., 272 (2014), 96–110. https://doi.org/10.1016/j.ins.2014.02.092 doi: 10.1016/j.ins.2014.02.092
    [51] Y. Chen, J. X. Yang, Study on center-of-sets type-reduction of interval type-2 fuzzy logic systems with noniterative algorithms, J. Intell. Fuzzy Syst., 40 (2021), 11099–11106. https://doi.org/10.3233/JIFS-202264 doi: 10.3233/JIFS-202264
    [52] X. Tao, J. Yi, Z. Pu, T. Xiong, Robust adaptive tracking control for hypersonic vehicle based on interval type-2 fuzzy logic system and small-gain approach, IEEE T. Cybernetics, 51 (2021), 2504–2517. https://doi.org/10.1109/TCYB.2019.2927309 doi: 10.1109/TCYB.2019.2927309
    [53] Y. Chen, J. X. Yang, Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification, T. I. Meas. Control, 43 (2021), 2780–2787. https://doi.org/10.1177/01423312211006635 doi: 10.1177/01423312211006635
    [54] L. Wu, F. Qian, L. Wang, X. Ma, An improved type-reduction algorithm for general type-2 fuzzy sets, Inform. Sci., 2022.
    [55] Y. Chen, Study on weighted-based noniterative algorithms for computing the centroids of general type-2 fuzzy sets, Int. J. Fuzzy Syst., 24 (2022), 587–606. https://doi.org/10.1007/s40815-021-01166-y doi: 10.1007/s40815-021-01166-y
    [56] C. Chen, D. Wu, J. M. Garibaldi, R. I. John, J. Twycross, J. M. Mendel, A comprehensive study of the efficiency of type-reduction algorithms, IEEE T. Fuzzy Syst., 29 (2020), 1556–1566. https://doi.org/10.1109/TFUZZ.2020.2981002 doi: 10.1109/TFUZZ.2020.2981002
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