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PH/PH/1 Bulk Arrival and Bulk Service Queue with Randomly Varying Environment

PH/PH/1 Bulk Arrival and Bulk Service Queue with Randomly Varying Environment

versions of the PH distributions of the new environment. The stationary version of the distribution for residual time has been well explained in Qi-Ming He [12] where it is named as equilibrium PH distribution. Randomly varying environment PH/PH/1 queue models with bulk arrival and bulk service have not been treated so far at any depth. In this paper the partitioning of the matrix is carried out in a way that the stationary probability vector exhibits a matrix geometric structure for PH/PH/1 bulk queues with random environment where the arrivals and service sizes are finite. Two models (A) and (B) on PH/PH/1 bulk queue systems with infinite storage space for customers are studied using the block partitioning method. Model (A) presents the case when M, the maximum of the arrival sizes is bigger than N, the maximum of the service sizes. In Model (B), its dual case N is bigger than M, is treated. In general in Queue models, the state space of the system has the first co-ordinate indicating the number of customers in the system but here the customers in the system are grouped and considered as members of blocks of sizes of the maximum for finding the rate matrix. Using the maximum of the bulk arrival size or the maximum of the bulk service size and grouping the customers as members of blocks in addition to coordinates of the arrival and service phases for the partitioning the infinitesimal generator is a new approach in this area. The matrices appearing as the basic system generators in these two models due to block partitioned structure are seen as block circulant matrices. The paper is organized in the following manner. In sections II and III the stationary probability of the number of customers waiting for service, the expectation and the variance and the probability of empty queue are derived for these Models (A) and (B). In section IV numerical cases are presented to illustrate them.

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Performance analysis of discrete time bulk service queuing model nb (l,k)/geo/1

Performance analysis of discrete time bulk service queuing model nb (l,k)/geo/1

Chaudhry and Templeton [1] have presented extensive discussions of bulk service systems that operate according to a rule admitting non-accessible batch. Mathias [6] studies a inter-departure time distribution for batches and studies correlation between inter- departure time and batch sizes. Mathias and Alexander [5] provide an discrete-time analysis in the performance evaluation of manufacturing systems. Sivasamy and Elangovan [11] on the other hand, the batch, which entries service queue with accessible and non-accessible batches. Goswami, et al., [3] discrete-time bulk-service queues with accessible and non-accessible batches. Sivasamy and Pukazhenthi [12] have carried out and analyzed the discrete time bulk service queue for the accessible batch with the arrivals time being geometrical distribution and services time being negative binomial distribution. Vijaya Laxmi Pikala, et.al., [13] have studied the discrete-time renewal input bulk service queue with changeover time. Pukazhenthi and Ezhilvanan [10] discussed the discrete time queue length distribution with a bulk service rule. Daniel Wei-Chung Miao1, et al., [2] computational analysis of a markovian queueing system with geometric mean-reverting arrival process. Pukazhenthi and Ezhilvanan [8] the analysis of

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PH/PH/1 Bulk Arrival and Bulk Service Queue

PH/PH/1 Bulk Arrival and Bulk Service Queue

sold in various bulk sizes depending on market requirements. Noam Paz and Uri Yechali [9] have studied M/M/1 queue with disaster. Usually bulk arrival models have M/G/1 upper- Heisenberg block matrix structure. The decomposition of a Toeplitz sub matrix of the infinitesimal generator is required to find the stationary probability vector as done in William J. Stewart [10] and even in such models the recurrence relation method to find the stationary probabilities is stopped at certain level in most general cases indicating limitations of such approach. For M/M/1 bulk queues with random environment models one may refer Rama Ganesan, Ramshankar and Ramanarayanan [11]. In this paper the partitioning of the matrix is carried out in a way that the stationary probability vector exhibits a matrix geometric structure for PH/PH/1 bulk queues where the arrivals and service sizes are finite. Two models (A) and (B) on PH/PH/1 bulk queue systems with infinite storage space for customers are studied here using the block partitioning method. In the models considered here, the maximum arrival sizes and the maximum service sizes are different. Model (A) presents the case when M, the maximum of arrival sizes is bigger than N, the maximum of the sales sizes. In Model (B), its dual case N is bigger than M, is treated. In general in Queue models, the state space of the system has the first co-ordinate indicating the number of customers in the system but here the customers in the system are grouped and considered as members of M sized blocks of customers when M >N and N sized blocks of customers when N > M for finding the rate matrix. Using the maximum of the bulk arrival size or the maximum of the bulk service size and grouping the customers as members of the blocks for the partitioning the infinitesimal generator is a new approach in this area. The matrices appearing as the basic system generators in these two models due to block partitioned structure are seen as a block circulant matrices. The stationary probability of the number of customers waiting for service, the expectation and the variance and the probability of empty queue are derived for these models. Numerical cases are presented to illustrate them.

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M/M/C Bulk Arrival And Bulk Service Queue With Randomly Varying Environment

M/M/C Bulk Arrival And Bulk Service Queue With Randomly Varying Environment

are very common. Manufactured products arrive in bulk sizes and several bulk sizes of products are sold in markets. Recently M/M/1 queue system with disaster has been studied by Noam Paz and Uri Yechali [9] but random arrival size or random service size with varying environments is not studied. Usually the partitions of the bulk arrival models have M/G/1 upper-Heisenberg block matrix structure with zeros below the first sub diagonal. The decomposition of a Toeplitz sub matrix of the infinitesimal generator is required to find the stationary probability vector. Matrix geometric structures have not been noted so far as mentioned by William J. Stewart [10]. But in this paper the partitioning of the matrix is carried out in a way that the stationary probability vectors have a Matrix Geometric solution or a Modified Matrix Geometric solution for infinite capacity C server bulk arrival and bulk service queues with randomly varying environments.

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Performance analysis of a discrete time queuing model with bulk service policy

Performance analysis of a discrete time queuing model with bulk service policy

and service-times are assumed to be independent and geometrically distributed. Sivasamy and Pukazhenthi [12] has analyzed the discrete time bulk service queue with accessible batch with the arrivals and service times as general and negative binomially distribution. An interesting paper given by Baburaj [1] and he deals with the concept of a discrete time bulk service queue under the policy '( , , )′. The inter-arrival times are assumed to be independent and geometrically distributed. Discrete-time renewal input bulk service queue with changeover time studies has been discussed by Vijaya Laxmi Pikala. et.al., [14]. In this paper they consider a model such that a discrete-time infinite buffer renewal input single server queue with changeover time under ′(a, c, b)′ policy. The service and changeover time are geometrically distributed. The server begins service if there are at least c customers in the queue and the services are performed in batches of minimum size a and maximum size b (a ≤ c ≤ b). Pukazhenthi and Ezhilvanan [10] discussed the discrete time queue length distribution with a bulk service rule. Pukazhenthi and Ezhilvanan [11] recently used the analysis of discrete time queues with single server using correlated times.

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BMAP/M/C Bulk Service Queue with Randomly Varying Environment

BMAP/M/C Bulk Service Queue with Randomly Varying Environment

distribution after the arrival. Model (A) presents the case when M, the maximum of all the maximum arrival sizes in the environments is bigger than N, the maximum of all the maximum service sizes in all the environments. In Model (B), its dual, N is bigger than M, is treated. In general in Queue models, the state space of the system has the first co-ordinate indicating the number of customers in the system but here the customers in the system are grouped and considered as members of M sized blocks of customers (when M >N) or N sized blocks of customers (when N > M) for finding the rate matrix. For the C server system under consideration, Model (A) gives three cases namely (A1) M > N ≥ C, (A2) M ≥ C > N and (A3) C > M > N and Model (B) gives two cases namely (B1) N ≥ C, and (B2) C > N. The case M=N with various C values can be treated using Model (A) or Model (B). The matrices appearing as the basic system generators in these models due to block partitions are seen as block circulant matrices. The stationary probability of the number of customers waiting for service, the expected queue length, the variance and the probability of empty queue are derived for these models. Numerical cases are presented to illustrate their applications. The paper is organized in the following manner. In section II and section III the BMAP/M/C bulk service queues with randomly varying environment in which maximum arrival size M is greater than maximum service size N and the maximum arrival size M less than the maximum service size N are studied respectively with their sub cases. In section IV numerical cases are presented.

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Analysis of FM/M (a,b)/1/MWV Queueing Model

Analysis of FM/M (a,b)/1/MWV Queueing Model

terms of crisp value for FM/M(a,b)/1 under multiple working vacations with fuzzy numbers. The bulk service rule is applied. The most general bulk service rule is introduced by Neuts[14]. The batches are served according to FCFS discipline. By considering the arrival rate, service rate for busy period, service rate for vacation period, exponential distribution of vacation parameters are as trapezoidal fuzzy numbers and the basic idea is to convert all these fuzzy numbers into crisp values by applying Robust Ranking Technique. Further Robust Ranking technique is used to find the expected mean queue length. Moreover, the analytical results are numerically illustrated for L q , P v and P busy under

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M x/G(a,b)/1 With Modified Vacation, Variant Arrival Rate With Restricted Admissability Of Arriving Batches And Close Down

M x/G(a,b)/1 With Modified Vacation, Variant Arrival Rate With Restricted Admissability Of Arriving Batches And Close Down

ABSTRACT : In this paper, a bulk arrival general bulk service queuing system with modified M-vacation policy, variant arrival rate under a restricted admissibility policy of arriving batches and close down time is considered. During the server is in non- vacation, the arrivals are admitted with probability with ' α ' whereas, with probability ' β ' they are admitted when the server is in vacation. The server starts the service only if at least ‘a’ customers are waiting in the queue, and renders the service according to the general bulk service rule with minimum of ‘a’ customers and maximum of ‘b’ customers. At the completion of service, if the number of waiting customers in the queue is less than ‘𝑎’ then the server performs closedown work , then the server will avail of multiple vacations till the queue length reaches a consecutively avail of M number of vacations, After completing the Mth vacation, if the queue length is still less than a then the server remains idle till it reaches a. The server starts the service only if the queue length b ≥ a. It is considered that the variant arrival rate dependent on the state of the server.

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Analysis of Bulk Queueing System of Variant Threshold for Multiple Vacations, Restricted Admissiblity of Arriving Batches and Setup

Analysis of Bulk Queueing System of Variant Threshold for Multiple Vacations, Restricted Admissiblity of Arriving Batches and Setup

A bulk arrival general bulk service queueing with variant threshold policies for secondary jobs is analyzed. The probability generating function for queue size at an arbitrary epoch is derived. Various performance measures are also obtained. Some particular cases are also discussed.

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Markov denumerable process and queue theory

Markov denumerable process and queue theory

state 0, which means that the arrival distribution is arbitrary when nobody is in the queue. Then A. Y. Chen and E. Renshaw (1997) (2004) established the possibility to let the queue system idle(i.e. nobody is in the queue) at any time. However, all the models discussed before are only the simple queue (i.e. only one person arrives or leaves at the same moment), which eliminates the cases such practical cases as the waiting for the lift and the arrival of the passengers in the aircraft. Since M. F. Neuts (1979) introduced versatile Markovian arrival processes by using several kinds of batch-arrival process and M. L. Chaudhry and J. G. C. Templeton (1983) discussed the first course of bulk queues, the theory of batch arrival and bulk service have been well developed until now. For example, we can see the most recent result from C. Armero and D. Conesa (2000), R. Arumuganathan and K. S. Ramaswami (2005), S. H. Chang, D. W. Choi and T. S. Kim (2004), D. Fakinos (1991), L. Srinivasan, N. Renganathan and R. Kalyanaraman (2002), U. Sumita and Y. Masuda (1997) and P. V. Ushakumari and A. Krishnamoorthy (1998).

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Performance Modeling in Client Server Network Comparison of Hub, Switch ad Bluetooth Technology by using MARKOV Algorithm & Queuing Petrinets with security of Steganography

Performance Modeling in Client Server Network Comparison of Hub, Switch ad Bluetooth Technology by using MARKOV Algorithm & Queuing Petrinets with security of Steganography

A network consists of a set of interconnected queues. Each queue represents a service station, which serves requests (also called jobs) sent by customers. A service station consists of one or more servers and a waiting area which holds requests waiting to be served. When a request arrives at a service station, its service begins immediately if a free server is available. Otherwise, the request is forced to wait in the waiting area. Service is done as per the general bulk service rule introduced by Neuts (1967) with a=1. As per this rule immediately after the completion of the service, if the server finds a unit present, it start its service; if it finds one or more but almost bit takes them all in a batch and if it finds more than bit takes in the batch for service b units , while others wait. The batch takes a minimum of one unit and a maximum of b units.[4]The time between successive request arrivals is called interarrival time. Each request demands a certain amount of service,

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Appointment Capacity Planning in Specialty Clinics: A Queueing Approach

Appointment Capacity Planning in Specialty Clinics: A Queueing Approach

Specialty clinics provide specialized care for patients referred by primary care physicians, emergency depart- ments, or other specialists. Urgent patients must often be seen on the referral day, while non-urgent referrals are typically booked an appointment for the future. To deliver a balanced performance, the clinics must know how much ‘appointment capacity’ is needed for achieving a reasonably quick access for non-urgent patients. To help identify the capacity that leads to the desired performance, we model the dynamics of appointment backlog as novel discrete-time bulk service queues, and develop numerical methods for efficient computation of corresponding performance metrics. Realistic features such as arbitrary referral and clinic appointment cancellation distributions, delay-dependent no-show behaviour and rescheduling of no-shows are explicitly captured in our models. The accuracy of the models in predicting performance as well as their usefulness in appointment capacity planning is demonstrated using real data. We also show the application of our models in capacity planning in clinics where patient panel size, rather than appointment capacity, is the major decision variable.

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Design and performance analysis of delay tolerance network secure routing in a harsh wild scenario

Design and performance analysis of delay tolerance network secure routing in a harsh wild scenario

a mobile proxy node has one bulk service queue which satisfied the feature of G/G/1 model with a capacity s (all the calculation and analysis in the rest of this paper are based on this assumption). According to queuing theory, in order to deduce the delay and arrival rate, we should figure out the service time and waiting time of one piece of message first, which are also relevant with the service time and waiting time of bulk data.

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Stochastic Analysis of Bulk Production with Bulk and Catastrophic Sales System using Matrix Geometric Approach

Stochastic Analysis of Bulk Production with Bulk and Catastrophic Sales System using Matrix Geometric Approach

In this paper production and sales models are studied using matrix geometric methods. Aissani.A and Artalejo.J.R [1] and Ayyappan, Subramanian and Gopal Sekar [2] have analyzed retrial queueing system using matrix geometric methods. Bini, Latouche and Meini [3] have studied numerical methods for Markov chains and Chakravarthy and Neuts [4] have discussed in depth a multi-server queueing model. Gaver, Jacobs and Latouche [5] have treated birth and death models with random environment. Latouche and Ramaswami [6] have studied Analytic methods. For matrix geometric methods and models one may refer Neuts [7]. The models considered here are somewhat different from queueing theory models. Here random number of production and random number of sales of products are considered at a time whereas in queueing theory a fixed number of customers arrive or are served at any arrival or service epochs. Even in bulk service queueing model, when maximum service capacity is b, then fixed b customers are cleared by a service when more than b customers are waiting, Neuts and Nadarajan [8]. The models considered here being stocks, even when the maximum capacity of the selling system is b, only with some probability, the size b may be cleared. In a similar way arrival sizes of customers are usually fixed in bulk arrival queueing systems but the models here treat bulk production of products subject to a probability law. Further in these models catastrophic sales can occur, clearing the entire stock of products produced. Such situations are seen

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THE EFFECT OF RIPENING PERIODS ON PHYSICAL, CHEMICAL AND MECHANICAL PROPERTIES OF SERVICE TREE FRUITS (Sorbus Domestica L.)

THE EFFECT OF RIPENING PERIODS ON PHYSICAL, CHEMICAL AND MECHANICAL PROPERTIES OF SERVICE TREE FRUITS (Sorbus Domestica L.)

Abstract: The effect of ripening periods on physical, chemical and mechanical properties of service tree fruits (Sorbusdomestica L.) were determined. The geometric mean diameter, fruit mass, volume, surface area and fruit density of service tree fruits decreased with the increase of ripening periods, while, bulk density increased with ripening periods. L*, a*, b*decreased with the increase of ripening periods, while hue increased with ripening periods respectively. The friction coefficients of service tree fruits with increase ripening periods were lower for laminate than the other surfaces. The soluble solid content (SSC) and total acidity of service tree fruits decreased at ripening period. Titratable acidity was higher in physiological maturity as compared to ripening periods, and pH values of service tree fruits decreased with ripening periods. For this reason, post-harvest technological applications of the service tree fruits must be designed while taking these criteria into consideration such as physical, mechanical and chemical properties properties of service tree fruits.

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Analysis of the Bulk Queue Model with Multiple Vacations, Delayed Service and Closed Down Time

Analysis of the Bulk Queue Model with Multiple Vacations, Delayed Service and Closed Down Time

Various authors have analysed queueing problems of server vacations with several combinationsR. Arumuganathan and T. Judith Malliga analysed of a bulk queue with multiple vacations, delayed service and setup time[2]. A literature survey on queueing systems with server vacations can be found in Doshi [4]. Reddy et al. [6] have analysed a bulk queueing model and multiple vacations with setup time. They derived the expected number of customers in the queue at an arbitrary time epoch and obtained other measures. Recently, Ke [5] has analysed the optimal policy for / /1 queueing systems of different vacation types with startup time.

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A Bulk Queuing model of Optional Second Phase Service with Short and Long vacations

A Bulk Queuing model of Optional Second Phase Service with Short and Long vacations

[9] S. Maragathasundari and S. Srinivasan, Three phase M/G/1 queue with Bernoulli Feedback and multiple server vacation and service interruptions, International Journal of Applied mathematics and Statistics,Vol.33, (2013),Issue no.3.

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Bulk to Nanomaterials

Bulk to Nanomaterials

II. R ADICAL C HANGES A T T HE N ANO S CALE L EVEL When scientists synthesize nanomaterials in the laboratory, everything we understand about a material at a macroscopic level changes at a glance. Color, chemical properties, conductivity - it all changes. At the nanoscale level, the quantum properties of materials overpower its bulk properties and stuff gets unusual. The graphite and carbon nanotubes are familiar due to honeycomb (graphene) structure; however, the simple structural change from sheets to tubes changes carbon from one of the softest elements (graphite) to one of the strongest (carbon nanotubes). Gold nanoparticles trade their glossy yellow glitter for a dark reddish shade [3]. The numerous differences between nanoscale and macroscale materials have flung the door wide open for new technology and applications. From medicine to energy to information technology, the nanomaterial revolution is coming.

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Nanobubbles in bulk

Nanobubbles in bulk

In Chapter 3, the methods developed to distinguish bulk nanobubbles from other nanoparticles are explained in detail. This includes size measurement under the application of external pressure and the determination of nanoparticle density. These methods were applied to armoured nanobubbles that are usually used in ultrasound imaging. The results of these experiments are discussed in detail. These techniques have been applied in chapters 4-6. Chapter 4 investigates the existence of long-lived bulk nanobubbles produced using two different devices that are designed to produce bulk nanobubble solutions by mechanical means. In Chapter 5, a simple method for generating nanoparticles by mixing ethanol and water is presented. The constitution of these particles whether they are gas filled or otherwise is determined and how the nanoparticles are formed is explained.

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Environmental Geology Abstracts

Environmental Geology Abstracts

Practical assessment of The U.S. Forest Service Forest Health Monitoring (FHM) soil sampling protocol to accurately measure carbon and nitrogen stocks in thousands of forest plots across North America. Twelve established research plots in the French Creek Watershed, of Berks and Chester Counties in Pennsylvania, were revisited in order to obtain sufficient data to compare FHM soil cores to quantitative soil pits on the basis of estimation of bulk density, coarse

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