Search results

The authors consider parallel four-state tandem open queueing network. The queue capacity is infinite. Passenger arrival rate is Poisson distribution and service rate is exponential distribution. The queue is constructed in the form of tandem queue, and each and every queue of tandem queue is single server (M/M/1) queue. In tandem queue, passengers will leave the system once they receive service from both the states. The purpose of this paper is to provide performance analysis for four-state tandem open queue network, and a governing equation is formulated with the help of transition diagram. Using Burke theorem, the authors formulated equation for average number of passenger in the system, average waiting time of passenger in the system, average number of passenger in the queue and average waiting time of passenger in the queue.

This paper used Burke’s theorem.

In this paper, performance analysis is done for parallel four-state tandem open queueing network and performance measure solved using Burkes theorem formula. K. Sreekanth et al. has done performance analysis for single tandem queue with three states. In this paper, the authors have done performance analysis for two tandem queues parallel with four states. This four-state tandem open queueing network is suitable for real world applications. This paper can extend for more number of service states and multi-server states according to the application, and in such case, the authors have to prove and explain with numerical examples. This analysis is more useful for the applications such as airports, railway stations, bus-stands and banks.

In this paper, parallel four-state tandem open queueing network and performance measure has been solved using Burke’s theorem formula.

The use of queueing network models was stimulated by the appearance (1975) of the exact product form solution of a class of open, closed and mixed queueing networks obeying the local balance principle and solved, a few years later, by the popular mean value analysis algorithm (1980). Since then, research efforts have been produced to approximate solutions for non-exponential services and non-pure random mechanisms in customer processing and routing. The purpose of this paper is to examine the suitability of modeling choices and solution approaches consolidated in other domains with respect to two key logistic processes in container terminals.

In particular, the analytical solution of queueing networks is assessed for the vessel arrival-departure process and the container internal transfer process with respect to a real terminal of pure transshipment.

Numerical experiments show the extent to which a decomposition-based approximation, under fixed or state-dependent arrival rates, may be suitable for the approximate analysis of the queueing network models.

The limitation of adopting exponential service time distributions and Poisson flows is highlighted.

Comparisons with a simulation-based solution deliver numerical evidence on the companion use of simulation in the daily practice of managing operations in a finite-time horizon under complex policies.

Discussion of some open modeling issues and encouraging results provide some guidelines on future research efforts and/or suitable adaption to container terminal logistics of the large body of techniques and algorithms available nowadays for supporting long-run decisions.

Considers a class of control systems known as generalized kanban control systems (GKCS) which can be used to implement a pull control mechanism in a manufacturing system. In a GKCS, the production system is decomposed into stages, where each stage consists of a production sub‐system. There are two design parameters per stage: one controls the work‐in‐process in the stage and the other determines the maximum number of finished products of this stage. Investigates the influence of these design parameters on the efficiency of generalized kanban control policies by deriving qualitative properties as well as using experimental results on the behaviour of GKCS.

Computer networks are very complex and somewhat unpredictable systems in dynamic operations. Cybernetic modelling, incorporating stochastic, fuzzy variables, wherever appropriate, approximating interactions not easily tractable via a black‐box systems approach, may be more accurate. A method of analysis of computer networks is considered by representing subsystems and interactions, accurately as far as possible, and then utilising the results of systems sciences for analysis, including specifics, e.g. queuing systems. The aim is to identify correspondences between the behaviour of subsystems of networks and suitable analytic tools. Automation along such lines for the analysis and design of networks and other systems may be a possibility.

Relief item distribution to victims is a key activity during disaster response. Currently many humanitarian organizations follow simple guidelines based on experience to assess need and distribute relief supplies. However, the interviews with practitioners suggest a problem in efficiency in relief distribution efforts. The purpose of this paper is to develop a model and solution methodology that can estimate relief center (RC) performance, measured by waiting time for victims and throughput, for any RC design and analyze the impact of key design decisions on these performance measures.

Interviews with practitioners and current practice guidelines are used to understand relief distribution and a queuing network model is used to represent the relief distribution. Finally, the model is applied to data from the 2015 Nepal earthquake.

The findings identify that dissipating congestion created by crowds, varying item assignment decisions to points of distribution, limiting the physical RC capacity to control congestion and using triage queue to balance distribution times, are effective strategies that can improve RC performance.

This research bases the RC designs on Federal Emergency Management Agency guidelines and assumes a certain area and volunteer availability.

This paper contributes to humanitarian logistics by discussing useful insights that can impact how relief agencies set up and operate RCs. It also contributes to the queuing literature by deriving analytic solutions for the steady state probabilities of finite capacity, state dependent queues with blocking.

An integrated queueing network focused on container storage/retrieval operations occurring on the yard of a transshipment hub is proposed. The purpose of the network is to support decisions related to the organization of the yard area, while also accounting for operations policies and times on the quay.

A discrete-event simulation model is used to reproduce container handling on both the quay and yard areas, along with the transfer operations between the two. The resulting times, properly estimated by the simulation output, are fed to a simpler queueing network amenable to solution via algorithms based on mean value analysis (MVA) for product-form networks.

Numerical results justify the proposed approach for getting a fast, yet accurate analytical solution that allows carrying out performance evaluation with respect to both organizational policies and operations management on the yard area.

Practically, the expected performance measures on the yard subsystem can be obtained avoiding additional time-expensive simulation experiments on the entire detailed model.

As a major takeaway, deepening the MVA for generally distributed service times has proven to produce reliable estimations on expected values for both user- and system-oriented performance metrics.

Petri net (PN) and queuing theory are used in performance analysis of a flexible manufacturing system (FMS). They are used to determine the FMS measures of performance. These measures of performance include optimal work‐in‐process (WIP), lead time, production rate, machine utilization, and number of servers at each station. The purpose of this paper is to present a comparison between PN and queuing network tools to determine the optimum values for FMS measures of performance.

PN model with dual kanban and closed queuing network are used for analysis and performance evaluation of FMS. Integrated network analyzer and Lingo softwares are used for performance evaluation of FMS by PN tool. CANQ software is used for performance evaluation of FMS by queuing network. In both the approaches, the throughput is bounded by the utilization of the bottleneck machines.

The comparison shows that for the given number of servers, PN gives better values of performance measures for FMS. The PN optimization gives minimum WIP corresponding to the maximum production rate. Minimum WIP leads to minimum lead time.

The results are concluded based on one case study. In future research, the results may be achieved by doing more case studies with different numbers of system parameters and/or parameter settings.

Using the PN model, the production manager may design, analyze, evaluate, and even optimize the layout of the production system for minimum WIP, maximum throughput, and reduced lead time. The determination of the total WIP, total number of stations in the production system, and the number of servers at each station may be helpful in shop floor management. It may result in more production efficiency along with ease of supervision.

This paper presents a first novel comparison of its kind between PN and queuing network for evaluation of FMS.

This paper considers correction aspects of computer communication networks modelling with emphasis on their performance evaluation. In general, the problem is to achieve the highest possible performance given constraints on the system.

The paper reviews the application of the analytical methods, based on the queueing theory, to the computer communication systems and makes an extension of theory to the improvement of the developed analytical models. In this sense the paper describes the derivation of a correction factor for analytical models to study more precise their basic parameters (end‐to‐end delay, performance, etc.).

The contribution is in incorporating the derived correction factor to account for the real non‐exponential nature of the input to the transmission channels of computer communication systems. The produced results by corrected analytical model are compared with results previously reported in the literature to estimate the magnitude of improvement.

The improved analytical models were tested under various ranges of parameters, which influence the architecture of the computer communication networks and which are important for practical use.

The rapid rate of growth of computer‐based communication systems (e.g. distributed computer networks, mobile data networks) has resulted in a renewed and intensive interest in this area. Efficient design of their service facilities leads to the sharing of resources among users. Such public shared networks are largely oversubscribed by independent users, which make random demands on the network resources. The optimal resource allocation to satisfy such demands and the proper settlement of contention when demands exceed the capacity of the resources, constitute the problem of being able to understand and to predict system behaviour.

To behaviour analysis we can use both analytical and simulation methods. Modelling and simulation are methods, which are commonly used by performance analysts to represent constraints and optimise performance. Principally the application of analytical queuing theory results belongs to the preferred method in comparison to the simulation method, because of their ability to analyse also very large networks.

As new computer communication systems, such as distributed computer networks or mobile data networks, grow in scale and complexity, the problem of being able to understand and to predict system behaviour becomes increasingly important. To their behaviour analysis we can use both analytical and simulation methods. Principally the application of analytical queuing theory results belongs to the preferred method in comparison to the simulation method. In this sense the article describes the development, realisation and verification of the new analytical model for the study of the basic parameters (end‐to‐end delay, performance etc.) of distributed data networks (computer networks, mobile data networks). The suggested model considers for every node of the data network one part for its own node's activities (communication functions) and another one for the modelling of each node channel for data transmission. When using a multiprocessor system, as the modern node communication processor, the model for its own node activities is the more realistic M/D/r system (Poisson arrival process/Deterministic service time distribution/r server system) and for the every node transmission channel the M/M/1 system (Exponential service time, Single server system). The new developed analytical model includes the influence of the communication functions to the whole delay in each node of a computer communication network. The achieved results of the developed model are compared with the results of the commonly used analytical and simulation models to estimate the magnitude of improvement. Likewise the developed analytical model was tested under various ranges of parameters, which influence the architecture of the distributed data networks and which are important for practical use.

The aim of this paper is to develop an efficient analytical procedure to evaluate performance of the most general pull production systems particularly when multiple‐part‐types are involved. The authors consider a kanban controlled production system that can be modelled as a closed queuing network with different product classes. The production line is decomposed into stages which consist of one or several stations and an output buffer. Each stage is associated with a given number of kanbans. The main idea of this analytical algorithm is to analyze each subnetwork individually using a product form approximation technique. The iterative procedure is used to find the unknown parameters.

The authors design a multiclass queuing network that can be used to represent kanban controlled production systems. To solve this model, three procedures are used: decompose the original network into M subnetworks, convergence of unknown parameters in each subnetwork, and convergence of unknown parameters in the original network. The authors now describe these procedures separately.

The main contribution of this paper is the formulation of the problem of kanban controlled production systems with several part‐types. The methodology is based on approximate formula with decomposition and is applicable to more general manufacturing environments. The authors' method can be applied to both limited and unlimited demands. The analytical algorithm designed in this work has demonstrated excellent performance in analyzing kanban controlled production systems.

The methodology of this algorithm is based on approximate formula and is applicable to more general manufacturing environments.