Queuing Network

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 20274 Experts worldwide ranked by ideXlab platform

Yili Liu - One of the best experts on this subject based on the ideXlab platform.

  • Queuing Network modeling of driver eeg signals based steering control
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017
    Co-Authors: Xinan Fan, Jinling Lian, Yili Liu
    Abstract:

    Directly using brain signals rather than limbs to steer a vehicle may not only help disabled people to control an assistive vehicle, but also provide a complementary means of control for a wider driving community. In this paper, to simulate and predict driver performance in steering a vehicle with brain signals, we propose a driver brain-controlled steering model by combining an extended Queuing Network-based driver model with a brain–computer interface (BCI) performance model. Experimental results suggest that the proposed driver brain-controlled steering model has performance close to that of real drivers with good performance in brain-controlled driving. The brain-controlled steering model has potential values in helping develop a brain-controlled assistive vehicle. Furthermore, this study provides some insights into the simulation and prediction of the performance of using BCI systems to control other external devices (e.g., mobile robots).

  • computational modeling of finger swipe gestures on touchscreen application of fitts law in 3d space
    Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2016
    Co-Authors: Heejin Jeong, Yili Liu
    Abstract:

    Although swiping (also called flicking) is one of the commonly used touchscreen gestures, few modeling studies have been conducted. In this paper, a computational model that focuses on touchscreen swipe gestures was developed by extending the QN-MHP (Queuing Network-Model Human Processor) architecture. The model assumed that the swiped-route follows a three-dimensional path. To model the finger swipe gesture, an operator (i.e., “Swipe-with-finger”) for the Queuing Network Cognitive Architecture was developed using an existing regression equation for predicting the finger movement time in 3D space (Cha and Myung, 2013). The model was validated with two corresponding experimental results in the literature. As a result, the swiping times generated by the model were well fit with the human subject data.

  • Queuing Network Modeling of the Psychological Refractory Period (PRP)
    2015
    Co-Authors: Yili Liu
    Abstract:

    The psychological refractory period (PRP) is a basic but important form of dual-task information processing. Existing serial or parallel processing models of PRP have successfully accounted for a variety of PRP phenomena; however, each also encounters at least 1 experimental counterexample to its predictions or modeling mechanisms. This article describes a Queuing Network-based mathematical model of PRP that is able to model various experimental findings in PRP with closed-form equations including all of the major counterexamples encountered by the existing models with fewer or equal numbers of free parameters. This modeling work also offers an alternative theoretical account for PRP and demonstrates the importance of the theoretical concepts of “Queuing ” and “hybrid cognitive Networks ” in understanding cognitive architecture and multitask performance

  • detecting driver normal and emergency lane changing intentions with Queuing Network based driver models
    International Journal of Human-computer Interaction, 2015
    Co-Authors: Cuie Wang, Xuerui Yang, Mingtao Wang, Yili Liu
    Abstract:

    Driver intention detection is an important component in human-centric driver assistance systems. This article proposes a novel method for detecting driver normal and emergency left- or right-lane-changing intentions by using driver models based on the Queuing Network cognitive architecture. Driver lane-changing and lane-keeping models are developed and used to simulate driver behavior data associated with 5 kinds of intentions (i.e., normal and emergency left- or right-lane-changing and lane-keeping intentions). The differences between 5 sets of simulated behavior data and the collected actual behavior data are computed, and the intention associated with the smallest difference is determined as the detection outcome. The experimental results from 14 drivers in a driving simulator show that the method can detect normal and emergency lane-changing intentions within 0.325 s and 0.268 s of the steering maneuver onset, respectively, with high accuracy (98.27% for normal lane changes and 90.98% for emergency la...

  • using Queuing Network and logistic regression to model driving with a visual distraction task
    International Journal of Human-computer Interaction, 2014
    Co-Authors: Guodong Gan, Yili Liu
    Abstract:

    Computational dual-task models of driving with a secondary task can help compute, simulate, and predict driving behavior in dual task situations. These models can thus help improve the process of developing in-vehicle devices by reducing or eliminating the need for conducting driver experiments in the early stage of the development. Further, these models can help improve traffic flow simulation. This article develops a dual-task model of driving with a visual distraction task using the Queuing Network model of driver lateral control and a logistic regression model. The comparison between the model simulation data and the human data from drivers in a driving simulator shows that this computational model can perform driving with a secondary visual task well and its performance is consistent with the driver data.

Vidhyacharan Bhaskar - One of the best experts on this subject based on the ideXlab platform.

  • Queuing Network model of uniformly distributed arrivals in a distributed supply chain using subcontracting
    Decision Support Systems, 2011
    Co-Authors: Vidhyacharan Bhaskar, Patrick Lallement
    Abstract:

    In this paper, a supply chain (four-input three-stage Queuing Network) receives uniformly distributed orders from clients. An input order is represented by two stochastic variables, occurrence time and the quantity of items to be delivered. The objective of this work is to compute the minimum response time, and thus the average number of items (optimum capacity) that can be delivered with this response time. Performance measures such as average queue lengths, average response times, and average waiting times of the jobs in the supply chain, and in the equivalent single-server Network are derived, plotted and discussed.

  • modeling a supply chain using a Network of queues
    Applied Mathematical Modelling, 2010
    Co-Authors: Vidhyacharan Bhaskar, Patrick Lallement
    Abstract:

    In this paper, a supply chain is represented as a two-input, three-stage Queuing Network. An input order to the supply chain is represented by two stochastic variables, one for the occurrence time and the other for the quantity of items to be delivered in each order. The objective of this paper is to compute the minimum response time for the delivery of items to the final destination along the three stages of the Network. The average number of items that can be delivered with this minimum response time constitute the optimum capacity of the Queuing Network. After getting serviced by the last node (a queue and its server) in each stage of the Queuing Network, a decision is made to route the items to the appropriate node in the next stage which can produce the least response time.

  • A four-input three-stage Queuing Network approach to model an industrial system
    Applied Mathematical Modelling, 2009
    Co-Authors: Vidhyacharan Bhaskar, Patrick Lallement
    Abstract:

    An industrial system is represented as a four-input, three-stage Queuing Network in this paper. The four-input Queuing Network receives orders from clients, and the orders are waiting to be served. Each order comprises (i) time of occurrence of the orders, and (ii) quantity of items to be delivered in each order. The objective of this paper is to compute the optimal path which produces the least response time for the delivery of items to the final destination along the three stages of the Network. The average number of items that can be delivered with this minimum response time constitute the optimum capacity of the Queuing Network. After getting serviced by the last node (a queue and its server) in each stage of the Queuing Network, a decision is made to route the items to the appropriate node in the next stage which can produce the least response time. Performance measures such as average queue lengths, average response times, average waiting times of the jobs in the four-input Network are derived and plotted. Closed-form expressions for the equivalent service rate, equivalent average queue lengths, equivalent response and waiting times of a single equivalent queue with a server representing the entire four-input Queuing Network are also derived and plotted.

  • A closed Queuing Network model with single servers for multi-threaded architecture
    Applied Mathematical Modelling, 2009
    Co-Authors: Vidhyacharan Bhaskar
    Abstract:

    In this paper, a closed Queuing Network model with single servers for each queue is proposed to model dataflow in a multi-threaded architecture. Multi-threading is useful in reducing the latency by switching among a set of threads in order to improve the processor utilization. Two sets of processors, synchronization and execution processors exist. Synchronization processors handle load/store operations and execution processors handle arithmetic/logic and control operations. A closed Queuing Network model is suitable for large number of job arrivals. The normalization constant is derived using a recursive algorithm for the given model. State diagrams are drawn from the closed Queuing Network model, and the steady-state balance equations are derived from it. Performance measures such as average response times and average system throughput are derived and plotted against the total number of processors in the closed Queuing Network model. Other important performance measures like processor utilizations, average queue lengths, average waiting times and relative utilizations are also derived.

  • Activity routing in a distributed supply chain: Performance evaluation with two inputs
    Journal of Network and Computer Applications, 2008
    Co-Authors: Vidhyacharan Bhaskar, Patrick Lallement
    Abstract:

    In this paper, an industrial system is represented as a 2-input, three-stage Queuing Network. The two input Queuing Network receives orders from clients, and the orders are waiting to be served. Each order comprises (i) time of occurrence of the orders and (ii) quantity of items to be delivered in each order. The objective of this paper is to compute the minimum response time for the delivery of items to the final destination along the three stages of the Network. The average number of items that can be delivered with this minimum response time constitute the optimum capacity of the Queuing Network. After getting serviced by the last node (a queue and its server) in each stage of the Queuing Network, a decision is made to route the items to the appropriate node in the next stage which can produce the least response time. Performance measures such as average queue lengths, average response times, and average waiting times of the jobs in the 2-input Network are derived and plotted. Closed-form expressions for the equivalent service rate, equivalent average queue lengths, and equivalent response and waiting times of a single queue with a single server representing the 2-input Queuing Network are also derived and plotted.

Sunderesh S Heragu - One of the best experts on this subject based on the ideXlab platform.

  • conwip closed or semi open Queuing Network
    International Journal of Operational Research, 2015
    Co-Authors: Sunderesh S Heragu, Aman Gupta
    Abstract:

    Closed Queuing Networks (CQNs) are generally used to model CONWIP systems. An implicit assumption in the CQN model is that whenever the work-in-process (WIP) level goes below the set value, there is always a job waiting for the released kanban. In other words, there are an infinite number of jobs in the external queue and as a result the kanban never waits in the resource pool. Modelling the CONWIP under this unrealistic assumption tends to underestimate the true cycle time of the kanban (and the job). In this technical note, we represent a CONWIP as a semi-open Queuing Network (SOQN) in which the job waits in the external queue until a kanban is available or vice-versa. Using published data and numerical experiments, we point out a modelling flaw of CONWIP systems in the literature and also show that the SOQN approach is a more appropriate model for representing CONWIP systems.

  • stochastic models for unit load operations in warehouse systems with autonomous vehicles
    Annals of Operations Research, 2015
    Co-Authors: Ananth Krishnamurthy, Sunderesh S Heragu, Charles J Malmborg
    Abstract:

    This paper presents stochastic models for multi-tier warehouse systems that handle unit load transactions using autonomous vehicle-based technology. In this system, self-powered autonomous vehicles carry out unit-load transactions by moving along rails within a tier and using lifts or conveyors for vertical movement between tiers. We develop semi-open Queuing Network models to evaluate congestion effects in processing storage and retrieval transactions in this system. The Queuing Network is evaluated using a decomposition-based approach. The Queuing model provides design insights on the effect of vehicle interference and vertical transfer mechanism on various system performance measures of interest. Insights from such studies can be especially useful during the conceptualization stage of warehouses that use autonomous vehicle technology.

  • matrix geometric solution for semi open Queuing Network model of autonomous vehicle storage and retrieval system
    Computers & Industrial Engineering, 2014
    Co-Authors: Banu Yetkin Ekren, Ananth Krishnamurthy, Sunderesh S Heragu, Charles J Malmborg
    Abstract:

    In this paper, we model the autonomous vehicle storage and retrieval system (AVS/RS) as a semi-open Queuing Network (SOQN) and apply a matrix-geometric method (MGM) for analyzing it. An AVS/RS is an automated material handling system for the high-rise pallet storage area of a warehouse and allows pallets to be stored and retrieved quickly and efficiently from their storage locations. It is an alternative to the traditional crane-based AS/RS (automated storage and retrieval system). A combination of lifts and autonomous vehicles store pallets into and retrieve them out of their respective rack storage locations. The crane based AS/RS typically utilizes aisle-captive, mast-mounted cranes that can access any storage location in an aisle via horizontal movement of the mast and vertical movement of the crane on the mast. In an SOQN, it is assumed that an arriving job or customer is paired with another device and the two visit all the stations that must process the job in the appropriate sequence. After all operations are completed on the job, it exits the system, but the device returns back to a device pool and awaits the next customer. Sometimes a job may have to wait for a device to arrive at the pool or a device may have to wait for a job to arrive. Although closed Queuing Networks (CQNs) and open Queuing Networks (OQNs) model systems that require pairing of an incoming job with a device, unlike the SOQN, they ignore the time that a device waits for a job or the time that a job waits for a device. In the context of an AVS/RS, the jobs correspond to storage/retrieval (S/R) transaction requests and the autonomous vehicles (AVs) correspond to the devices. Because an AV may sometimes have to wait for an S/R transaction or vice versa, we model the AVS/RS as an SOQN. We build the Queuing Network by deriving general travel times of pre-defined servers. We model the AVS/RS system as a single-class, multiple-server, SOQN. Then, we solve the Network using the MGM and obtain its key performance measures. We apply the MGM technique for solving the SOQN model to a warehouse in France that uses AVS/RS.

  • batch size modeling in a multi item discrete manufacturing system via an open Queuing Network
    Iie Transactions, 2004
    Co-Authors: Gang Meng, Sunderesh S Heragu
    Abstract:

    An open Queuing Network Analyzer (QNA) is often used in areas such as communication systems and discrete parts manufacturing systems. It uses the parametric decomposition method to approximately calculate the performance measures. The core of the method involves solving two systems of linear equations to calculate the first two moments of the effective arrival rate and service time. In this paper, we consider the effects of batch size on the parametric decomposition procedure of the QNA and modify the two sets of linear equations accordingly. The concept of a relative batch size is proposed for the purpose of modeling the effects of batch size. Experimental results are shown to illustrate the effectiveness of the method.

Jun Ota - One of the best experts on this subject based on the ideXlab platform.

Patrick Lallement - One of the best experts on this subject based on the ideXlab platform.

  • Queuing Network model of uniformly distributed arrivals in a distributed supply chain using subcontracting
    Decision Support Systems, 2011
    Co-Authors: Vidhyacharan Bhaskar, Patrick Lallement
    Abstract:

    In this paper, a supply chain (four-input three-stage Queuing Network) receives uniformly distributed orders from clients. An input order is represented by two stochastic variables, occurrence time and the quantity of items to be delivered. The objective of this work is to compute the minimum response time, and thus the average number of items (optimum capacity) that can be delivered with this response time. Performance measures such as average queue lengths, average response times, and average waiting times of the jobs in the supply chain, and in the equivalent single-server Network are derived, plotted and discussed.

  • modeling a supply chain using a Network of queues
    Applied Mathematical Modelling, 2010
    Co-Authors: Vidhyacharan Bhaskar, Patrick Lallement
    Abstract:

    In this paper, a supply chain is represented as a two-input, three-stage Queuing Network. An input order to the supply chain is represented by two stochastic variables, one for the occurrence time and the other for the quantity of items to be delivered in each order. The objective of this paper is to compute the minimum response time for the delivery of items to the final destination along the three stages of the Network. The average number of items that can be delivered with this minimum response time constitute the optimum capacity of the Queuing Network. After getting serviced by the last node (a queue and its server) in each stage of the Queuing Network, a decision is made to route the items to the appropriate node in the next stage which can produce the least response time.

  • A four-input three-stage Queuing Network approach to model an industrial system
    Applied Mathematical Modelling, 2009
    Co-Authors: Vidhyacharan Bhaskar, Patrick Lallement
    Abstract:

    An industrial system is represented as a four-input, three-stage Queuing Network in this paper. The four-input Queuing Network receives orders from clients, and the orders are waiting to be served. Each order comprises (i) time of occurrence of the orders, and (ii) quantity of items to be delivered in each order. The objective of this paper is to compute the optimal path which produces the least response time for the delivery of items to the final destination along the three stages of the Network. The average number of items that can be delivered with this minimum response time constitute the optimum capacity of the Queuing Network. After getting serviced by the last node (a queue and its server) in each stage of the Queuing Network, a decision is made to route the items to the appropriate node in the next stage which can produce the least response time. Performance measures such as average queue lengths, average response times, average waiting times of the jobs in the four-input Network are derived and plotted. Closed-form expressions for the equivalent service rate, equivalent average queue lengths, equivalent response and waiting times of a single equivalent queue with a server representing the entire four-input Queuing Network are also derived and plotted.

  • Activity routing in a distributed supply chain: Performance evaluation with two inputs
    Journal of Network and Computer Applications, 2008
    Co-Authors: Vidhyacharan Bhaskar, Patrick Lallement
    Abstract:

    In this paper, an industrial system is represented as a 2-input, three-stage Queuing Network. The two input Queuing Network receives orders from clients, and the orders are waiting to be served. Each order comprises (i) time of occurrence of the orders and (ii) quantity of items to be delivered in each order. The objective of this paper is to compute the minimum response time for the delivery of items to the final destination along the three stages of the Network. The average number of items that can be delivered with this minimum response time constitute the optimum capacity of the Queuing Network. After getting serviced by the last node (a queue and its server) in each stage of the Queuing Network, a decision is made to route the items to the appropriate node in the next stage which can produce the least response time. Performance measures such as average queue lengths, average response times, and average waiting times of the jobs in the 2-input Network are derived and plotted. Closed-form expressions for the equivalent service rate, equivalent average queue lengths, and equivalent response and waiting times of a single queue with a single server representing the 2-input Queuing Network are also derived and plotted.

Related terms

Queuing Network - 15 days free trial to Access Experts & Abstracts