Queuing Approach Research Articles

A spatial queuing approach to optimize coordinated signal settings to obviate gridlock in adjacent work zones

AbstractGridlock is defined when traffic comes to a complete halt inducing huge delays. If a work zone on a two‐lane two‐way highway is set up, in which one of the traffic lanes is closed for maintenance road works, the remaining lane has to be controlled to serve the two‐way traffic alternatively. The study objective is to optimize the traffic signal controls across two closely spaced work zones to prevent a gridlock, which can occur easily if upstream and downstream signals are not well coordinated. When vehicle queues build up in the middle sections between two work zones and further expand to occupy the single available lanes in both directions, the two‐way traffic is then blocked and no vehicle can leave from the queues generating a gridlock. To address this problem, spatial queues are important parameters that must be explicitly analyzed. The cell transmission model, which is known to be a robust mathematical tool for the modeling of queue dynamics, is adopted in this study. A signal cell is used to represent each traffic signal control, the exit flow capacity of which is defined in accordance with the signal plan. A set of linear constraints is established to relate all of the model parameters and variables. The objective function is taken as the total number of vehicles in the critical section between the two work zones. The minimization of this objective function can effectively obviate the occurrence of a gridlock. The optimization problem is formulated as a Binary‐Mixed‐Integer‐Linear‐Program that can be solved by the standard branch‐and‐bound technique. Numerical examples are given to demonstrate the effectiveness of the proposed methodology. Copyright © 2010 John Wiley & Sons, Ltd.

A queueing approach to production-inventory planning for supply chain with uncertain demands: Case study of PAKSHOO Chemicals Company

In some industries such as the consumable product industry because of small differences between products made by various companies, customer loyalty is directly related to the availability of products required at that time. In other words, in such industries demand cannot be backlogged but can be totally or partly lost. So companies of this group use make-to-stock (MTS) production policy. Therefore, in these supply chains, final product warehouses play a very important role, which will be highlighted by considering the demand uncertainty as it happens in real world, especially in the consumable product industries in which demand easily varies according to the customer’s taste variation, behavioral habits, environmental changes, etc. In this article, an ( s , Q ) inventory system with lost sales and two types of customers, ordinary and precedence customers and exponentially distributed lead times are analyzed. Each group of demands arrives according to the two independent Poisson processes with different rates. A computationally efficient algorithm for determining the optimal values for safety stock as reorder level and reorder quantity for a multi-item capacitated warehouse is developed. The algorithm also suggests the optimal warehouse capacity. A Multi-item Capacitated Lot-sizing problem with Safety stock and Setup times (MCLSS) production planning model is then developed to determine the optimal production quantities in each period using optimal values computed by the first algorithm as inputs. Finally, the proposed production-inventory-queue model is implemented in a case study in PAKSHOO Chemicals Company and results are obtained and analyzed. Moreover, solving this problem can help to strategic decision making about supply chain decoupling point.

A make-to-stock manufacturing system with component commonality: A queuing approach

In this article a manufacturing system that features component commonality and a production allocation mechanism that postpones inventory commitment is modeled. Under this mechanism, which is called the First-Use First-Serve (FUFS), approach, a component is committed to an order only when all other required components become available. Furthermore, production of a component can only occur if its inventory is below a threshold. The performance of the proposed model is compared with the benchmark case of the First-Come First-Served (FCFS) approach under the sequence in which orders are received. The presented results show that FUFS outperforms FCFS on most system performance criteria. However, FCFS may outperform FUFS under some performance measures of dispersion, when the workload is heavy.

Variation in job opportunities for men and women in the Swiss labor market 1962–1989

For the period of the 1960s to the late 1980s, this paper examines variation in job opportunities for men and women in the occupationally segmented and highly gender-segregated Swiss labor market. Job opportunities are defined as the volume of job openings potentially accessible to individuals with given skills and other desired characteristics. They are conceptualized within the queuing approach, taking into account the structure and cyclical fluctuations of the Swiss labor market. Theoretical arguments about employers’ criteria for ranking workers are abundant. However, the testing of the corresponding hypotheses has often been hampered due to the difficulty of measuring individual job opportunities empirically. This paper attempts to advance this research field by proposing an individual-level and time-dependent measurement of job opportunities based on annual occupation-level job-advertisement data. It yields an indicator of the volume of job opportunities accessible to workers with given occupation-specific credentials and other job-relevant characteristics (i.e., gender, age, work experience). Based on separate regression analyses for men and women, job opportunities for salient groups of labor-market participants and their ranking within the labor queues are assessed. The findings, which differ markedly by gender, show that macro-level processes, such as the economic cycle, play an important role for men's job opportunities in particular. Furthermore, job opportunities strongly depend on occupational credentials whereas educational attainment plays a minor part. Age and cohort effects are found mainly for women. These findings imply that in occupationally segmented and sex-segregated labor markets occupational credentials and sex serve as employers’ primary ranking criteria.

Interactive Process of Microsimulation and Logistic Regression for Short-Term Work Zone Traffic Diversion

The rapidly growing number of work zones on national highways is having significant operational impacts due to the temporary loss of capacity. Work zone impact on safety and mobility creates a strong need to alleviate work zone congestion and protect road users and workers, which requires a sufficient understanding of work zone impact on traffic flow. Previous studies and field observations demonstrated the importance of considering diversion phenomena when performing work zone impact analysis. To overcome the limitations of deterministic queuing approaches applied in most work zone impact analysis tools, an interactive process combining microsimulation and logistic regression was developed to imitate diversion behavior dynamically in the upstream of work zones with a number of entrance and exit ramps. Specifically, the logistic regression model based on the field observations was incorporated into a well-calibrated VISSIM model to simulate traffic flow in work zones with diversion behavior. The integration of the two models was achieved via the development of diversion calculation module using a COM interface provided by VISSIM. The comparison between simulated results and field observations suggested that the diversion calculation module using logistic regression can simulate the queue propagation process due to lane closure in an efficient and effective manner. It was demonstrated that the interactive process can improve work zone impact analysis by using real-time traffic feedback information to emulate the diversion phenomenon upstream of work zones.

A multiplayer real-time game protocol architecture for reducing network latency

Multiplayer, real-time games have become a huge commercial service. Because of this success, the need exists for a more efficient and reliable game server solution. Such games are a kind of soft real-time systems because a game server has to respond to requests from many clients, compute the action of a non-player character (NPC), and filter the data from each client within specified time constraints. Some client and server tasks are periodic and some are not. To meet these timing constraints, we propose a task scheduling policy. Our solution includes two approaches: (1) the STU segment queue approach to convert sporadic events from game clients into serialized periodic event groups; and (2) the spatial domain approach to reduce queuing delay caused by the network outgoing event queue. To implement our solution on the JAVA framework with the non-real-time JAVA virtual machine, we propose a new client/server architecture for a scalable game server that in addition, will reduce the frequency of garbage collection by reusing server resources <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

A Cross-layer Dual Queue Approach for Improving TCP Fairness in Infrastructure WLANs

Fairness is one of the most important performance measures in IEEE 802.11 Wireless Local Area Networks (WLANs), where channel is accessed through competition. In this paper, we focus on the fairnes...

A Model for Understanding the Impacts of Demand and Capacity on Waiting Time to Enter a Congested Recovery Room

When a recovery room is fully occupied, patients frequently wait in the operating room after emerging from anesthesia. The frequency and duration of such delays depend on operating room case volume, average recovery time, and recovery room capacity. The authors developed a simple yet nontrivial queueing model to predict the dynamics among the operating and recovery rooms as a function of the number of recovery beds, surgery case volume, recovery time, and other parameters. They hypothesized that the model could predict the observed distribution of patients in recovery and on waitlists, and they used statistical goodness-of-fit methods to test this hypothesis against data from their hospital. Numerical simulations and a survey were used to better understand the applicability of the model assumptions in other hospitals. Statistical tests cannot reject the prediction, and the model assumptions and predictions are in agreement with data. The survey and simulations suggest that the model is likely to be applicable at other hospitals. Small changes in capacity, such as addition of three beds (roughly 10% of capacity) are predicted to reduce waiting for recovery beds by approximately 60%. Conversely, even modest caseload increases could dramatically increase waiting. A key managerial insight is that there is a sensitive relationship among caseload and number of recovery beds and the magnitude of recovery congestion. This is typical in highly utilized systems. The queueing approach is useful because it enables the investigation of future scenarios for which historical data are not directly applicable.

A network queuing approach for evaluation of performance measures in autonomous vehicle storage and retrieval systems

A network queuing approach is used to estimate performance measures for Autonomous Vehicle Storage and Retrieval (AVS/R) Systems using opportunistic interleaving. The model exploits the distribution of cycle types and random storage assumptions to estimate the proportion of single and dual command cycles in a system. Comparable in accuracy and computational efficiency to procedures based on standard queuing models, the proposed technique provides the additional advantage of flexibility for modeling the interfaces between a storage system and the overall material flow system in a facility. The procedure is demonstrated for a range of problem scenarios.

A queuing approach for inventory planning with batch ordering in multi-echelon supply chains

This paper presents stylized models for conducting performance analysis of the manufacturing supply chain network (SCN) in a stochastic setting for batch ordering. We use queueing models to capture the behavior of SCN. The analysis is clubbed with an inventory optimization model, which can be used for designing inventory policies . In the first case, we model one manufacturer with one warehouse, which supplies to various retailers. We determine the optimal inventory level at the warehouse that minimizes total expected cost of carrying inventory, back order cost associated with serving orders in the backlog queue, and ordering cost. In the second model we impose service level constraint in terms of fill rate (probability an order is filled from stock at warehouse), assuming that customers do not balk from the system. We present several numerical examples to illustrate the model and to illustrate its various features. In the third case, we extend the model to a three-echelon inventory model which explicitly considers the logistics process.

Application of a simulation-based dynamic traffic assignment model

The evaluation of on-line intelligent transportation system (ITS) measures, such as adaptive route-guidance and traffic management systems, depends heavily on the use of faster than real time traffic simulation models. Off-line applications, such as the testing of ITS strategies and planning studies, are also best served by fast-running traffic models due to the repetitive or iterative nature of such investigations. This paper describes a simulation-based, iterative dynamic equilibrium traffic assignment model. The determination of time-dependent path flows is modeled as a master problem that is solved using the method of successive averages (MSA). The determination of path travel times for a given set of path flows is the network-loading sub-problem, which is solved using the space-time queuing approach of Mahut. This loading method has been shown to provide reasonably accurate results with very little computational effort. The model was applied to the Stockholm road network, which consists of 2100 links, 1191 nodes, 228 zones, representing and 4964 turns. The results show that this model is applicable to medium-size networks with a very reasonable computation time.

Tandem Queue Models with Applications to QoS Routing in Multihop Wireless Networks

We consider the problem of quality of service (QoS) routing in multi-hop wireless networks where data are transmitted from a source node to a destination node via multiple hops. The routing component of a QoS-routing algorithm essentially involves the link and path metric calculation which depends on many factors such as the physical and link layer designs of the underlying wireless network, transmission errors due to channel fading and interference, etc. The task of link metric calculation basically requires us to solve a tandem queueing problem which is the focus of this paper. We present a unified tandem queue framework which is applicable for many different physical layer designs. We present both exact and approximated decomposition approaches. Using the queueing framework, we can derive different performance measures, namely, end-to-end loss rate, end-to-end average delay, and end-to-end delay distribution. The proposed decomposition approach is validated and some interesting insights into the system performance are highlighted. We then present how to use the decomposition queueing approach to calculate the link metric and incorporate this into the route discovery process of the QoS routing algorithm. The extension of the queueing and QoS routing framework to wireless networks with class-based queueing for QoS differentiation is also presented.

Vehicle routing with dynamic travel times: A queueing approach

Transportation is an important component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routes is a crucial management problem. In this paper, a vehicle routing problem with dynamic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture travel times. The queueing approach is compared with other approaches and its potential benefits are described and quantified. Moreover, the optimization of the starting times of a route at the distribution center is evaluated. Finally, the trade-off between solution quality and calculation time is discussed. Numerous test instances are used, both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment, which is usually the case on European road networks.

A queueing approach to optimal resource replication in wireless sensor networks

We develop a queueing model for analyzing resource replication strategies in wireless sensor networks. The model can be used to minimize either the total transmission rate of the network (an energy-centric approach) or to ensure the proportion of query failures does not exceed a predetermined threshold (a failure-centric approach). The model explicitly considers the limited availability of network resources, as well as the frequency of resource requests and query deadlines, to determine the optimal replication strategy for a network resource. While insufficient resource replication increases query failures and transmission rates, replication levels beyond the optimum result in only marginal decreases in the proportion of query failures at a cost of higher total energy expenditure and network traffic.

Long‐term and short‐term customer reaction: a two‐stage queueing approach

AbstractWe analyse a simple, deterministic queueing system with feedback. We model a customer's decision to seek service as a two‐stage process: (1) deciding whether or not to use a facility (become a customer), and (2) deciding the frequency of use (daily, weekly, monthly, etc.). We consider both a constant and a deterministic, time‐dependent usage pattern. We ignore variability, and focus on three forms of feedback: (1) the service rate increases as queue length increases; (2) the frequency with which customers use the service depends on customers' perception of recent waiting times; and (3) the number of customers depends on their perception of long‐term average waiting time. Although highly stylised, this model captures the essential features of many real‐life systems whose average arrival rate varies over time. The main conclusions are that more capacity can make the system less manageable, and a stronger external cycle can be a stabilising factor. The model turns out to be remarkably robust to external disturbances. Copyright © 2006 John Wiley &amp; Sons, Ltd.

A Queueing Framework for Routing Problems with Time-dependent Travel Times

Assigning and scheduling vehicle routes in a dynamic environment is a crucial management problem. Despite numerous publications dealing with efficient scheduling methods for vehicle routing, very few addressed the inherent stochastic and dynamic nature of travel times. In this paper, a vehicle routing problem with time-dependent travel times due to potential traffic congestion is considered. The approach developed introduces the traffic congestion component based on queueing theory. This is an innovative modelling scheme to capture the stochastic behavior of travel times as it generates an analytical expression for the expected travel times as well as for the variance of the travel times. Routing solutions that perform well in the face of the extra complications due to congestion are developed. These more realistic solutions have the potential to reduce real operating costs for a broad range of industries which daily face routing problems. A number of datasets are used to illustrate the appropriateness of the novel approach. Moreover it is shown that static (or time-independent) solutions are often infeasible within a congested traffic environment which is generally the case on European road networks. Finally, the effect of travel time variability (obtained via the queueing approach) is quantified for the different datasets.

Towards a unified process model for graphemic buffer disorder and deep dysgraphia

Models based on the competitive queuing (CQ) approach can explain many of the effects on dysgraphic patients' spelling attributed to disruption of the “graphemic output buffer”. Situating such a model in the wider spelling system, however, raises the question of what happens when input to the buffer (e.g., from a semantic system) is degraded while the buffer remains intact. We present a preliminary exploration of predictions following from the CQ approach. We show that the CQ account of the graphemic buffer predicts and explains the finding that deep dysgraphic patients generally show features of graphemic buffer disorder, as disrupted input from a damaged semantic system has an inevitable effect upon the functioning of the buffer. The approach also explains the most salient differences between the two syndromes, which are seen as consequences of the difference between an intact sequence generation system operating on degraded input versus a damaged sequencing system operating on intact input.

Empirical validation of a queueing approach to uninterrupted traffic flows

In this paper, the use of queueing theory for modeling uninterrupted traffic flows is evaluated. Empirical data on speeds and flows are used to evaluate speeds generated by the different queueing models. Using the Theil inequality coefficient as evaluation criterion, the speeds generated by the queueing models are compared to the empirical speeds. Queueing models that best fit the observed speeds are obtained. It appears that traffic flow on a highway during non-congested hours is best described using a M/G/1 queueing model. During the congested hours however, the state dependent queueing GI/G/z models are more realistic. Because the queueing models describe the empirical data well, they can also be used to evaluate potential improvements in existing traffic conditions.

Impact of Temporary Seaport Closures on Freight Supply Chain Costs

A decision model designed to help stakeholders quantify the productivity impacts of temporary closures of a container seaport on global supply chains is presented. Such closures have many potential causes, including natural disasters, labor disruptions and shortages, and security-related disruptions. Temporary closures result in highly variable product delivery lead times and thus increase supply chain inventory management costs. The decision model is a Markov decision process that determines an optimal inventory management policy and its resultant long-run average cost for a firm operating a supply chain that uses a seaport subject to unexpected closure. Since short port closures typically lead to ships waiting to off-load containers, a simple deterministic queuing approach is used to model port freight processing dynamics. The results of a numerical study indicate that the expected length of a seaport closure more negatively affects supply chain productivity than the probability of closure, for example, causing increases in expected supply chain inventory holding and penalty costs of up to 136%. Furthermore, supply chain firms that do not properly account for port closures during inventory planning can expect significant inventory holding and penalty cost increases, which in some cases eliminate positive operating income. The results indicate the importance of contingency planning and disruption management for all stakeholders, including private firms and public agencies. In addition, results of the study point to the economic importance of investment to increase the processing capabilities of highly utilized seaports immediately following a port closure, when resultant congestion is at its worst levels.

Impact of Temporary Seaport Closures on Freight Supply Chain Costs

A decision model designed to help stakeholders quantify the productivity impacts of temporary closures of a container seaport on global supply chains is presented. Such closures have many potential causes, including natural disasters, labor disruptions and shortages, and security-related disruptions. Temporary closures result in highly variable product delivery lead times and thus increase supply chain inventory management costs. The decision model is a Markov decision process that determines an optimal inventory management policy and its resultant long-run average cost for a firm operating a supply chain that uses a seaport subject to unexpected closure. Since short port closures typically lead to ships waiting to off-load containers, a simple deterministic queuing approach is used to model port freight processing dynamics. The results of a numerical study indicate that the expected length of a seaport closure more negatively affects supply chain productivity than the probability of closure, for example, causing increases in expected supply chain inventory holding and penalty costs of up to 136%. Furthermore, supply chain firms that do not properly account for port closures during inventory planning can expect significant inventory holding and penalty cost increases, which in some cases eliminate positive operating income. The results indicate the importance of contingency planning and disruption management for all stakeholders, including private firms and public agencies. In addition, results of the study point to the economic importance of investment to increase the processing capabilities of highly utilized seaports immediately following a port closure, when resultant congestion is at its worst levels.