Dynamic Queues Research Articles

Simulation of Heuristics for Automated Guided Vehicle Task Sequencing with Resource Sharing and Dynamic Queues

Automated guided vehicles (AGVs) stand out as a paradigmatic application of Industry 4.0, requiring the seamless integration of new concepts and technologies to enhance productivity while reducing labor costs, energy consumption, and emissions. In this context, specific industrial use cases can present a significant technological and scientific challenge. This study was inspired by a real industrial application for which the existing AGV literature did not contain an already well-studied solution. The problem is related to the sequencing of assigned tasks, where the queue formation dynamics and the resource sharing define the scheduling. The combinatorial nature of the problem requires the use of advanced mathematical tools such as heuristics, simulations, or a combination of both. A heuristic procedure was developed that generates candidate task sequences, which are, in turn, evaluated in a discrete-event simulation model developed in Simul8. This combined approach allows high-quality solutions to be generated and realistically evaluated, even graphically, by stakeholders and decision makers. A number of computational experiments were developed to validate the proposed method, which opens up some future lines of research, especially when considering stochastic settings.

USER: Unified Semantic Enhancement With Momentum Contrast for Image-Text Retrieval.

As a fundamental and challenging task in bridging language and vision domains, Image-Text Retrieval (ITR) aims at searching for the target instances that are semantically relevant to the given query from the other modality, and its key challenge is to measure the semantic similarity across different modalities. Although significant progress has been achieved, existing approaches typically suffer from two major limitations: (1) It hurts the accuracy of the representation by directly exploiting the bottom-up attention based region-level features where each region is equally treated. (2) It limits the scale of negative sample pairs by employing the mini-batch based end-to-end training mechanism. To address these limitations, we propose a Unified Semantic Enhancement Momentum Contrastive Learning (USER) method for ITR. Specifically, we delicately design two simple but effective Global representation based Semantic Enhancement (GSE) modules. One learns the global representation via the self-attention algorithm, noted as Self-Guided Enhancement (SGE) module. The other module benefits from the pre-trained CLIP module, which provides a novel scheme to exploit and transfer the knowledge from an off-the-shelf model, noted as CLIP-Guided Enhancement (CGE) module. Moreover, we incorporate the training mechanism of MoCo into ITR, in which two dynamic queues are employed to enrich and enlarge the scale of negative sample pairs. Meanwhile, a Unified Training Objective (UTO) is developed to learn from mini-batch based and dynamic queue based samples. Extensive experiments on the benchmark MSCOCO and Flickr30K datasets demonstrate the superiority of both retrieval accuracy and inference efficiency. For instance, compared with the existing best method NAAF, the metric R@1 of our USER on the MSCOCO 5K Testing set is improved by 5% and 2.4% on caption retrieval and image retrieval without any external knowledge or pre-trained model while enjoying over 60 times faster inference speed. Our source code will be released at https://github.com/zhangy0822/USER.

DynaQ: online learning from imbalanced multi-class streams through dynamic sampling

AbstractOnline supervised learning from fast-evolving data streams, particularly in domains such as health, the environment, and manufacturing, is a crucial research area. However, these domains often experience class imbalance, which can skew class distributions. It is essential for online learning algorithms to analyze large datasets in real-time while accurately modeling rare or infrequent classes that may appear in bursts. While methods have been proposed to handle binary class imbalance, there is a lack of attention to multi-class imbalanced settings with varying degrees of imbalance in evolving streams. In this paper, we present the Dynamic Queues (DynaQ) algorithm for online learning in multi-class imbalanced settings to fill this knowledge gap. Our approach utilizes a batch-based resampling method that creates an instance queue for each class to balance the number of instances. We maintain a queue threshold and remove older samples during training. Additionally, we dynamically oversample minority classes based on one of four rate parameters: recall, F1-score, $$\kappa _m$$ κ m , and Euclidean distance. Our learning algorithm consists of an ensemble that uses sliding windows and a soft voting schema while incorporating a drift detection mechanism. Our experimental results demonstrate the superiority of the DynaQ approach over state-of-the-art methods.

Learning-Based Computation Offloading for IoRT Through Ka/Q-Band Satellite–Terrestrial Integrated Networks

In this article, we propose a multilayer Ka/Q-band satellite–terrestrial integrated network for the Internet of Remote Things (IoRT) to achieve a high transmission rate with communication robustness in dynamic network environments. Under this architecture, we investigate how to jointly manage the offloading path selection and resource allocation to offload computation-intensive and delay-sensitive tasks in the IoRT. Considering continuous low earth orbit (LEO) satellite movements and Markovian rainfall changes, the computation offloading problem is described as a Markov decision process (MDP) formulation with the objective of maximizing the number of offloaded tasks with satisfied delay requirements and minimizing the power consumption of the LEO satellites. A deep reinforcement learning (DRL) approach is leveraged to make optimal decisions by taking account of dynamic queues of IoRT devices, channel conditions that vary with rainfall intensities and satellite positions, and computing capabilities of ground stations. Extensive simulations are conducted to validate the effectiveness and superiority of our proposed scheme.

Impact of possible random factors on queue behaviors of passengers and taxis at taxi stand of transport hubs

The waiting processes of both passengers and taxis at taxi stand of transport hubs are a complicated random queueing problem because of some typical random characteristics, including the random number of passengers carried by per taxi, random arrival behaviors of passengers, and taxis, random length of boarding time for a batch of passengers, etc. In particular, the arriving process of passengers at transport hubs often has multiple uncertain peak hours within a day, which makes the waiting times of both the passengers and the taxis highly variable and unpredictable at a certain time. Hence, it is necessary to explore and understand the dynamic queues of the passengers and taxis through fully studying the random nature of this system. In this paper, a matching model with two random inputs is developed to simulate the dynamic queueing behaviors of passengers and taxis by considering the random features of the matching number of passengers, matching time, and time-dependent arrival processes, etc. We obtain some important quantities of queues along the time. A series of numerical simulations is conducted to find how the queues of passengers and taxis are affected by the most possible factors. The proposed model in this paper can provide a methodological possibility of clarifying the dynamic queues of passengers and taxis for not only the computer simulation but also the statistical inference, and will further help to improve the service of the passenger–taxi system.

Modeling and uncovering the passenger–taxi dynamic queues at taxi station with multiple boarding points using a Markovian environment

The arriving of passengers at transport hubs often has a highly variable demand within a day; it makes the waiting times of both passengers and taxis unpredictable at taxi station of transport hubs. Aiming to uncover the queue dynamics of passengers and taxis at taxi station with multiple boarding points, this paper proposes a passenger–taxi matching queueing model by incorporating much more realistic descriptions including the number of boarding points, matching time, and variable arrivals over time, etc. In this paper, the passengers and taxis related queueing indices are derived, such as the overall average numbers of queueing passengers and taxis, the overall average waiting times of passengers and taxis, etc. In particular, the highly fluctuating queues of passengers and taxis at taxi station are respectively formulated by introducing the indices of conditional average numbers of both the queueing passengers and taxis at a given time. We also conduct a series of numerical experiments; the quantitative results will help us to understand the high variability of passengers’ and taxis’ queues at taxi station due to the variable arrival of passengers over time within a day. The obtained indices in this paper can provide an effective tool to evaluate the service level of passenger–taxi system, and thus contribute to improve the service of the passenger–taxi system at transport hubs according to the airport (or train, coach) schedule, weather, and traffic condition, etc.

A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment

Task scheduling is one of the most challenging aspects to improve the overall performance of cloud computing and optimize cloud utilization and Quality of Service (QoS). This paper focuses on Task Scheduling optimization using a novel approach based on Dynamic dispatch Queues (TSDQ) and hybrid meta-heuristic algorithms. We propose two hybrid meta-heuristic algorithms, the first one using Fuzzy Logic with Particle Swarm Optimization algorithm (TSDQ-FLPSO), the second one using Simulated Annealing with Particle Swarm Optimization algorithm (TSDQ-SAPSO). Several experiments have been carried out based on an open source simulator (CloudSim) using synthetic and real data sets from real systems. The experimental results demonstrate the effectiveness of the proposed approach and the optimal results is provided using TSDQ-FLPSO compared to TSDQ-SAPSO and other existing scheduling algorithms especially in a high dimensional problem. The TSDQ-FLPSO algorithm shows a great advantage in terms of waiting time, queue length, makespan, cost, resource utilization, degree of imbalance, and load balancing.

A Multi-Objective Compounded Local Mobile Cloud Architecture Using Priority Queues to Process Multiple Jobs.

As a result of the greatly increased use of mobile devices, the disadvantages of portable devices have gradually begun to emerge. To solve these problems, the use of mobile cloud computing assisted by cloud data centers has been proposed. However, cloud data centers are always very far from the mobile requesters. In this paper, we propose an improved multi-objective local mobile cloud model: Compounded Local Mobile Cloud Architecture with Dynamic Priority Queues (LMCpri). This new architecture could briefly store jobs that arrive simultaneously at the cloudlet in different priority positions according to the result of auction processing, and then execute partitioning tasks on capable helpers. In the Scheduling Module, NSGA-II is employed as the scheduling algorithm to shorten processing time and decrease requester cost relative to PSO and sequential scheduling. The simulation results show that the number of iteration times that is defined to 30 is the best choice of the system. In addition, comparing with LMCque, LMCpri is able to effectively accommodate a requester who would like his job to be executed in advance and shorten execution time. Finally, we make a comparing experiment between LMCpri and cloud assisting architecture, and the results reveal that LMCpri presents a better performance advantage than cloud assisting architecture.

The GPU on the simulation of cellular computing models

Membrane Computing is a discipline aiming to abstract formal computing models, called membrane systems or P systems, from the structure and functioning of the living cells as well as from the cooperation of cells in tissues, organs, and other higher order structures. This framework provides polynomial time solutions to NP-complete problems by trading space for time, and whose efficient simulation poses challenges in three different aspects: an intrinsic massively parallelism of P systems, an exponential computational workspace, and a non-intensive floating point nature. In this paper, we analyze the simulation of a family of recognizer P systems with active membranes that solves the Satisfiability problem in linear time on different instances of Graphics Processing Units (GPUs). For an efficient handling of the exponential workspace created by the P systems computation, we enable different data policies to increase memory bandwidth and exploit data locality through tiling and dynamic queues. Parallelism inherent to the target P system is also managed to demonstrate that GPUs offer a valid alternative for high-performance computing at a considerably lower cost. Furthermore, scalability is demonstrated on the way to the largest problem size we were able to run, and considering the new hardware generation from Nvidia, Fermi, for a total speed-up exceeding four orders of magnitude when running our simulations on the Tesla S2050 server.

A THEORY OF MULTIPLE VEHICLE TYPE DYNAMIC MARGINAL COST CONSIDERING DEPARTURE TIME CHOICES

The analysis of single vehicle type dynamic marginal cost is extended to multiple vehicle type dynamic one based on time‐dependent multiple vehicle type queue analysis at a bottleneck. First, a dynamic link model to rep‐ resent the interactions between cars and trucks is provided. Then, the analytic expression of a multiple vehicle type dynamic marginal cost function considering departure time choices is deduced under congested and un‐congested conditions and consequently, a dynamic toll function is given. A heuristic algorithm is introduced to solve multiple vehicle type dynamic queues and toll under system optimum and user equilibrium conditions taking into account traveler's departure time. A numerical example shows that a dynamic congestion toll can diminish queues and improve system conditions when traffic demand is not changed.

On the relation between the mean and variance of delay in dynamic queues with random capacity and demand

This paper investigates the distribution of delays during a repeatedly occurring demand peak in a congested facility with random capacity and demand, such as an airport or an urban road. Congestion is described in the form of a dynamic queue using the Vickrey bottleneck model and assuming Nash equilibrium in arrival times. The paper shows that the expected delay and the variance of delay vary differently over time during the peak and must hence be considered separately. The paper gives some characterization of how the expected delay and the variance of delay are related, which explain the looping phenomenon that has now been observed a number of times. Empirical illustration is provided.

Queries on two headed fast disks with smart track management using dynamic scans

Scan policies for two-headed disks using dynamic queues are simulated. Older format and latest format disks with one and two heads are compared. The results confirm a recent finding that the optimal head separation distance is about 0.47 N ( N is the number of logical cylinders on the disk), rather than [ N 2 ]. The maximum batch size need be no greater than 50 requests. Two heads are shown to reduce average service time by more than a third. Using cache memory to manage the accessing of sectors out of track sequence (smart tracking) reduces average service time for longer (near full track) requests by more than a third.

A Based-Bottleneck Multiple Vehicle Type Dynamic Marginal Cost Model and Algorithm

Single vehicle type dynamic marginal cost model is extended to multiple vehicle type dynamic one based on time-dependent multiple vehicle type queue analysis at a bottleneck. A dynamic link model is presented to model interactions between cars and trucks, given the link consists of two distinct segments. The first segment is the running segment on which cars (trucks) run at their free-flow speeds and the second segment is the exit queue segment. A car or a truck is assumed to be a point without length. The class-specific pi parameter is used to transform the effect of truck into passenger car equivalents, so the exit flow of cars and trucks can be calculated according to the exit capacity of a bottleneck. The analytic expression of multiple vehicle type dynamic marginal cost function is deduced under congested and uncongested conditions. Then a heuristic algorithm is presented in solving multiple vehicle type dynamic queues, tolls under system optimum and user equilibrium conditions. The numerical example illustrates the simplicity and applicability of the proposed approach.

A Taxicab Problem with Time-Dependent Arrival Rates

Previous article Next article A Taxicab Problem with Time-Dependent Arrival RatesSamuel M. GiveenSamuel M. Giveenhttps://doi.org/10.1137/1005031PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] David G. Kendall, Some problems in the theory of queues, J. Roy. Statist. Soc. Ser. B., 13 (1951), 151–173; discussion: 173–185 MR0047944 (13,957a) 0355.65084 Google Scholar[2] J. G. Skellam, The frequency distribution of the difference between two Poisson variates belonging to different populations, J. Roy. Statist. Soc. (N.S.), 109 (1946), 296– MR0020750 (8,592e) 0063.07068 CrossrefISIGoogle Scholar[3] J. M. Dobbie, A double-ended queuing problem of Kendall, Operations Res., 9 (1961), 755–757 CrossrefISIGoogle Scholar[4] T. C. Fry, Probability and its engineering uses, D. Van Nostrand Company, New York, 1928, 232–235 Google Scholar[5] Eugene Jahnke and , Fritz Emde, Tables of Functions with Formulae and Curves, Dover Publications, New York, 1943, 226–233 MR0008332 (4,281j) Google Scholar Previous article Next article FiguresRelatedReferencesCited ByDetails On a many-to-many matched queueing system with flexible matching mechanism and impatient customersJournal of Computational and Applied Mathematics, Vol. 416 | 1 Dec 2022 Cross Ref Equilibrium and socially optimal strategies of a double-sided queueing system with two-mass point matching timeQuality Technology & Quantitative Management, Vol. 2300 | 9 August 2022 Cross Ref Upper bound on the rate of convergence and truncation bound for non-homogeneous birth and death processes on ZApplied Mathematics and Computation, Vol. 423 | 1 Jun 2022 Cross Ref On some integral equations for the evaluation of first-passage-time densities of time-inhomogeneous birth-death processesApplied Mathematics and Computation, Vol. 422 | 1 Jun 2022 Cross Ref Double-ended queues with non-poisson inputs and their effective algorithmsComputers & Operations Research, Vol. 62 | 1 Apr 2022 Cross Ref Optimal pricing and service capacity management for a matching queue problem with loss-averse customersOptimization, Vol. 70, No. 10 | 12 June 2020 Cross Ref Impact of possible random factors on queue behaviors of passengers and taxis at taxi stand of transport hubsPhysica A: Statistical Mechanics and its Applications, Vol. 580 | 1 Oct 2021 Cross Ref Modeling and uncovering the passenger–taxi dynamic queues at taxi station with multiple boarding points using a Markovian environmentPhysica A: Statistical Mechanics and its Applications, Vol. 572 | 1 Jun 2021 Cross Ref On a class of birth-death processes with time-varying intensity functionsApplied Mathematics and Computation, Vol. 379 | 1 Aug 2020 Cross Ref Bell Polynomial Approach for Time-Inhomogeneous Linear Birth–Death Process with ImmigrationMathematics, Vol. 8, No. 7 | 9 July 2020 Cross Ref Equilibrium and Optimization in a Double-Ended Queueing System with Dynamic ControlJournal of Advanced Transportation, Vol. 2019 | 19 Mar 2019 Cross Ref Diffusion approximations for double-ended queues with reneging in heavy trafficQueueing Systems, Vol. 91, No. 1-2 | 6 October 2018 Cross Ref Strategic behavior and social optimization in a double-ended queue with gated policyComputers & Industrial Engineering, Vol. 114 | 1 Dec 2017 Cross Ref Optimization and strategic behavior in a passenger–taxi service systemEuropean Journal of Operational Research, Vol. 249, No. 3 | 1 Mar 2016 Cross Ref Study of a passenger-taxi queueing system with nonzero matching time2015 12th International Conference on Service Systems and Service Management (ICSSSM) | 1 Jun 2015 Cross Ref Double-Sided Batch Queues with Abandonment: Modeling Crossing NetworksOperations Research, Vol. 62, No. 5 | 1 Oct 2014 Cross Ref BIVARIATE EXTENSIONS OF SKELLAM'S DISTRIBUTIONProbability in the Engineering and Informational Sciences, Vol. 28, No. 3 | 17 March 2014 Cross Ref Queueing theoryJournal of Soviet Mathematics, Vol. 2, No. 1 | 1 Jan 1974 Cross Ref Volume 5, Issue 2| 1963SIAM Review113-202 History Submitted:19 February 1962Published online:01 August 2006 InformationCopyright © 1963 © Society for Industrial and Applied MathematicsPDF Download Article & Publication DataArticle DOI:10.1137/1005031Article page range:pp. 119-127ISSN (print):0036-1445ISSN (online):1095-7200Publisher:Society for Industrial and Applied Mathematics