Time In Queue Research Articles

A scheduling mechanism for hybrid flow shops with reworks under general queue time limits

This study addresses multi-stage hybrid flow shop scheduling in which a job is reworked if the queue time between two arbitrary stages exceeds an upper limit. The problem is to determine the allocations of jobs to machines at each stage and the start times of jobs and rework setups/operations when incurred. A mixed integer programming model is proposed for each of the makespan and the total tardiness measures. Then, because the problem is NP-hard, a scheduling mechanism is proposed that consists of three phases: (a) filtering the jobs to be delayed; (b) searching the jobs to be reworked; and (c) dispatching non-delayed and delayed jobs sequentially. Simulation results show that the mechanism proposed in this study outperforms the conventional dispatching approach in the high rework setup time case for the makespan problem and low/high setup time cases for the tardiness problem. The best priority rules of the mechanism under each of the measures are also reported.

Congestion-aware delay-guaranteed scheduling and routing with renewal optimization

The emergence of time-critical applications imposes great challenges on traditional best-effort data networks. Such applications demand delay-guaranteed services with different granularity. In this paper, we propose a novel fully distributed approach that provides delay-guaranteed services at the packet level within an autonomous system. Specifically, we adopt priority queues with fixed buffer sizes to provide differentiated services and per-hop delay upper bound and explore path diversity in the network to achieve multiplex gain. Network congestion is a major cause of long delay. To address this issue, a virtual queue manager is deployed at each node in the network to exchange their local queue information with neighbors periodically. The exchanged queue information reflects the congestion status of the neighborhood so the nodes can avoid congested routes in making routing decisions. Given the rich control space including routing and queuing decisions, we aim at maximizing the overall network utility. Due to the randomness caused by network dynamics, we transform the utility maximization problem into renewal optimization which is solved at each node. A delay laxity-based reward function and a weighted queue time cost are designed to characterize each decision. To solve the renewal optimization problem, an algorithm named DSROpt is proposed using an iterative approach. Extensive experiments are conducted to verify the performance of the proposed solution using NS-3. Simulation results show that the proposed solution can guarantee packet-level delay while achieving significant performance improvements in goodput and network utility over the state-of-the-art.

Queuing system without queue and deterministic service time

Today, queueing systems that describe failures in the process of software testing are subject to extensive research. Such systems involve a dependence between the rate of an input Poisson stream and the rate of exponentially distributed handling time. Using this dependence, system load levelling procedures are defined. However, such a model and method of research are not suitable for systems with a deterministic handling time. Therefore, this paper examines the dependence of the Poisson distribution parameter of the number of requests in a system from the deterministic handling time in the presence of a peak rate of the input stream. This dependence is examined analytically and numerically. It is shown that a reduction of the handling time levels the peak number of customers in the system

Multi objective queue theory based optimal planning of rapid charging stations and distributed generators in coupled transportation and distribution network

AbstractThe environment is adversely affected by greenhouse gas (GHG) emissions from conventional combustion engines. In this regard, electric vehicles (EVs) are a viable transportation option that benefit the environment in reducing GHG emissions. Although the installation of rapid charging stations (RCSs) helps to promote EVs, installing these at improper locations in the distribution network worsens the voltage profile, increases power loss, and energy loss while travelling from EV's current location to RCS. Furthermore, RCS installation cost and waiting time at RCS need to be considered. Therefore, a two‐stage optimal planning is proposed in this article to address the issues stated above. In the first stage, simultaneous optimal planning of RCS and distributed generators is done to minimize active power loss, voltage deviation, EV user cost and to maximize voltage stability index. In the second stage, optimal number of connectors are decided to minimize the installation cost and waiting time in queue at RCS. Here, queuing model is considered to determine the waiting time. A test network of coupled IEEE 33 bus distribution system and transport network is proposed to validate the proposed methodology. Multi objective Rao algorithm (MORA) is used to solve the formulated optimization problems, and results are compared with non dominated sorting genetic algorithm (NSGA‐II) algorithm.

Knowing how satisfied/dissatisfied is far from enough: a comprehensive customer satisfaction analysis framework based on hybrid text mining techniques

PurposeThis study aims to establish a comprehensive satisfaction analysis framework by mining online restaurant reviews, which can not only accurately reveal consumer satisfaction but also identify factors leading to dissatisfaction and further quantify improvement opportunity levels.Design/methodology/approachAdopting deep learning, Cross-Bidirectional Encoder Representations Transformers (BERT) model is developed to measure customer satisfaction. Furthermore, opinion mining technique is used to extract consumers’ opinions and obtain dissatisfaction factors. Furthermore, the opportunity algorithm is introduced to quantify attributes’ improvement opportunity levels. A total of 19,133 online reviews of 31 restaurants in Universal Beijing Resort are crawled to validate the framework.FindingsResults demonstrate the superiority of Cross-BERT model compared to existing models such as sentiment lexicon-based model and Naïve Bayes. More importantly, after effectively unveiling customer dissatisfaction factors (e.g. long queuing time and taste salty), “Dish taste,” “Waiters’ attitude” and “Decoration” are identified as the three secondary attributes with the greatest improvement opportunities.Practical implicationsThe proposed framework helps managers, especially in the restaurant industry, accurately understand customer satisfaction and reasons behind dissatisfaction, thereby generating efficient countermeasures. Especially, the improvement opportunity levels also benefit practitioners in efficiently allocating limited business resources.Originality/valueThis work contributes to hospitality and tourism literature by developing a comprehensive customer satisfaction analysis framework in the big data era. Moreover, to the best of the authors’ knowledge, this work is among the first to introduce opportunity algorithm to quantify service improvement benefits. The proposed Cross-BERT model also advances the methodological literature on measuring customer satisfaction.

Effect of alkanethiol chain length on the oxidation resistance of self-assembled monolayer passivated Ge(100) surfaces

• The resistance to oxidation is dependent on the length of thiol chain • Oxidation resistance increases with increasing chain length. • Acceptable oxidation resistance down the C4 chain length. • Van der Waals interactions are significant for SAM stability. The use of self-assembled monolayers (SAMs) of heteroalkanes as passivation layers to protect against oxidation has been studied on a variety of materials. However, typically heteroalkanes with C8 and longer have been used, since the contributing van der Waals between the carbon chains is a significant stabilising force and the longer the chain the greater the force. The industry requirement for passivating semiconducting materials is that they remain oxide free for a queue time of 24 h. The remit of this study is to explore whether passivation using shorter chain alkanethiols, which would be beneficial for reducing carbon, will work to prevent re-oxidation over this time frame. A series of 1-alkanethiols, with chain lengths from C2 to C12 are used to create SAMs on Ge(100) and a study of the re-oxidation of the passivated Ge upon exposure to ambient conditions is undertaken in an effort to determine how chain length effects oxidation resistance of the passivated Ge. X-ray photoelectron spectroscopy is used, complimented by water contact angle measurements to show that the longer thiol molecules outperform their shorter-chain counterparts at inhibiting re-oxidation over 168 h of exposure to ambient. Nonetheless, Ge surfaces passivated by the short-chain thiols, down to C4, still display acceptable resistance to re-oxidation. Finally, a detailed summary of density functional theory simulations whereby the most stable SAM structures and coverages are explored.

Joint decision-making of virtual module formation and scheduling considering queuing time

Formation and scheduling are the most important decisions in the virtual modular manufacturing system; however, the global performance optimization of the system may be sacrificed via the superposition of two independent decision-making results. The joint decision of formation and scheduling is very important for system design. Complex and discrete manufacturing enterprises such as shipbuilding and aerospace often comprise multiple tasks, processes, and parallel machines, resulting in complex routes. The queuing time of parts in front of machines may account for 90% of the production cycle time. This study established a weighted allocation model of a formation-scheduling joint decision problem considering queuing time in system. To solve this nondeterministic polynomial (NP) problem, an adaptive differential evolution-simulated annealing (ADE-SA) algorithm is proposed. Compared with the standard differential evolution (DE) algorithm, the adaptive mutation factor overcomes the disadvantage that the scale of DE’s differential vector is difficult to control. The selection strategy of the SA algorithm compensates for the deficiency that DE’s greedy strategy may fall into a local optimal solution. The comparison results of four algorithms of a series of random examples demonstrate that the overall performance of ADE-SA is superior to the genetic algorithm, and average iteration, maximum completion time, and move time are 24%, 11%, and 7% lower than the average of other three algorithms, respectively. The method can generate the joint decision-making scheme with better overall performance, and effectively identify production bottlenecks through quantitative analysis of queuing time.

EV Fast Charging Station Planning Considering Competition Based on Stochastic Dynamic Equilibrium

The rapid growth of electric vehicles (EVs) and the deployment of fast charging infrastructures bring considerable impacts on the planning and operation of power systems. Integrating photovoltaic (PV) and energy storage system (ESS) with fast charging station (FCS) can alleviate the negative impacts and bring benefits to both power system operation and charging service provider. In this paper, an integrated procedure is proposed for the charging service provider to optimally plan fast charging stations integrated with PV and ESS in urban area, while taking the influence of its competitors, EV users' decision-making psychology, the uncertainties of charging demand and PV power output into consideration. Firstly, a charging demand estimation method based on average travel speed is presented. Secondly, a stochastic dynamic user choice equilibrium (SDUCE) model and its solution algorithm based on method of successive averages (MSA) are proposed. The model considers the key factors influencing the charging decision of EV users as well as the uncertainty of their perceptions of utility values. In addition, the time-varying queuing time and the temporal coupling among EVs arriving at a charging station in different time periods are also considered. Thirdly, based on the equilibrium model, a multi-scenario benefit maximization model is built from the perspective of charging service provider, and solved by genetic algorithm. Finally, a distributionally robust optimization (DRO) planning model based on the Wasserstein-metric is formulated to determine the capacities of PV and ESS. The proposed planning method is tested using real traffic data of Shenzhen City. Simulation results verify the effectiveness of the proposed method.

Optimal dispatch of hydrogen/electric vehicle charging station based on charging decision prediction

The hydrogen/electric vehicle charging station (HEVCS) is widely regarded as a highly attractive system for facilitating the popularity of hydrogen and electric vehicles in the future. However, conventional optimal dispatch of HEVCS could lead to poor performance due to the lack of adequate consideration of vehicle charging decision behaviours and neglection of the impacts of different information sources on it. This paper investigates a charging demand prediction method that considers multi-source information and proposes a multi-objective optimal dispatching strategy of HEVCS. First, an information interaction framework of integrated road network, vehicles and HEVCS is introduced. Road network model and HEVCS model are established based on the proposed framework. To improve the flexibility of dispatch, two charging modes are designed, which are intended to guide drivers to adjust their consumption behaviour by electricity price incentives. Furthermore, psychologically based hybrid utility-regret decision model and Weber-Fechner (W–F) stimulus model are developed to reasonably predict drivers' choice of charging stations and charging modes. The daily revenue of HEVCS and the total queuing time of drivers are the objective functions considered in this paper simultaneously. The above multi-objective optimization results that the proposed strategy can effectively improve the benefits of HEVCS and reduce energy waste. Additionally, this paper discusses the results of a sensitivity analysis conducted by varying incentive discount, which reveals the combined benefits of the HEVCS and the vehicles are effectively increased by setting reasonable incentive discounts.

Coupled Control of Traffic Signal and Connected Autonomous Vehicles at Signalized Intersections

To enhance the traffic operation efficiency at signalized intersections, a model for coupled control of traffic signals and connected autonomous vehicles at isolated signalized intersections is proposed. This model estimates the time that CAVs reach stop lines with real-time information about the speed and position of CAVs. The arrival time is leveraged to optimize traffic signal timing by rolling horizon, with the maximization of phase saturation as the optimization objective. Based on the optimized traffic signal timing, the speed profile of CAVs is optimized by a linear integer programming, with the maximization of speed at the moment of reaching the stop line as the optimization objective. Through the coupled control of travel speed and the traffic signal, CAVs can pass through the intersection safely, efficiently, and smoothly. NetLogo, a multiagent microscopic simulator, is developed to test this strategy, and an intersection in Weihai is taken for verification and analysis lastly. The simulation results demonstrate that, compared with the fixed traffic signal timing control and the model optimizing only speed profile of CAVs, the proposed model can reduce the average number of stops by 47.0% and the queuing time by 41.3%. In addition, the optimization is better during off-peak hours, about 10% higher than the peak hours.

A queueing-inventory model to control the congestion of patients and medical waste in the medical centers, a case study

During epidemics, controlling the patients’ congestion is a way to reduce disease spreading. Raising medical demands converts hospitals into one of the sources of disease outbreaks. The long patient waiting time in queues to receive medical services leads to more casualties. The rise of patients increases their waste, which is another source of disease outbreak. In this study, a mathematical model is developed to control patients’ congestion in a medical center and manage their waste, considering environmental issues. Besides a queueing system controlling the patients’ congestion in the treatment center, another queue is considered for vehicles. An inventory model is employed to prevent waste accumulation. The developed model is solved and reaches an exact solution in small size, and obtains an acceptable solution in large size using the Grasshopper algorithm. A case study is considered to demonstrate the model’s applicability. Also, Sensitivity analysis and valuable managerial insights are presented.

Research on a Dynamic Task Update Assignment Strategy Based on a “Parts to Picker” Picking System

Order picking is a crucial operation in the storage industry, with a significant impact on storage efficiency and cost. Responding quickly to customer demands and shortening picking time is crucial given the random nature of order arrival times and quantities. This paper presents a study on the order-picking process in a distribution center, employing a “parts-to-picker” system, based on dynamic order batching and task optimization. Firstly, dynamic arriving orders with uncertain information are transformed into static picking orders with known information. A new method of the hybrid time window is proposed by combining fixed and variable time windows, and an order consolidation batch strategy is established with the aim of minimizing the number of target shelves for picking. A heuristic algorithm is designed to select a shelf selection model, taking into account the constraint condition that the goods on the shelf can meet the demand of the selection list. Subsequently, task division of multi-AGV is carried out on the shelf to be picked, and the matching between the target shelf and the AGVs, as well as the order of the AGVs to complete the task of picking, is determined. A scheduling strategy model is constructed to consider the task completion time as the incorporation of moving time, queuing time, and picking time, with the shortest task completion time as the objective function and AGV task selection as the decision variable. The improved ant colony algorithm is employed to solve the problem. The average response time of the order batching algorithm based on a hybrid time window is 4.87 s, showing an improvement of 22.20% and 40.2% compared to fixed and variable time windows, respectively. The convergence efficiency of the improved ant colony algorithm in AGV task allocation is improved four-fold, with a better convergence effect. By pre-selecting the nearest picking station for the AGVs, the multi-AGV picking system can increase the queuing time. Therefore, optimizing the static picking station selection and dynamically selecting the picking station queue based on the queuing situation are proposed. The Flexsim simulation results show that the queue-waiting and picking completion times are reduced to 34% of the original, thus improving the flexibility of the queuing process and enhancing picking efficiency.

Steady State Analysis of Base Station Buffer Occupancy in a Cellular Mobile System

The 3rd generation partnership project (3GPP) standards organizations makes great efforts in order to reduce the latency of 5G mobile networks to the least possible extent. Recently, these networks are associated with big buffers to maximize the network utilization and minimize the wasted wireless resources. However, in existence of the TCP congestions, having bottlenecks are still expected on radio access networks (RANs) data paths. Consequently, this influences the network performance and reduces its quality of services (QoSs). Apparently, studying and improving the behavior of buffers deployed at 5G mobile networks devices can contribute to solving these problems (at least by reducing the queuing time at these buffers). In this paper, we study the buffer behavior of base stations in a 5G mobile network at steady state. We consider a cellular mobile network consisting of finite number of users (stations, terminals, and mobiles). At any time-slot, a station may be using the channel (busy) or not using the channel (idle). Since system analysis of cellular mobile networks in general form is rather complex, solutions are always obtained in closed forms or by numerical techniques. A two-dimensional traffic system for cellular mobile networks is presented, and the main performance evaluations are derived. Moreover, different moments of the base station buffer occupancy are calculated. The study reveals that there is a correlation between the state of the mobile stations (busy or idle) and the expected buffers occupancy of the base station. In addition, the results discussions demonstrate some important factors and parameters that affect the base station buffers and the overall network performance. These factors can be further worked on and controlled to obtain the least possible latency in next generation mobile networks.

CONTENT ANALYSIS ON THE GUEST EXPERIENCE OF PARK VISITORS IN ENCHANTED KINGDOM, LAGUNA

During the pandemic, the implementation of safety protocols allowed theme parks to reopen with a certain capacity which gave tourists mixed experiences during their stay at the theme park. On the other hand, the growing popularity of social media led to the creation of websites such as Facebook where online reviews can be posted on the official Facebook page of different establishments. In this study, the researchers used qualitative content analysis by utilizing Microsoft Teams Excel and the Experience Economy Theory to identify the guest experience of park visitors in theme parks during the pandemic through utilizing the 4 Realms of Experience which are Entertainment, Educational, Escapist and Esthetic. The results show that majority of the online reviews mentioned the Escapist realm while the Entertainment realm was the least mentioned experience. Furthermore, most of the online reviews were negative which mentioned “queue times” and “safety protocols” as the main factor that negatively affected their experience. This study aims to improve the management’s understanding of the guest experience during the pandemic in Enchanted Kingdom in Santa Rosa, Laguna and suggested recommendations based on the results gathered from each realm of experience to contribute in the betterment of the guest’s experiences.

ANALYZE QUEUE PERFORMANCE USING ARENA 16.0 SOFTWARE (Z Cinema Case Study)

Queues are often found in everyday life, such as waiting for cinema counters, check-in queues at airports and self-service cashier queues. Service facilities for a long time cause queues because they cannot meet the needs or capacity, so facility users have to wait. As in the example of the case of Cinema Z in Solo City has a maximum queue time of 13.6 minutes with a maximum number of queues of 11 people until served. This can cause customers to wait long enough to get movie tickets. This problem can be overcome by the need to manage the counter operator so that it can regulate when to open a new counter so that there is no queue. This research discusses the problem of queuing models at Cinema Z in Solo, Central Java Province. From the results of the analysis that can be known the performance values of the queuing system such as waiting time, number in, number out, dan number waiting.

Modeling and Simulation of Crude Oil Sea–River Transshipment System in China’s Yangtze River Basin

China’s Yangtze River Basin has an increasingly strong demand for crude oil. As a seaborne import port for crude oil, Ningbo-Zhoushan Port is under pressure to undertake the transshipment of crude oil to various oil terminals in the Yangtze River Basin. To alleviate the stress of crude oil transportation in Ningbo-Zhoushan Port, the port operator proposed the crude oil sea–river transshipment scheme in Nantong Port. Therefore, this paper aims to verify the feasibility of this scheme. We used the discrete event system modeling and entity relationship diagram method to construct the hierarchical and concept models of the Yangtze River Basin’s crude oil sea–river transportation system. Furthermore, we developed corresponding simulation modules on the Witness platform and carried out a simulation experiment of the crude oil sea–river transfer scheme. In the experiment, we analyzed the influence of the transshipment ratio on berth utilization, waiting time, and sailing time of other ports by adjusting the parameter of the transshipment ratio. The experimental results show that when the transshipment rate reaches 100%, the utilization rates of loading and unloading berth in Nantong Port are 4% and 13%, respectively, which evidences that Nantong Port has transshipment potential. At the same time, the simulation experiment’s statistical indicators, such as the utilization rate of oil berths, the queuing time of oil tankers, and the sailing time, not only confirm the feasibility of the crude oil sea–river transshipment scheme of Nantong Port but also confirm that the scheme is helpful to improve crude oil transportation efficiency. The simulation results benefit the port operation decision, and the established model and simulation module can be encapsulated and reused.

Intensive Care Strain Indicators: Recommendations for Critical Care Processes and Research Objectives

Intensive care units (ICU’s) are particularly susceptible to resource and personnel strain given the complexity and unpredictability of care. This featured prominently in the early course of the SARS-CoV-2 (COVID-19) pandemic, where poor patient outcomes were clearly linked to the increasing severity of ICU strain associated with decreased ICU capacity. Despite attempts at measuring ICU strain, there exists no operational model that ICU directors can implement to monitor strain or researchers can use to examine its effects. This article reviews ICU strain indicators including census load (census, acuity, and admissions), ICU flow characteristics (admission/discharge criteria, sufficient staffing levels, and ICU performance), and consequence mediators (ICU queuing time and high-risk discharges) with attention to common themes and measures. Census load data suggests mortality risk is greater when ICU census starts higher, has high overall acuity, and with greater numbers of admissions especially when they arrive close together. Optimal ICU flow depends on maintaining a “strain mindset” when prioritizing patients, optimal ICU professional staffing, and maintaining high level ICU performance processes. Finally, delaying ICU admissions beyond six hours, or “after hours” or rushed ICU discharges result in increased mortality risk. Incorporating these ICU strain factors into an outcomes-focused model is proposed based on a conceptual framework with future research objectives recommended.

Heavy-traffic single-server queues and the transform method

Heavy-traffic limit theory is concerned with queues that operate close to criticality and face severe queueing times. Let W denote the steady-state waiting time in the GI/G/1 queue. Kingman (1961) showed that W, when appropriately scaled, converges in distribution to an exponential random variable as the system’s load approaches 1. The original proof of this famous result uses the transform method. Starting from the Laplace transform of the pdf of W (Pollaczek’s contour integral representation), Kingman showed convergence of transforms and hence weak convergence of the involved random variables. We apply and extend this transform method to obtain convergence of moments with error assessment. We also demonstrate how the transform method can be applied to so-called nearly deterministic queues in a Kingman-type and a Gaussian heavy-traffic regime. We demonstrate numerically the accuracy of the various heavy-traffic approximations.

Managing customer contact centers with delay announcements and automated service

This article presents a study of the queueing system resulting from the service of customers in a generic customer contact center that has both automated service and traditional human agent service. Contact center managers would prefer customers use the provided automated service to reduce average customer queuing time as well as staffing costs of required human agent service agents. However, forcing customers to use automated service may lead to customer dissatisfaction. In this study, we propose to increase the use of automated service by using delay announcements as a tool to help guide customers to use automated service who would otherwise have chosen agent service. We present a stochastic optimization formulation to determine the optimal staffing level and delay announcement policy, with an objective to minimize staffing cost, customer balking, and reneging penalty. Closed-form solutions are derived using a fluid approximation, and the asymptotic optimality of the solutions is established. The obtained optimal policies are demonstrated by numerical experiments. A key managerial insight is that with automated service, the optimal delay announcement policy no longer satisfies the well-known information-consistent balking property for a customer contact center using delay announcement without automated service. Instead, there is a new equilibrium delay policy that uses an “extreme positive bias” to reduce the length of the queue for human agent service to zero.

ForETaxi: Data-Driven Fleet-Oriented Charging Resource Allocation in Large-Scale Electric Taxi Networks

Charging processes are the key to promoting electric taxis and improving their operational efficiency due to frequent charging activities and long charging time. Nevertheless, optimizing charging resource allocation in real time is extremely challenging because of uneven charging demand/supply distributions, heuristic-based charging behaviors of drivers, and city-scale of the fleets. The existing solutions have utilized real-time contextual information for charging recommendation, but they do not consider the much-richer fleet information, leading to the suboptimal individual-based charging recommendation. In this paper, we design a data-driven fleet-oriented charging recommendation system for charging resource allocation called ForETaxi for electric taxis , which aims to minimize the overall charging overhead for the entire fleet, instead of individual vehicles. ForETaxi considers not only current charging requests but also possible charging requests of other nearby electric taxis in the near future by inferring their status in real time. More importantly, we implement ForETaxi with multiple types of sensor data from the Chinese Shenzhen city including GPS data, and taxi transaction data from more than 13,000 electric taxis, combined with road network data and charging station data. The data-driven evaluation results show that compared to the state-of-the-art individual-based recommendation methods, our fleet-oriented ForETaxi outperforms them by 16% in the total charging time reduction and 82% in the queuing time reduction.