Waiting Queue Length Research Articles

A multi-channel queue model to optimize service level and staff availability in bank industry

Staff scheduling in service organizations like banks, stores, call centers, and emergency centers is critical due to direct customer interaction and uncertain service demand. This research presents a multi-objective model for scheduling bank staff, focusing on uncertain customer arrival and service rates. The model aims to optimize customer service efficiency and maximize staff satisfaction through three objective functions: minimizing the customer waiting queue length (using an M/M/C system), minimizing the number of assigned employees, and maximizing employee satisfaction by considering preferred working times. By simulating Poisson distribution for client arrival and service times, we predicted the bank's queue system performance and optimized staffing levels using the proposed model. Tested with real data from Agribank in Iran, the results showed an 8% reduction in customer waiting times and a 53% increase in employee satisfaction, demonstrating significant improvements in service efficiency and workplace morale. These percentages highlight the model's ability to effectively balance operational efficiency and employee well-being, facilitated by its transparent work pattern structure. Given the NP-hard nature of the model, we employed a meta-heuristic approach (NSGA-II) and GAMS with the ε-constraint method to solve it. Comparative results indicated that NSGA-II outperformed GAMS in both solution quality and computational time

Two-layer coordinated reinforcement learning for traffic signal control in traffic network

Intersection traffic signal control considering vehicle emissions has become an important topic, however, the decision complexity of traffic signal control increases dramatically in a dynamic traffic environment with multi-intersections. It is a severe challenge to coordinate traffic signals at multi-intersections based on Internet of Things information to improve the traffic condition of the road network. This paper proposes a two-layer coordination algorithm based on multi-agent reinforcement learning—Multi-agent Coordinated Policy Optimization (MACoPO), for solving traffic signal control at multi-intersections. MACoPO consists of local cooperation, which adjusts the weights of individual rewards and neighborhood agents' rewards by using local cooperation factors (LCF), and global coordination, which updates the LCF to maximize global rewards. The state and reward functions are designed in terms of the current state of the signal, waiting queue length, vehicle density and emission concentration in the lane, vehicle delay, and vehicle emissions, thus making full use of the intersection state information. The proposed method is extensively assessed through simulation experiments using artificial and real road networks and the numerical results confirm its effectiveness in complex and dynamic real-time traffic environments with multi-intersections.

An adaptive timing mechanism for urban traffic pre-signal based on hybrid exploration strategy to improve double deep Q network

With the increasing traffic congestion in cities, the priority of public transit has become a consensus for the development and management of urban transportation. The traffic pre-signal mechanism, which gives priority in time and space to buses by optimizing road right-of-way allocation, has gained wide attention and application. In order to broaden the action exploration range of the agent and avoid the pre-signal decision from falling into suboptimal strategy or local optimal strategy. For the exploration strategy of the DDQN algorithm, this paper reduces the probability of directly selecting the local optimal action and increases the probability of selecting non-greedy actions based on the principle that “the action with a larger value function is more likely to be selected.” This paper addresses the problem that the existing urban traffic pre-signal mechanism cannot adaptively adjust the advance time, and proposes a traffic pre-signal adaptive timing mechanism based on a Hybrid Exploration Strategy Double Deep Q Network (HES-DDQN) by combining the epsilon -greedy strategy and Boltzmann strategy. We have used the traffic simulation software VISSIM to conduct simulation experiments on an intersection. The experimental results show that, compared with the method of setting no pre-signal and the formula method of setting pre-signal, the HES-DDQN pre-signal mechanism can significantly reduce the average delay of buses, the waiting queue length, and the number of stops of social vehicles.

A Trade-Off Task-Offloading Scheme in Multi-User Multi-Task Mobile Edge Computing

Mobile edge computing is a novel technique that can reduce computational burden of local terminal devices by tasks offloading, which emerges as a promising paradigm to provide computing capabilities near mobile users. In this paper, the task offloading decision to solve the queuing problem of tasks to be processed in the local terminals was being considered, with an application of Lyapunov theory to ensure the queue stability. Then, a trade-off model was formulated to minimize the delay and energy consumption to achieve a minimum execution cost. Moreover, an improved Dynamic Niche-based Self-organizing Learning Algorithm was presented to accelerate the speed of the search process to gain an optimal task offloading scheme. The simulation results provided supporting evidence that the proposed optimal scheme IDO could achieve a lower average energy consumption than LFO and a lower average execution delay than EFO under the scenarios of the waiting queue length varies within [10, 50], the number of users varies within [4, 20], and the number of tasks varies within [20, 100].

A parallel joint optimized relay selection protocol for wake-up radio enabled WSNs

Conflicts in the data transmission lead to a serious waste of energy, so it is pivotal to design a parallel and less conflict communication protocol for sensing nodes with limited energy. In this paper, a Parallel joint Optimized Relay Selection (PORS) protocol is proposed to reduce collision, delay as well as energy consumption for wake-up radio enabled WSNs. First, the basic Consecutive Packet Transmissions (CPT) method is adopted by PORS protocol. In this method, once a node obtains the channel, its data packets are sent continuously at once, and other nodes go to sleep in this period to reduce channel conflicts and save energy. Then, two thresholds namely maximum waiting queue length and waiting time are set for each node. When the data packets of the node do not meet the threshold, it keeps silent to reduce channel conflicts, and accumulates more packets to increase the number of data packets sent at once, thereby improving energy efficiency. Furthermore, a relay node selection approach is proposed by comprehensively considering factors such as the number of data packets, waiting time, and remaining energy. Those nodes that have many packets, long waiting time, and a large amount of remaining energy are more likely to be selected as relay nodes, so that they can receive more data packets in a short time and meet the threshold requirements as soon as possible. While the other nodes try not to obtain data and keep silent on the channel, thus reducing the collision as well as delay. Finally, the threshold values of nodes in adjacent layers differ by a constant. Thus, the crossing nodes of adjacent layers can transmit data in parallel to reduce the collision and delay Comprehensive analysis and experimental results demonstrate that the average energy consumption is reduced by 11.9%, network lifetime is increased by 26.1%, and network delay is reduced by 35.6% compared with the strategy with the fixed relaying nodes.

An analysis of steady-state distribution in M/M/1 queueing system with balking based on concept of statistical mechanics

The phenomenon of balking has been considered frequently in the steady-state analysis of the M/M/1 queueing system. Balking means the phenomenon that a customer who arrives at a queueing system leaves without joining a queue, since he/she is disgusted with the waiting queue length at the moment of his/her arrival. In the traditional studies for the steady-state analysis of the M/M/1 queueing system with balking, it has been typically assumed that the arrival rates obey an inverse proportional function for the waiting queue length. In this study, based on the concept of the statistical mechanics, we have a challenge to extend the traditional steady-state analysis model for the M/M/1 queueing system with balking. As the result, we have defined an extended analysis model for the M/M/1 queueing system under the consideration of the change in the directivity strength of balking. In addition, the procedure for estimating the strength of balking in this analysis model using the observed data in the M/M/1 queueing system has been also constructed.

Environmental Adaptive Urban Traffic Signal Control Based on Reinforcement Learning Algorithm

Urban traffic signal control is an important part of the construction of intelligent regional traffic. Aiming at the problem of the optimal control strategy in urban traffic signals, this paper proposes environmental adaptive urban traffic signal control based on reinforcement learning algorithms. Through the continuous perception of the traffic environment, the position and speed of the vehicles in different environments are expressed in a matrix, and the parameters are continuously iteratively optimized through the reinforcement learning method to optimize the objective function (the vehicle that can pass the most in a limited time) to achieve the purpose of effective vehicle control. According to the traffic simulation software Vissim, it can be known that the algorithm proposed in this paper performs better in terms of average waiting queue length and global average speed compared with other algorithms, and the deep learning algorithm is significantly better than other algorithms in terms of stability. The average speed of the deep learning algorithm is increased by 9% compared with the baseline, and the average waiting queue length is reduced by 13.4% compared with the baseline. The experiments studied this time are sufficient to prove that the algorithm in this paper can adapt to the dynamically changing and complex urban traffic environment and has great research value.

Study of Performance Measures and Energy Consumption for Cloud Computing Centers Based on Queueing Theory

Cloud computing, as an effective method to provide computing resource and service to users on demand, is attracting more and more attention. This paper establishes a GI/G/m queue to characterize the cloud computing centers with general arrival process and service times. We analyze the main performance measures such as the mean waiting queue length and average waiting time of service requests in the cloud computing system. Besides, the energy consumption model of the cloud computing center is established. The results show that increasing the service rate can reduce the average waiting time, the quantity of running servers needs to be varied with the arrival rates of service requests to meet the satisfaction of users. The total energy consumption is connected with the arrival rate, service rate and the quantity of servers in the cloud computing center, but it has nothing to do with the coefficient of variation squared of the interarrival times and service times.

Analysis of the Effect of the Line-Seru Conversion on the Waiting Time with Batch Arrival

The conveyor assembly line has been widely used in manufacturing industries to produce standard products with low costs. However, due to lack of flexibility, this production method has not been conducive to multivariety and small-batch production. In this situation, seru production formed by converting conveyor assembly lines has been a successful innovation in the Japanese manufacturing industry. Most of the existing literature has studied the benefits of this line-seru conversion from the perspective of the enterprises themselves, but this paper studies the effect of the line-seru conversion on the waiting time from the perspective of the customer. First, the change in the average waiting queue length caused by the line-seru conversion is proposed as an evaluation index. Second, with the consideration of the practical situation of random batch arrivals, the average waiting queue length formulas for the conveyor assembly line and seru production are established based on the assumption that the arrival is a Poisson process. Then, under two scenarios, we investigate the relationship between the average waiting queue length changed by the line-seru conversion and other parameters and find that the conversion can reduce the average waiting queue length in multivariety and small-batch production. Finally, under other potential scenarios, the equations for determining the average waiting queue length resulting from a change to line-seru conversion are derived.

Short-term prediction of vehicle waiting queue at ferry terminal based on machine learning method

Ferry service plays an important role in several cities with waterfront areas. Transportation authorities often need to forecast volumes of vehicular traffic in queues waiting to board ships at ferry terminals to ensure sufficient capacity and establish schedules that meet demand. Several previous studies have developed models for long-term vehicle queue length prediction at ferry terminals using terminal operation data. Few studies, however, have been undertaken for short-term vehicular queue length prediction. In this study, machine learning methods including the artificial neural network (ANN) and support vector machine (SVM) are applied to predict vehicle waiting queue lengths at ferry terminals. Through time series analysis, the existence of a periodic queue-length pattern is established. Hence, methodologies used in this study take into account periodic features of vehicle queue data at terminals for prediction. To further consider the cyclical characteristics of vehicle queue data at ferry terminals, a prediction approach is proposed to decompose vehicle waiting queue length into two components: a periodic part and a dynamic part. A trigonometric regression function is introduced to capture the periodic component, and the dynamic part is modeled by SVM and ANN models. Moreover, an assembly technique for combining SVM and ANN models is proposed to aggregate multiple prediction models and in turn achieve better results than could be attained from a lone predictive method. The prediction results suggest that for multi-step ahead vehicle queue length prediction at ferry terminals, the ensemble model outperforms the separate prediction models and the hybrid models, especially as prediction step size increases. This research has important practical significance to both traffic service management interests and the travelers in cities along waterfront areas.

Real-time signalization for an oversaturated intersection via static state feedback control: A switched system approach

In this paper, the traffic-responsive signalization problem for an oversaturated intersection is addressed. A static state feedback control strategy is presented to relieve the waiting vehicles in lanes. By modeling the intersection as a discrete-time switched system and with the aid of quadratic Lyapunov function method, the green time of each phase in signalization is determined by a set of linear state feedback controllers, and moreover, in order to avoid the risk of overflow of waiting vehicles which could lead to the traffic congestion, the boundary of waiting queue length is estimated and further minimized by the state feedback control. Then, linear matrix inequality (LMI) technique is employed to numerically tackle the design problem through solving a set of LMI optimization problems. Furthermore, the design results are extended to the case with unknown disturbance, and H∞ controller is designed to achieve the disturbance attenuation performance. In the end, a simulation study in comparison with traditional Webster method is presented to illustrate the effectiveness of state feedback control in relieving the oversaturated situation for an intersection.

The Analysis of ATM Queuing Phenomenon on Campus

The article focused on the study of the ATM of Industrial and Commercial Bank in Qinhuangdao. To achieve the purposes of improving customers’ satisfaction and optimizing the allocation of resources, the article calculated some major quantity index including average queue length and waiting queue length, average waiting time and sojourn time as well as utilization rate of the system, analyzed ATM working efficiency of queuing system and gave some suggestions for improvement.