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An M/M/1 constant retrial queue with balking customers and set-up time is considered. Once the system becomes empty, the server will be turned down to reduce operating costs, and it will be activated only when there is a customers arrives. In this paper, the almost unobservable case is studied, in which the information of the queue length is unavailable, whereas the state of the server can be obtained. Firstly, the steady state solutions are derived and the individual equilibrium strategies are analyzed. In addition, social optimization problems, including cost analysis and social welfare maximization are investigated by using the PSO algorithm. Finally, by appropriate numerical examples, the sensitivity of some main system parameters is shown.

This study examines a matching queue problem (double-sided matching queue) where customers are loss-averse with respect to their waiting times. We first set the reference point and construct the lo...

In some queueing systems, customers are frequently asked for giving a service quality feedback for their service at their service completion instants. Based on this phenomenon, in this paper, we model this type of queueing systems as clearing queues with service quality feedback and system maintenance. Once the system receives an unsatisfied (negative) feedback from customers (i.e., a customer is unsatisfied with the service), the system undergoes an adjustment procedure, and at the same time, all the present customers are forced to leave the system. By considering the waiting cost and reward, we discuss the joining behavior of customers and, respectively, derive the corresponding equilibrium joining strategies and social optimal strategies under different levels of information (the observable and the unobservable cases). Finally, some numerical examples are provided to show the effect of several system parameters on the equilibrium and optimal balking strategies.

This paper investigates an M/M/1 constant retrial queue with reserved time and vacations. A new arriving customer will take up the server and accept service immediately if the server is idle. Otherwise, if the server is busy or on vacation, customers have to join a retrial orbit and wait for retry. Once a service is completed, the server will reserve a random time to seek a customer from the orbit at a constant retrial rate. If there is no arrivals (from the orbit or outside) during the idle period, to save energy, the server will take a vacation. This paper studies the fully unobservable case. First, the steady-state condition of the system is analyzed by using the Foster’s criterion, and the customers’ expected waiting time is obtained based on the generating function technique. And then, by introducing an appropriate revenue structure, the equilibrium strategies of customers and the socially optimal strategy are all derived. Furthermore, a comparison between them is made and the effect of some main system parameters is studied.

The existing tollbooth systems with multi-type vehicles in transportation literature typically assume the servers are either dedicated for each vehicle type or generic for all vehicle types. Conversely, in this paper, we focus on studying two similar systems both with skill-based servers, i.e., each tollbooth can handle a given subset of vehicle types. Meanwhile, we assume that vehicles queue together in a single lane, thus they get served by global First-Come-First-Served discipline. The slight difference between these two systems is the number of exits: one with a single exit (model 1) and the other with two exits (model 2). Nevertheless, it creates large difference in the resolving process. By spectral expansion technique, we obtain a closed-form solution for the system’s steady-state probabilities in the former model and the explicit solution without closed-form expressions in the latter model. By numerical results, we demonstrate how the parameters selection influences system’s performance, along with some intriguing managerial insights. We also use the system of the same structure but with dedicated servers as the baseline for comparisons to show the benefits of skill-based servers.

In this paper we consider a batch matching system with impatient servers and boundedly rational customers, which is essentially a double-ended batch service system. Each server is responsible for a batch of customers and serves them instantaneously. Especially, we assume that the servers have the characteristic of ”impatience”, which will lead to incomplete batch processing of each service. To derive the stationary distribution of system states, we combine two methods (called probability generating function method and G-matrix method), and some main performance measures are captured. In addition, we assume the customers are boundedly rational, that is, they are unable to estimate their sojourn times and utilities accurately. To deal, a logit model is employed to model this characteristic of the customers, and by which, we obtain the customers’ equilibrium joining fraction which is compared with the equilibrium joining probability of fully rational customers afterwards. Depending on whether the service is visible or invisible, we conduct some numerical analyses to show main parameters’ impact on equilibrium fraction, along with some intuitive explanations. Finally, to make the system viable economically, a function of service provider’s utility is developed and several numerical plots are presented to study the impact of several main parameters on the utility. Through the PSO (Particle Swarm Optimization) algorithm, we numerically obtain the maximum utility point within a certain range of multiple parameters.

In this article, customers’ strategic behavior and social optimation in a constant retrial queue with setup time and the N-policy are investigated. Customers who find the server isn’t idle either l...

In this paper, strategic behavior of passengers in a batch transfer queuing system with gated policy under complete information is investigated. Two solutions are used to solve the stationary distribution of the system. Particularly, suppose that arriving passengers have the opportunity to observe all information of the system upon arrival, we derive equilibrium joining strategy of passengers under this information when passengers fall into the dilemma of joining or balking. We prove that the equilibrium joining strategy is a threshold type, but when some parameters change, it may be changed. Under the equilibrium strategy of threshold type of the observable case, we give the parameterized expected total net social benefit function. Finally, a number of numerical experiments on equilibrium social benefit with respect to several parameters are presented, which are used to explore the theoretical results of the system and compare observable case and unobservable case of the system.

In order to meet the requirements of high reliability and economy of space launch vehicle control system, a heterogeneous redundancy method of flight control computer based on mutual exclusion and spin mechanism is proposed in this paper. This method avoids common cause failure mode and improves hardware resource utilization efficiency by resolving redundancy. The reliability and economy of the launch vehicle control system can be improved without increasing the cost of volume and weight.

The chapter introduces a Cyber-Physical System Engineering Oriented Intelligent High Performance Simulation Cloud (CPSEO-IHPSC) developed by the author’s team. CPSEO-IHPSC is a kind of intelligent high performance simulation cloud system with the characteristics of digitization, networking, cloud, and intelligence, supporting user to access services of intelligent high performance simulation resource, capability, and CPS product on demand, anytime and anywhere, based on the ubiquitous network. It could promote the CPS engineering activities to be more efficient, optimal, economic, flexible, and safer. Firstly, the CPSEO-IHPSC’s connotation, architecture, and bodies of knowledge are proposed. Then, the author team’s preliminary research results are introduced including the CPSEO-IHPSC’s preliminary prototype developed, some key technologies solved, and application examples implemented in intelligent manufacturing system engineering and smart city system engineering. Finally, the proposal for developing CPSEO-IHPSC in the “new Internet + cloud computing + big data + new AI +” era is discussed.

In this paper, we study the equilibrium strategies of heterogeneous delay sensitive individuals in matching systems. Take customers as the research object. The priorities hinge on the magnitude of customers’ admission fee. Before entering the system where there are no servers, customers will make a two-stage strategy. First, customers should determine an optimal additional fee to pursue the optimal utilities, i.e., the so-called additional payment strategy. Then, they decide whether to join or not. We primarily give some necessary conditions for the equilibrium. As regards the heterogeneity of customers in delay sensitivity, both of the non-atomic continuous and atomic discrete types of customers are discussed, respectively. Applying the sequential approach, we capture the unique equilibrium for both cases. Specially, for the non-atomic continuous type of customers, the incoming ones adopt the pure additional payment strategy at equilibrium. While for the atomic discrete type of customers, the equilibrium additional payment strategies for incoming ones become mixed-type. It is found that the equilibrium additional fee and flexible cost for each incoming customer are both decreasing in fundamental payment. Finally, we use the particle swarm algorithm to consider the optimal social revenue when the incoming ones from both sides are strategic.

In recent years, data-driven approaches for remaining useful life (RUL) prognostics have aroused widespread concern. Bearings act as the fundamental component of machinery and their conditioning status is closely associated with the normal operation of equipment. Hence, it is crucial to accurately predict the remaining useful life of bearings. This paper explores the degradation process of bearings and proposes an enhanced encoder–decoder framework. The framework attempts to construct a decoder with the ability to look back and selectively mine underlying information in the encoder. Additionally, trigonometric functions and cumulative operation are employed to enhance the quality of health indicators. To verify the effectiveness of the proposed method, vibration data from PRONOSTIA platform are utilized for RUL prognostics. Compared with several state-of-the-art methods, the experimental results demonstrate the superiority and feasibility of the proposed method.

Manifold learning is a promising intelligent data analysis method, and the manifold learning preserves the local embedding features of the data in manifold mapping space. Manifold learning has its limitations on extracting the nonlinear features of the data in many applications. For example, hyperspectral image classification needs to seek the nonlinear local relationships between spectral curves. For that, researchers applied the kernel trick to manifold learning in the previous works. The kernel-based manifold learning was developed, but still endures the problem that the inappropriate kernel model reduces the system performance. In order to solve the problem of kernel model selection, we propose a manifold framework of multiple-kernel learning for the application of hyperspectral image classification. In this framework, the quasiconformal mapping-based multiple-kernel model is optimized based on the optimization objective equation, which maximizes the class discriminant ability of data. Accordingly, the discriminative structure of data distribution is achieved for classification with the quasiconformal mapping-based multiple-kernel model.

Kernel machine is a feasible and effective nonlinear feature extraction method on data analysis, for example, hyperspectral sensing data. Kernel trick improves largely the performance of learning system including recognition, clustering, prediction through the nonlinear kernel mapping from the input data space to output data space. The performance of kernel-based system is largely influenced by the function and parameter of kernel. Optimizing only the parameters is not effective to promote the kernel-based learning system, because the data distribution is not changed only changing the kernel parameter. Moreover, no any universal single kernel is very adaptive to all applications. We present a framework of quasiconformal mapping-based kernel learning machine under single kernel and multiple kernels for hyperspectral image data classification. The performance of learning system is improved largely owing to the two facts: quasiconformal kernel structure changes the data structure in the kernel empirical space; quasiconformal multikernels characterize precisely the data for improving performance on solving complex visual learning tasks. The learning framework is applied to the hyperspectral image classification, and some experiments are implemented on two hyperspectral image databases.

Considering the power consumption and delay performance, we propose a clearing queueing system with N-policy and stochastic restarting scheme. At the service completion epoch, the server switches to the dormant state to save energy if there are no customers waiting for services, otherwise initiates a new clearing service. After a dormant period, the server will be activated if there are at least N waiting customers, and be activated stochastically with less than N waiting customers. We derive the stationary distribution of system states with the closed form and an effective algorithm is designed to obtain the distribution of the sojourn time. In addition, a reward-cost structure is introduced in the queueing system. We analyze customers’ equilibrium strategic behaviours based on the optimistic value and provide a comparison between the criteria of using average and optimistic value by numerical examples. Finally, we discuss the social optimization problems and present some numerical experiences to illustrate impacts of system parameters on the socially optimal strategies and social optimal welfare.