Admission Control Policy Research Articles

Admission, discharge, and transfer control in patient flow logistics: Overview and future research

Patient flow logistics involves managing and coordinating the movement of patients within a healthcare system. It aims to optimize the patients’ flow from their arrival to discharge or transfer, ensuring efficient and effective use of resources while minimizing delays and bottlenecks. Key components of patient flow logistics include capacity planning, resource allocation, appointment scheduling, and notably, admission, discharge, and transfer control. Our focus is on admission, discharge, and transfer control policies, which manage and regulate the flow of individual patients into and out of various facilities delivering care. We review a selection of 37 analytical, empirical, and experimental papers in this area published in leading operations research, operations management, and medical journals. These papers are categorized based on the specific department they target and the type of policies they consider. In particular, we identify: (i) four types of policies for intensive care units, (ii) three types of policies for emergency departments, and (iii) four types of policies for general wards. For each paper, we provide an overview of the research questions, formulations, solution methodologies, and results. This comprehensive review culminates in identifying future research directions for academics in this field.

Novel generalized policy iteration for efficient evolving control of nonlinear systems

In this article, we construct a novel generalized policy iteration framework to address optimal regulation problems for discrete-time nonlinear systems in a more efficient way. Relevant properties are investigated for the framework, including monotonicity and convergence of the iterative value function sequence as well as the admissibility of the iterative control policy. Additionally, an innovative approach is developed to seek an initial admissible control policy for the framework with an adjustable searching speed. Based on these, an evolving control algorithm is presented with stability guarantee. This algorithm employs iterative control policies for system control during the computation of the optimal control policy, as opposed to waiting for the generation of the optimal control policy before implementing control. Eventually, two simulation experiments are conducted with real-world physical backgrounds, in order to illustrate the performance of the proposed strategy.

Admission Control in Multi-server Systems Under Binary Reward Structure

We study a multi-server queueing system where a customer is satisfied (and generates a unit revenue) if their queueing time is at most a given constant. If the queueing time of the admitted customer exceeds this constant, the customer gets served, but is unsatisfied and generates no revenue. Such queueing systems arise in the context of modeling service systems where excessive delays are of concern. A key challenge is how to design an admission control policy to maximize the number of satisfied customers per unit time in the long run, assuming that we can observe the number of customers in the system at any time. We call this the binary reward structure system and show that a threshold-type admission policy is optimal. The optimal threshold policy has to be computed numerically. Hence we propose a square-root admission policy to approximate the optimal admission control policy, and compare the performance of these two policies. We derive an analytical upper bound on the performance of optimal admission control policy by deriving an optimal admission policy assuming we have full information over the queueing time of the admitted customers. This is equivalent to a queueing system where customers abandon the queue (i.e., leave without service) if their queueing time exceeds the given constant. We demonstrate that the optimal policy that includes customer abandonment, or alternatively, the optimal policy under full information, the optimal threshold policy, and the square-root admission policy, all exhibit identical performance in the asymptotic regions of the parameter space. Our numerical results indicate that the worst optimality gap of the square-root admission policy is within 3.9% of the optimal revenue, and implementing the square-root admission policy in the observable queueing system leads to a revenue loss that is at most 5.6% of the maximum possible revenue rate in the full information system. We also compare the binary reward structure with the more common linear reward structure where the system incurs holding cost per unit queueing time per customer. In addition, we also show that the analysis based on queueing time is applicable to the system time as well.

Packets’ Congestion Management with Fuzzy Admission Control Policy for Differentiated Service Networks

This study presents a priority-based admission control system for ensuring stability in a differentiated service network using a fuzzified admission policy. Packets of varying sizes of data were transmitted from N sources through a traffic conditioner which categorizes the packets into two independent sources, consequently classifying them into “high” and “low” priorities. While class A packets are not denied admission into the buffer, this is not the case with packets of class B. Arrivals from both sources follow a Poisson distribution process of\(\alpha\)(k) = (\(\lambda\) /k!)e -\(\lambda\) . It is assumed that r > h / \(\mu\) in order to avoid a situation in which a class B arrival is denied admission while the system is empty. The arrival rates \(\lambda\) 1 \(\in\) [02\(\mu\) ) and \(\lambda\)2 \(\in\) [02 \(\alpha\)) serve as fuzzy inputs with four linguistic values while the output is a decision, d. Simulation started with an initial state, zero and system performance for the first 300time units was monitored. Results indicate that the admission controller admits arriving class B packets provided that the value of y ≤ 4 while it denied admission to arriving class B packets when y > 4, thus giving a threshold policy of y=4 . Arrivals denied admission are dropped and transmitted to the “tree manager” via the bottleneck router. These packets are arranged as nodes in an AVL tree structure which adopts tree properties to manage and transmit nodes to the buffer based on node rotations.

Admissible Control Laws for Constrained Linear Power Flow: The General Case

Linearized power flow with line flow and voltage constraints can be modeled as a system of linear inequalities depending on the power injections. When some injections are controlled by the grid operator while others are determined exogenously, robust control aims at determining grid operator's actions under imperfect system observability such that the grid state is feasible for all possible realizations of the exogenous actions. It was shown how to design and analyze such admissible control policies efficiently for the subclass of affine control laws, but this paper shows that there are important cases that require more general control policies. We show that, for the constrained linear power flow setting, general admissible control policies can always be chosen as piecewise affine (PWA) mappings. The PWA mapping can be explicitly characterized offline or control actions can be computed online by solving an optimization problem given a current observation. For the latter setup, we provide an algorithm that verifies offline that the online control scheme is admissible. This verification step is a crucial precondition to apply the online control scheme in real, safety-critical power grids. The proposed framework is demonstrated with applications to voltage regulation in active distribution grids subject to uncertain prosumers and low observability.

Optimal management strategies of renewable energy systems with hyperexponential service provisioning: an economic investigation

The current research proposes optimal management strategies for queueing modeling-based renewable energy systems with hyper-exponentially distributed maintenance/repair under the assumption of an admission control policy. Using the concept and steps of the matrix-analytical method, the steady-state probability distribution associated with energy systems is explicitly presented. A relatively straightforward computation that can help with modeling wind energy generation, investigating wind farm performance, optimizing energy based on system storage, reliability inspection, service maintenance planning, and numerous other purposes can be employed to mathematically derive several system performance indicators. The investigation findings are validated via quantitative outcomes, illustrative possesses, and a step-by-step recursive methodology for efficient management of the renewable energy system. Additionally, considering multiple governing parameter values, the nature-inspired optimization technique, Cuckoo Search (CS), is employed to demonstrate the optimum anticipated cost of renewable energy system. A comparison with other metaheuristics and semi-classical approaches is also presented to establish the best convergence results. In order to help system designers, policymakers, engineers, and researchers, several numerical examples are also provided to construct more practical strategies based on the production of energy, storage, and system management. The economic, parametric, and performance investigation findings are highlighted, and the opportunities and recommendations for further research are provided. In a nutshell, the outcomes of the present analysis can be adopted to formulate the most effective economic strategies and regulate decision-making processes in the energy sectors.

Distributed Resource Allocation and Flow Control Algorithms for mmWave IAB Networks

This paper presents a new distributed slot reservation frame-work for joint resource allocation and flow control in mmWave IAB networks. We derive the Dynamic Slot Reservation (DSR) algorithm from a novel approach to solve a minimum clearing time linear program in a completely distributed manner. The algorithm to solve this problem, the Static Slot Reservation (SSR) algorithm, is also a contribution of the paper. We compare the delay performance of the DSR algorithm with a well known optimal, centralized algorithm, the joint-MWM algorithm, for a realistic IAB network scenario of multi-hop flows. We show that flows that traverse several links have significantly lower delays under DSR than under the joint-MWM algorithm. This paper also provides an instantaneous rate control policy for IAB networks which changes flow rates based on the number of flows at each node in the network. The flow rates under this policy are the same as the steady-state flow rates achieved by the DSR algorithm. We prove that the proposed flow control policy provides stability for all flow arrival rate vectors that are achievable by any flow control policy. This paper provides distributed admission control policies to provide rate and/or latency guarantees to flows under dynamic scenarios with stochastic flow arrivals and changing access link rates.

ADP-Based Optimal Control of Linear Singularly Perturbed Systems With Uncertain Dynamics: A Two-Stage Value Iteration Method

We study the problem of adaptive dynamic programming (ADP) based on optimal control of linear singularly perturbed systems (SPSs) subject to completely unknown dynamics. Previous works on ADP-based optimal control of SPSs require that the fast dynamics of the system are either known a priori or unknown but asymptotically stable, and such results are limited to standard cases, i.e., the internal dynamics of their fast subsystems are nonsingular. In this brief, these conditions can be removed through a sequential procedure to design feedback gains of both fast and slow states in the framework of ADP. In this procedure, two optimal control problems are formulated for the prefeedback fast subsystem and the modified slow subsystem. Then, a data-driven two-stage value iteration algorithm is imposed to learn the optimal controller without using any system dynamics. Fundamentally different from existing works, an initial admissible control policy is no longer needed during learning. Finally, the effectiveness of the learning algorithm is proved by a simulation example.

Temperature field optimization for laser powder bed fusion as a traveling salesperson problem with history

AbstractLaser Powder Bed Fusion (LPBF) is a form of metal additive manufacturing in which a laser traces a path on a metal powder bed to heat powder particles and progressively melt and/or fuse them together to form a part. Outcomes of LPBF are functions of the metal's temperature history and are strongly affected by the path traced by the laser. Using temperature field optimization for LPBF as a motivating application, we focus on a class of combinatorial problems where admissible control policies are a permutation of a set of control actions. We abstract this class of problems as an optimal control problem with the objective of identifying an admissible control policy that minimizes a cost function of a dynamical system's state. While, in principle, the effect of the control actions on the cost function can last an infinitely long time, we will consider systems in which the effects of these control actions can be considered to last short (finite) periods of time. In this paper, we formalize this class of combinatorial problems as a Traveling Salesperson Problem with History and prove its equivalence to an Equality Generalized Traveling Salesperson Problem (E‐GTSP), enabling the use of well‐developed E‐GTSP solvers. We demonstrate this equivalence by computing the solutions obtained using an E‐GTSP solver for an LPBF‐inspired application.

Optimal stabilizing rates of switched linear control systems under arbitrary known switchings

The problem of stabilizing discrete-time switched linear control systems using continuous control input under arbitrary mode switchings is studied. It is assumed that at each time instance the switching mode can be arbitrarily chosen but is always known by the controller designing the continuous control input; thus the continuous controller is of the general form of an ensemble of mode-dependent state feedback controllers. Under this setting, the fastest (worst-case) stabilizing rate is proposed as a quantitative metric of the systems’ stabilizability. Conditions are derived on when this stabilizing rate can be exactly achieved by an admissible control policy and a counter example is given to show that the stabilizing rate may not always be attained by a mode-dependent linear state feedback control policy. Bounds on the stabilizing rate are derived using (semi)norms. When such bounds are tight, the corresponding extremal norms are characterized geometrically. Numerical algorithms based on ellipsoid and polytope norms are developed for computing bounds of the stabilizing rate and illustrated through examples.

Intelligent Hierarchical Admission Control for Low-Earth Orbit Satellites Based on Deep Reinforcement Learning.

Low-Earth orbit (LEO) satellites have limited on-board resources, user terminals are unevenly distributed in the constantly changing coverage area, and the service requirements vary significantly. It is urgent to optimize resource allocation under the constraint of limited satellite spectrum resources and ensure the fairness of service admission control. Therefore, we propose an intelligent hierarchical admission control (IHAC) strategy based on deep reinforcement learning (DRL). This strategy combines the deep deterministic policy gradient (DDPG) and the deep Q network (DQN) intelligent algorithm to construct upper and lower hierarchical resource allocation and admission control frameworks. The upper controller considers the state features of each ground zone and satellite resources from a global perspective, and determines the beam resource allocation ratio of each ground zone. The lower controller formulates the admission control policy based on the decision of the upper controller and the detailed information of the users' services. At the same time, a designed reward and punishment mechanism is used to optimize the decisions of the upper and lower controllers. The fairness of users' services admissions in each ground zone is achieved as far as possible while ensuring the reasonable allocation of beam resources among zones. Finally, online decision-making and offline learning were combined, so that the controller could make full use of a large number of historical data to learn and generate intelligent strategies with stronger adaptive ability while interacting with the network environment in real time. A large number of simulation results show that IHAC has better performance in terms of a successful service admission rate, service drop rate, and fair resource allocation. Among them, the number of accepted services increased by 20.36% on average, the packet loss rate decreased by 17.56% on average, and the resource fairness increased by 17.16% on average.

Adaptive optimal control of continuous-time nonlinear affine systems via hybrid iteration

In this paper, a novel successive approximation framework, named hybrid iteration (HI), is proposed to fill up the performance gap between two well-known dynamic programming algorithms, namely policy iteration (PI) and value iteration (VI). Using HI, an approximated optimal control policy can be learned without prior knowledge of an initial admissible control policy required by PI. Additionally, the HI algorithm converges to the optimal solution much faster than VI, and thus requires tremendously less number of learning iterations and CPU-time, compared to VI. Initially, we develop a model-based HI algorithm, and then extend it to a data-driven HI algorithm which learns the optimal control policy without any information of the physics of the system. Simulation results demonstrate the efficacy of the proposed HI algorithm.

Model-free algorithm for consensus of discrete-time multi-agent systems using reinforcement learning method

In this work, we investigate consensus issues of discrete-time (DT) multi-agent systems (MASs) with completely unknown dynamic by using reinforcement learning (RL) technique. Different from policy iteration (PI) based algorithms that require admissible initial control policies, this work proposes a value iteration (VI) based model-free algorithm for consensus of DTMASs with optimal performance and no requirement of admissible initial control policy. Firstly, in order to utilize RL method, the consensus problem is modeled as an optimal control problem of tracking error system for each agent. Then, we introduce a VI algorithm for consensus of DTMASs and give a novel convergence analysis for this algorithm, which does not require admissible initial control input. To implement the proposed VI algorithm to achieve consensus of DTMASs without information of dynamics, we construct actor-critic networks to online estimate the value functions and optimal control inputs in real time. At last, we give some simulation results to show the validity of the proposed algorithm.

Admission control policy of a two heterogeneous server finite capacity retrial queueing system with maintenance activity

Admission control policy of a two heterogeneous server finite capacity retrial queueing system with maintenance activity

Dual-Band Aerial Networks for Priority-Based Traffic

Unmanned aerial vehicle networks suffer from lackluster performance due to line-of-sight issues as well as resource scarcity. Network slicing, multi-band transmission, and numerology show great potential in mitigating such limitations. In this work, we propose the use of the sub-6 GHz and mmWave bands, the latter of which requires careful consideration of line-of-sight, with numerology for aerial network slicing to provision time-critical services and broadband access. Accordingly, we formulate a user admission control policy to regulate band access after which we formulate a joint resource block and power allocation problem, a mixed-integer non-linear programming problem, to minimize the quality-of-service gap of the throughput-dependent broadband users as a best-effort service and meet the time-critical service requirements. We propose a low-complexity algorithm, PREDICT, to tackle the formulated problem and present extensive simulation results to validate the advantages of our scheme.

Admission control policy and key agreement based on anonymous identity in cloud computing

Cloud computing has completely revolutionized the concept of computing by providing users with always-accessible resources. In terms of computational, storage, bandwidth, and transmission costs, cloud technology offers its users an entirely new set of advantages and cost savings. Cross-cloud data migration, required whenever a user switches providers, is one of the most common issues the users encounter. Due to smartphones’ limited local storage and computational power, it is often difficult for users to back up all data from the original cloud servers to their mobile phones to upload and download the data to the new cloud provider. Additionally, the user must remember numerous tokens and passwords for different applications. In many instances, the anonymity of users who access any or all services provided by this architecture must be ensured. Outsourcing IT resources carries risks, particularly regarding security and privacy, because cloud service providers manage and control all data and resources stored in the cloud. However, cloud users would prefer that cloud service providers not know the services they employ or the frequency of their use. Consequently, developing privacy protections takes a lot of work. We devised a system of binding agreements and anonymous identities to address this problem. Based on a binding contract and admission control policy (ACP), the proposed model facilitates cross-cloud data migration by fostering cloud provider trust. Finally, Multi-Agent Reinforcement Learning Algorithm (MARL) is applied to identify and classify anonymity in the cloud by conducting various pre-processing techniques, feature selection, and dimensionality reduction.

Correction to: Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-Dense Cellular Network

Correction to: Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-Dense Cellular Network

Fuzzy modeling and cost optimization for machine repair problem with retrial under admission control F-policy and feedback

In the present paper, the machine repair problem (MRP) with real-life scenarios is investigated, which plays a significant role in the manufacturing process, industries, production lines, etc. An M∖M∖1∖K machine repair problem with retrial attempts under admission control policy F-policy and machine’s feedback is considered in which a single repairman is available to repair the failed machines. The recursive technique is applied to solve the mathematical model and establish the probability distributions for different states, which are further used to develop the performance indices. The parametric non-linear programming (P-NLP) technique is employed to develop the MRP with F-policy in a fuzzy environment using the trapezoidal and sigmoidal fuzzy numbers. Zadeh’s extension principle and parametric non-linear programming technique help us to find the α-cuts of the developed performance predictors of MRP. We frame and minimize the total cost of the system. The harmony search method (HSM) and quasi-Newton method are implemented to achieve the total minimum cost of the system corresponding to the optimal repair rate, which helps the system managers in the formulation of business policies in industries, including the automobile sector. A numerical example is also given, which produces several tables and graphs that show the validity of the model.

Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems

This article studies the adaptive optimal stationary control of continuous-time linear stochastic systems with both additive and multiplicative noises, using reinforcement learning techniques. Based on policy iteration, a novel off-policy reinforcement learning algorithm, named optimistic least-squares-based policy iteration, is proposed, which is able to find iteratively near-optimal policies of the adaptive optimal stationary control problem directly from input/state data without explicitly identifying any system matrices, starting from an initial admissible control policy. The solutions given by the proposed optimistic least-squares-based policy iteration are proved to converge to a small neighborhood of the optimal solution with probability one, under mild conditions. The application of the proposed algorithm to a triple inverted pendulum example validates its feasibility and effectiveness.

Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-dense Cellular Network

Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-dense Cellular Network