Admission Scheduling Research Articles

Incremental Analysis of Cyclo-Static Synchronous Dataflow Graphs

In this article, we present a mathematical characterisation of admissible schedules of cyclo-static dataflow ( csdf ) graphs. We demonstrate how algebra ic manipulation of this characterization is related to unfolding csdf actors and how this manipulation allows csdf graphs to be transformed into mrsdf graphs that are equivalent , in the sense that they admit the same set of schedules. The presented transformation allows the rich set of existing analysis techniques for mrsdf graphs to be applied to csdf graphs and generalizes the well-known transformations from csdf and mrsdf into hsdf . Moreover, it gives rise to an incremental approach to the analysis of csdf graphs, where approximate analyses are combined with exact transformations. We show the applicability of this incremental approach by demonstrating its effectiveness on the problem of optimizing buffer sizes under a throughput constraint.

A case study: Applying quality improvement methods to reduce pre-operative length of stay in a resource-constrained setting in Rwanda

Objective: While several studies have focused on improving the quality of surgery, less attention has been paid to reducing pre-operative delays in care. We undertook a hospital quality improvement (QI) effort to reduce pre-operative delays in a teaching hospital in Rwanda. Without a coordinated admission schedule, many surgical patients arriving at the hospital for admissions were turned away because of unavailable beds. For those admitted for surgery, the pre-operative waits were long.Methods: A pre- and post-intervention study was conducted to examine the impact of a QI effort on two metrics: 1) pre-operative length-of-stay (LOS) for elective surgical patients, and 2) the number of elective surgical patients who were turned away on the scheduled admission date. Intervention: A multi-disciplinary work group utilized a Strategic Problem Solving Approach and implemented a centralized patient wait list and new schedule process utilizing the existing resources available at the hospital.Results: The percentage of elective surgical patients with a pre-operative LOS of more than two days was significantly lower in the post-intervention compared with the pre-intervention period (80% versus 26.8%, p-value < .001). The percentage of scheduled patients who were turned away due unavailable inpatient beds significantly decreased from 63.4% to 5.3%, p-value < .001.Conclusions: By following a methodical strategic problem solving approach, the pre-operative LOS was reduced, elective surgical patients turned away due to unavailable beds was decreased at very low financial cost.

Optimal correction of execution intervals for multiprocessor scheduling with additional resource

A problem of admissible multiprocessor preemptive scheduling is considered when execution intervals are specified, processors have arbitrary performance, and the execution time of tasks linearly depends on the amount of the additional resource allocated to their execution. If there is no admissible schedule for a given amount of the additional resource, we consider the problem of optimal correction of execution intervals. The solution is based on reducing the original problem to the problem of minimum cost flow.

Computation planning in multiprocessor real time automated control systems with an additional resource

We consider the problem of composing an admissible schedule with interruptions for a multiprocessor real time ACS in case when directive intervals are given, processors may have arbitrary performances, and the durations of jobs depend linearly on the amount of additional resource assigned to them. We develop algorithms based on a reduction of the original problem to a minimal cost flow problem and to a linear programming problem.

Optimal periodic gain scheduling for bipedal walking with hybrid dynamics

SUMMARYWe present an optimal gain scheduling control design for bipedal walking with minimum tracking error. We obtained a linear approximation by linearizing the nonlinear hybrid dynamic model about a nominal periodic trajectory. This linearization allows us to identify the linear model as a linear periodic system. An optimal feedback was designed using Bellman's dynamic programming. The linear periodic system allows us to determine a linear quadratic regulator (LQR) for a single period and to set the Hamilton-Jacobi-Bellman (HJB) function in a linear quadratic form. In this way, the dynamic programming yielded an admissible continuous gain scheduling that was designed with regard to the hybrid dynamics of the system. We tuned the optimization parameters such that the tracking error and the average energy consumption are minimized. Due to linearization, we were able to examine the stability of the approximated periodic system achieved by the periodic gain according to Floquet's theory, by calculating the monodromy matrix of the closed-loop hybrid system. In addition to determining stability, the eigenvalues of this approximated monodromy matrix allowed us to evaluate the settling time of the system. This approach presents a direct method for optimal solution of locomotion control according to a given reference trajectory.

Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays

We revisit and extend the patient admission scheduling problem, in order to make it suitable for practical applications. The main novelty is that we consider constraints on the utilisation of opera...

Review of WiMAX Scheduling Algorithms and Their Classification

Providing quality of service (QoS) in wireless communication networks has become an important consideration for supporting variety of applications. IEEE 802.16 based WiMAX is the most promising technology for broadband wireless access with best QoS features for tripe play (voice, video and data) service users. Unlike wired networks, QoS support is difficult in wireless networks due to variable and unpredictable nature of wireless channels. In transmission of voice and video main issue involves allocation of available resources among the users to meet QoS criteria such as delay, jitter and throughput requirements to maximize goodput, to minimize power consumption while keeping feasible algorithm flexibility and ensuring system scalability. WiMAX assures guaranteed QoS by including several mechanisms at the MAC layer such as admission control and scheduling. Packet scheduling is a process of resolving contention for bandwidth which determines allocation of bandwidth among users and their transmission order. Various approaches for classification of scheduling algorithms in WiMAX have appeared in literature as homogeneous, hybrid and opportunistic scheduling algorithms. The paper consolidates the parameters and performance metrics that need to be considered in developing a scheduler. The paper surveys recently proposed scheduling algorithms, their shortcomings, assumptions, suitability and improvement issues associated with these uplink scheduling algorithms.

Design and Optimization Methods for Elective Hospital Admissions

Hospitals typically lack effective enterprise level strategic planning of bed and care resources, contributing to bed census levels that are statistically “out of control.” This system dysfunction manifests itself in bed block, surgical cancelation, ambulance diversions, and operational chaos. This is the classic hospital admission scheduling and control (HASC) problem, which has been addressed in its entirety only through inexact simulation-based search heuristics. This paper develops new analytical models of controlled hospital census that can, for the first time, be incorporated into a mixed-integer programming model to optimally solve the strategic planning/scheduling portion of the HASC. Our new solution method coordinates elective admissions with other hospital subsystems to reduce system congestion. We formulate a new Poisson-arrival-location model (PALM) based on an innovative stochastic location process that we developed and call the patient temporal resource needs model. We further extend the PALM approach to the class of deterministic controlled-arrival-location models (d-CALM) and develop linearizing approximations to stochastic blocking metrics. This work provides the theoretical foundations for an efficient scheduled admissions planning system as well as a practical decision support methodology to stabilize hospital census.

Design and Optimization Methods for Elective Hospital Admissions

Hospitals typically lack effective enterprise level strategic planning of bed and care resources, contributing to bed census levels that are statistically out of control.'' This system dysfunction manifests itself in bed block, surgical cancellation, ambulance diversions and operational chaos. This is the classic Hospital Admission Scheduling and Control (HASC) problem, which has been addressed in its entirety only through inexact simulation-based search heuristics. This paper develops new analytical models of controlled hospital census that can, for the first time, be incorporated into a Mixed Integer Programming model to optimally solve the strategic planning/scheduling portion of the HASC. Our new solution method coordinates elective admissions with other hospital subsystems to reduce system congestion. We formulate a new Poisson-arrival-location model (PALM) based on an innovative stochastic location process that we developed and call the Patient Temporal Resource Needs (PATTERN) model. We further extend the PALM approach to the class of deterministic controlled-arrival-location models (d-CALM) and develop linearizing approximations to stochastic blocking metrics. This work provides the theoretical foundations for an efficient scheduled admissions planning system as well as a practical decision support methodology to stabilize hospital census.

Throughput-Efficient Scheduling and Interference Alignment for MIMO Wireless Systems

Multiple-input multiple-output (MIMO) wireless communication systems can achieve higher throughput through interference alignment. For a small number of users, determining the maximum possible degrees of freedom as well as the feasibility of interference alignment in MIMO systems is well studied. However, the issues of scheduling in systems employing interference alignment and serving a large number of users have received little attention so far. In this paper, we study the problem of joint scheduling, interference alignment, and packet admission control in MIMO wireless systems with the goal of maximizing system throughput subject to stability constraints. We formulate a stochastic network optimization problem and propose a scheduling and interference alignment (SIA) algorithm. In each time slot, SIA schedules some users among many competing ones to transmit data, and determines encoding and decoding matrices for the selected users. Packet admission control is performed in each time slot. In addition, we propose a heuristic semi-distributed algorithm (SDSIA), which has a lower computational complexity than the SIA algorithm. Via simulation, we evaluate the performance of SIA and SDSIA for different algorithm parameters and different numbers of users. We also compare the performance of SDSIA with other approaches which do not simultaneously exploit interference alignment and scheduling and find that the combination of these two techniques increases the achievable data rate dramatically.

Some algorithms of solving minimax multiprocessor scheduling problem

A problem of constructing schedules of minimal length without interrupts and switches is considered for a multiprocessor system, in which the job execution time depends on the processor assigned to the job. To solve this problem, the branch and bound method is developed. The method is based on efficient algorithms for calculating lower and upper bounds of minimal length of the schedule. For the particular case when processors are identical, their number is fixed and the directive deadline is given, a pseudo-polynomial algorithm is proposed for constructing the admissible schedule. The number of processors required for efficient parallelizing of the algorithm is found.

Some algorithms for analysis and synthesis of real-time multiprocessor computing systems

The problem of preemptive scheduling in a real-time multiprocessor computing system with release time/deadline intervals is investigated. Approximate algorithms based on the generalization of a single-processor algorithm of relative priority are developed and compared to the exact maximum flow algorithm. An algorithm has been developed for the case where requests for the tasks occur periodically with given periods. An algorithm for determining the values of the processor performance for which there exists an admissible schedule for a given assembly of tasks with release time/deadline intervals has been developed.

A column generation approach for solving the patient admission scheduling problem

This paper addresses the Patient Admission Scheduling (PAS) problem. The PAS problem entails assigning elective patients to beds, while satisfying a number of hard constraints and as many soft constraints as is possible, and arises at all planning levels for hospital management. There exist a few, different variants of this problem. In this paper we consider one such variant and propose an optimization-based heuristic building on branch-and-bound, column generation, and dynamic constraint aggregation to solve it. We achieve tighter lower bounds than previously reported in the literature and, in addition, we are able to produce new best known solutions for five out of twelve instances from a publicly available repository.

MACHINE LEARNING APPROACHES IN IMPROVING SERVICE LEVEL AGREEMENT-BASED ADMISSION CONTROL FOR A SOFTWARE-AS-A-SERVICE PROVIDER IN CLOUD

Software as a Service (SaaS) offers reliable access to software applications to the end users over the Internet without direct investment in infrastructure and software. SaaS providers utilize resources of internal data centres or rent resources from a public Infrastructure as a Service (IaaS) provider in order to serve their customers. Internal hosting can ample cost of administration and maintenance whereas hiring from an IaaS provider can impact the service quality due to its variable performance. To surmount these drawbacks, we propose pioneering admission control and scheduling algorithms for SaaS providers to effectively utilize public Cloud resources to maximize profit by minimizing cost and improving customer satisfaction level. There is a drawback in this method is strength of the algorithms by handling errors in dynamic scenario of cloud environment, also there is a need of machine learning method to predict the strategies and produce the according resources. The admission control provided by trust model that is based on SLA uses different strategies to decide upon accepting user requests so that there is minimal performance impact, avoiding SLA penalties that are giving higher profit. Machine learning method aims at building a distributed system for cloud resource monitoring and prediction that includes learning-based methodologies for modelling and optimization of resource prediction models. The learning methods are Artificial Neural Network (ANN) and Support Vector Machine (SVM) are two typical machine learning strategies in the category of regression computation. These two methods can be employed for modelling resource state prediction. In addition, we conduct a widespread evaluation study to analyze which solution matches best in which scenario to maximize SaaS provider’s profit. Results obtained through our extensive simulation shows that our proposed algorithms provide significant improvement (up to 40% cost saving) over literature reference ones.

The Patient Admission Scheduling of an Ophthalmic Hospital Using Genetic Algorithm

Currently, FCFS scheduling method is widely used in hospitals for patient admission scheduling, which ignores the impacts of patient length of stay and surgery arrangement on the usage of hospital resources. This paper proposes a more comprehensive mathematical model and evaluation mechanism for the patient admission scheduling of an ophthalmic hospital. A genetic algorithm (GA) is proposed to optimize the model, which can provide detailed scheduling of patient admission in the hospital for everyday. The result is compared with that of the traditional FCFS method, which indicates that the GA helps to reduce the preoperative waiting time for patients. Besides, GA can provide different kinds of scheduling for the hospital to select by adjusting the relative weights of different objectives in the algorithm.

An investigation on the generality level of selection hyper-heuristics under different empirical conditions

The present study concentrates on the generality of selection hyper-heuristics across various problem domains with a focus on different heuristic sets in addition to distinct experimental limits. While most hyper-heuristic research employs the term generality in describing the potential for solving various problems, the performance changes across different domains are rarely reported. Furthermore, a hyper-heuristic's performance study purely on the topic of heuristic sets is uncommon. Similarly, experimental limits are generally ignored when comparing hyper-heuristics. In order to demonstrate the effect of these generality related elements, nine heuristic sets with different improvement capabilities and sizes were generated for each of three target problem domains. These three problem domains are home care scheduling, nurse rostering and patient admission scheduling. Fourteen hyper-heuristics with varying intensification/diversification characteristics were analysed under various settings. Empirical results indicate that the performance of selection hyper-heuristics changes significantly under different experimental conditions.

Intelligent Patient Management and Resource Planning for Complex, Heterogeneous, and Stochastic Healthcare Systems

Effective resource requirement forecasting is necessary to reduce the escalating cost of care by ensuring optimum utilization and availability of scarce health resources. Patient hospital length of stay (LOS) and thus resource requirements depend on many factors including covariates representing patient characteristics such as age, gender, and diagnosis. We therefore propose the use of such covariates for better hospital capacity planning. Likewise, estimation of the patient's expected destination after discharge will help in allocating scarce community resources. Also, probable discharge destination may well affect a patient's LOS in hospital. For instance, it might be required to delay the discharge of a patient so as to make appropriate care provision in the community. A number of deterministic models such as ratio-based methods have failed to address inherent variability in complex health processes. To address such complexity, various stochastic models have therefore been proposed. However, such models fail to consider inherent heterogeneity in patient behavior. Therefore, we here use a phase-type survival tree for groups of patients that are homogeneous with respect to LOS distribution, on the basis of covariates such as time of admission, gender, and disease diagnosed; these homogeneous groups of patients can then model patient flow through a care system following stochastic pathways that are characterized by the covariates. Our phase-type model is then extended by further growing the survival tree based on covariates representing outcome measures such as treatment outcome or discharge destinations. These extended phase-type survival trees are very effective in modeling interrelationship between a patient's LOS and such outcome measures and allow us to describe patient movements through an integrated care system including hospital, social, and community components. In this paper, we first propose a generalization of the Coxian phase-type distribution to a Markov process with more than one absorbing state; we call this the multi-absorbing state phase-type distribution. We then describe how the model can be used with the extended phase-type survival tree for forecasting hospital, social, and community care resource requirements, estimating cost of care, predicting patient demography at a given time in the future, and admission scheduling. We can, thus, provide a stochastic approach to capacity planning across complex heterogeneous care systems. The approach is illustrated using a five year retrospective data of patients admitted to the stroke unit of the Belfast City Hospital.

Adaptive Priority Sliding Admission Control and Scheduling Scheme for DCF and EDCA WLANs

In this paper an Adaptive Priority Sliding Admission Control and Scheduling (APSAS) scheme is proposed to provide QoS over the existing IEEE802.11 WLANs which operate on Distributed Coordination Function (DCF) and Enhanced Distributed Channel Access (EDCA) mechanisms. The roles of this scheme are generally two folds: (1) To control the number of delay-sensitive real time flows that can be admitted into the WLAN Basic Service Set network and (2) To adjust the priority of selected real time flows in order to accommodate more real time flows without violating the QoS requirement. Extensive simulation studies show that APSAS improves the total throughput, flow throughput ratio, packets end-to-end delay, and jitter of the real time applications when compared with conventional best effort and scheduling-enhanced DCF/EDCA. APSAS also offers near to unity average throughput ratio, lower mean VoIP end-to-end packet delay (<130 ms) and lower mean video packet jitter (<130 ms) over DCF and EDCA.

Utility Based Scheduling and Call Admission Control for LTE (3GPP) Networks

In this paper, we propose to design a call admission control algorithm which schedules the channels for real time and non-real time users based on Utility function. In Long Term Evolution (LTE) 3GPP Networks, several works were done on call admission control but these works rarely considers scheduling of resources to the real time and non-real time users. The call requests are classified into new call (NC) request and handoff call (HC) request and the type of services are classified as VoIP and video. Then based upon the received signal strength (RSS) value, the channel is estimated as good channel or bad channel. Resource allocation is made for VoIP users based on traffic density. Then non-VoIP users and the non-real time users are allocated resource blocks using the channel condition based marginal utility function. When there are no sufficient resources to allocate, it allocates the resources of bad channel users there by degrading their service. Thus from our simulation results we show that this admission control algorithm provides channel quality and prioritizes the handover calls over new calls which allocates resources to all kinds of users.

One hyper-heuristic approach to two timetabling problems in health care

We present one general high-level hyper-heuristic approach for addressing two timetabling problems in the health care domain: the patient admission scheduling problem and the nurse rostering problem. The complex combinatorial problem of patient admission scheduling has only recently been introduced to the research community. In addition to the instance that was introduced on this occasion, we present a new set of benchmark instances. Nurse rostering, on the other hand, is a well studied operations research problem in health care. Over the last years, a number of problem definitions and their corresponding benchmark instances have been introduced. Recently, a new nurse rostering problem description and datasets were introduced during the first Nurse Rostering Competition. In the present paper, we focus on this nurse rostering problem description. The main contribution of the paper constitutes the introduction of a general hyper-heuristic approach, which is suitable for addressing two rather different timetabling problems in health care. It is applicable without much effort, provided a set of low-level heuristics is available for each problem. We consider the instances of both health care problems for testing the general applicability of the hyper-heuristic approach. Also, improvements to the previous best results for the patient admission scheduling problem are presented. Solutions to the new nurse rostering instances are presented and compared with results obtained by an integer linear programming approach.