基于负荷平衡的柔性预约决策

针对目前国内医疗机构普遍以步入病人求诊为主且个别时段密集到达的就诊需求特点,引入负荷均衡的思想,提出一种多目标优化的柔性门诊调度决策方法.根据实际负荷分布,合理利用柔性的预约负荷平衡调整各时段的总负荷,基于多种预约率、预约和排队规则形成方案集,在多目标灰靶决策模型下,优化出各应用场景下的最优调度方案.数值实验表明,所提出模型较其他模型能够有效降低等待时间高达77.2%.在实际应用分析中,所提出模型能够提高有效资源利用率66.7%,降低高空闲时间导致的资源敏感度.     

Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections

Analysis of hospital processes is essential for development of improved methods, policies and decision tools for overall performance improvement of the hospital system. Amidst the current scenario of continuously increasing healthcare costs and scarcity of resources, optimal utilization of resources without hampering the quality of care has gained importance in any country. Modelling, analysis and management of patient flows, in this context, plays a key role in performance analysis and improvement of hospital processes as appropriate modelling of patient flows may help healthcare managers make decisions related to capacity planning, resource allocation and scheduling, appointment scheduling and for making necessary changes in the process of care. The concept of patient flow and its modelling has gained much attention in healthcare management literature over past few decades. In this paper, the existing approaches pertaining to modelling of patient flows in hospital systems have been classified and critically appraised focussing on the recent advancements in order to identify future research avenues. A generic framework for patient flow modelling and performance analysis of hospital systems that may serve as a guide for the practitioners dealing with similar kinds of problems to improve healthcare delivery has also been provided.     

Optimal booking and scheduling in outpatient procedure centers

Patient appointment booking, sequencing, and scheduling decisions are challenging for outpatient procedure centers due to uncertainty in procedure times and patient attendance. We extend a previously developed appointment scheduling model to formulate a model based on a two-stage stochastic mixed integer program for optimizing booking and appointment times in the presence of uncertainty. The objective is to maximize expected profit. Analytical insights are reported for special cases and experimental results show that they provide useful rules of thumb for more general problems. Three solution methods are described which take advantage of the underlying structure of the stochastic program, and a series of experiments are performed to determine the best method. A case study based on an endoscopy suite at a large medical center is used to draw a number of useful managerial insights for procedure center managers.     

A hybrid simulation and genetic algorithm approach to determine the optimal scheduling templates for open access clinics admitting walk-in patients

With the pressing demand for improving patient accessibility, the traditional scheduling system may not be effective for mitigating the adverse effects caused by no-shows, appointment cancellations and late arrivals. For this reason, open access scheduling, which specifies that a portion of clinic appointment slots be reserved for short-notice appointments, was proposed and adopted in recent years. In literature, many studies have developed a variety of approaches and models to optimize the open access scheduling systems, while few considers the inclusion of walk-in patients and the optimal allocation of reserved slots on the scheduling template under the open access configuration. In this paper, we propose a Discrete Event Simulation and Genetic Algorithm (DES–GA) approach to find the heuristic optimal scheduling template under the clinic setting that allows both open access and walk-in patients. The solution can provide scheduling templates consisting of not only the optimal number of reservations for open access appointments and walk-ins, but also the optimized allocation of these reserved slots, by minimizing the average cost per admission of open access or walk-in patient. In this approach, the cost is measured by the weighted summation of patient waiting time, provider idle time, and provider overtime. A case study and sensitivity analysis are conducted to show how the heuristic optimal scheduling template generated from the proposed approach could vary under different scenarios. This also illustrates the viability of our model. The results show that the heuristic optimal scheduling templates are significantly affected by the patient attendance rate, level of demands of same-day appointment and walk-in admissions, as well as the cost coefficients associated with patient waiting time, provider idle time and provider overtime.     

Multiobjective study to implement outpatient appointment system at Hedi Chaker Hospital

The outpatient department at Hedi Chaker Hospital in Sfax, Tunisia, is currently using a block scheduling appointment whereby all patients are required to come at the beginning of the session. This brings about a tremendous waste of their time, creates congestion and puts further pressure on the medical staff. This paper evaluates a number of outpatient scheduling schemes based on the most recent research findings. Each model uses an appointment system scheme for scheduling with a well-performing appointment rule crossed with a sequencing rule. Environmental factors, such as the probability of no-shows and the probability of walk-ins, are included in the simulation of each of the different scenarios. Key measures of performance are determined for each of the different scenarios: waiting time in queues for each class of patients and resource utilisation. A Goal Programming Model is developed to choose the most appropriate schedule that optimizes the measures. Management preferences are incorporated to obtain a solution in line with their satisfaction.     

A novel approach to determine a tactical and operational decision for dynamic appointment scheduling at nuclear medical center

In this research, appointment scheduling is addressed in a nuclear medical center. A finite-horizon Markov Decision Process as dynamic programming is applied to formulate the problem by considering the patients' choice behavior, and different no-show rate for patients. The proposed model determines a tactical and operational decision for patient appointments. Based on the tactical decision; How many patients request for hospitalization as they call in and to what slot should they be assigned? According to the operational decision, should a walk-in patient hospitalization request be accepted? Also, this decision determines which patients must receive the services for each slot. One of the distinguishing contributions of this research is that two algorithms and one mathematical programming are developed hierarchically to solve exactly and deal with an intractable dimension of the Markov Decision Process model. Simulation tools are applied to compare the performance of optimal policies with First-Come-First-Serve policy based on a real case. The results show that the proposed model presents a more effective and efficient scheduling compared with current policies for scheduling. More revenue, lower patients waiting during the working day, and lower postponed patients are the results of the proposed model rather than the current policies for scheduling. Then, the impact of revenues, waiting costs, penalty costs, and center’s capacity on the results has been investigated. By increasing revenue and capacity and decreasing waiting costs and penalty costs, the total net revenue is increased.     

Computer simulation and discrete-event models in the analysis of a mammography clinic patient flow

OBJECTIVE: This work develops a discrete-event computer simulation model for the analysis of a mammography clinic performance. MATERIAL AND METHODS: Two mammography clinic computer simulation models were developed, based on an existing public sector clinic of the Brazilian Cancer Institute, located in Rio de Janeiro city, Brazil. Two clinics in a total of seven configurations (number of equipment units and working personnel) were studied. Models tried to simulate changes in patient arrival rates, number of equipment units, available personnel (technicians and physicians), equipment maintenance scheduling schemes and exam repeat rates. Model parameters were obtained by direct measurements and literature reviews. A commercially-available simulation software was used for model building. RESULTS: The best patient scheduling (patient arrival rate) for the studied configurations had an average of 29 min for Clinic 1 (consisting of one mammography equipment, one to three technicians and one physician) and 21 min for Clinic 2 (two mammography equipment units, one to four technicians and one physician). The exam repeat rates and equipment maintenance scheduling simulations indicated that a large impact over patient waiting time would appear in the smaller capacity configurations. CONCLUSIONS: Discrete-event simulation was a useful tool for defining optimal operating conditions for the studied clinics, indicating the most adequate capacity configurations and equipment maintenance schedules.     

Single versus hybrid time horizons for open access scheduling

Difficulty in scheduling short-notice appointments due to schedules booked with routine check-ups are prevalent in outpatient clinics, especially in primary care clinics, which lead to more patient no-shows, lower patient satisfaction, and higher healthcare costs. Open access scheduling was introduced to overcome these problems by reserving enough appointment slots for short-notice scheduling. The appointments scheduled in the slots reserved for short-notice are called open appointments. Typically, the current open access scheduling policy has a single time horizon for open appointments. In this paper, we propose a hybrid open access policy adopting two time horizons for open appointments, and we investigate when more than one time horizon for open appointments is justified. Our analytical results show that the optimized hybrid open access policy is never worse than the optimized current single time horizon open access policy in terms of the expectation and the variance of the number of patients consulted. In nearly 75% of the representative scenarios motivated by primary care clinics, the hybrid open access policy slightly improves the performance of open access scheduling. Moreover, for a clinic with strong positive correlation between demands for fixed and open appointments, the proposed hybrid open access policy can considerably reduce the variance of the number of patients consulted.     

A computerized resolution of scheduling conflicts

Residency training programs in Internal Medicine require resident attendance in a continuity clinic. This inevitably engenders conflicts between scheduling in the ambulatory clinic and the required teaching activities of the in-patient services. Some of the conflicts can be resolved by allowing the in-patient service directors to indicate preferred plans for their residents to attend in the continuity clinic. With this plan, scheduling becomes quite complicated. A computer program is presented with coordinates these service requests with ambulatory clinic scheduling. As a result, a given resident may have his/her clinic day changed on different rotations. The program automatically arbitrates conflicts and publishes the attendance dates for the entire academic year. This information is supplied to the appointment desk so that patients can be scheduled accordingly. This system has resulted in a 74% continuity rate and improved satisfaction by both residents and staff.     

Web server QoS models: applying scheduling rules from production planning

Most web servers, in practical use, use a queuing policy based on the Best Effort model, which employs the first-in-first-out (FIFO) scheduling rule to prioritize web requests in a single queue. This model does not provide Quality of Service (QoS). In the Differentiated Services (DiffServ) model, separate queues are introduced to differentiate QoS for separate web requests with different priorities. This paper presents web server QoS models that use a single queue, along with scheduling rules from production planning in the manufacturing domain, to differentiate QoS for classes of web service requests with different priorities. These scheduling rules are Weighted Shortest Processing Time (WSPT), Apparent Tardiness Cost (ATC), and Earliest Due Date. We conduct simulation experiments and compare the QoS performance of these scheduling rules with the FIFO scheme used in the basic Best Effort model with only one queue, and the basic DiffServ model with two separate queues. Simulation results demonstrate better QoS performance using WSPT and ATC, especially when requested services exceed the capacity of a web server.     

面向服务的测量船测控服务总线系统

当前测量船测控软件系统需要完成对各类飞行器的遥测、遥控、外测、仿真等数据的处理,该系统复杂性较高,不易扩展和维护。面向服务的体系架构SOA是一个组件模型,它将复杂系统的不同功能单元封装为服务,并通过接口实现对服务的调用,大大降低了系统的复杂性,方便了系统功能的扩展。分析了测量船测控软件系统,设计了面向服务的测量船测控服务总线系统架构,该架构支持“服务调度规则”与“服务调度系统”2种服务调度模式,实现了系统服务端、客户端和服务调度。测试结果表明：该系统能够保证2种模式下的服务调用性能,对提高测量船测控软件系统的效率具有重要意义。     

Support optimal scheduling with weighted random forest for operation resources

Operation-related resources are lots of manpower and material with the characteristics of high cost and high income in hospitals, and scheduling optimization is a very important research issue in medical service. In this paper, to cope with the actualities of operation resources scheduling, such as poor planning, lack of standardized scheduling rules, chaotic use of the operating rooms, and many human interference factors, we propose a systematic approach to optimize scheduling problems based on multiple characteristics of operating resources. We first design a framework that includes the composite dispatching rules (CDR), optimization ideology, and feedback mechanism, in which the CDR integrates flexible operating time, hold-up time of medical facilities, available time of medical staff, and multiple constraints. The optimization ideology is carried out through a learning model based on the weighted random forest (WRF) algorithm. The feedback mechanism enables the approach to realize closed-loop optimizations adaptively. Finally, the superiority of the systematic scheduling approach (SSA) is analyzed through numerical experiments on a simulation platform. Results of the simulation experiments show that the proposed scheduling method can improve performances significantly, especially in the waiting time of patients.     

Combined mathematical programming and heuristics for a radiotherapy pre-treatment scheduling problem

Demand for radiotherapy treatment services has increased due to higher cancer incidence, ageing population, and a larger number of prescribed radiotherapy fractions. This higher demand can, in turn, produce longer waiting times for radiotherapy treatment. We introduce a real-world radiotherapy pre-treatment scheduling problem at a hospital in the UK. This scheduling problem is modelled as an optimisation problem with multiple objectives. The objectives are hierarchical, thus the solution approach is based on solving a series of single-objective optimisation scheduling problems. Each of these problems is formulated as a mathematical programming model. Dispatching rules were introduced to produce an initial solution for the mathematical programming model. Their performance is compared to the mathematical programming approach.     

A multilevel integrative approach to hospital case mix and capacity planning

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.     

Performance evaluation study for QoS-aware triple play services over entry-level xDSL connections

Nowadays IT and telecom worlds and specifically all network, service, and content providers show special interest in multiservice delivery. The concurrent provision of video or TV content, voice, and fast Internet via the same network and user equipment, known as “triple play service”, constitutes one of the major concerns in modern telecommunications due to the latest developments in the areas of video and audio encoding which strain the capacity of content delivery systems. The combination of the above technologies has raised significant quality of service related issues. Problems such as full exploitation of available bandwidth, providing adequate quality of service to subscribers and meeting the requirements of all three supported services (video, voice, and data), must be addressed. Based on this framework, the paper examines the performance of the most common packet scheduling techniques (PQ, WFQ, WFQ-LL) used in IP triple play architecture, considering also two different packetization schemes applied to single-layer video. The performance of these scheduling algorithms is assessed, in order to propose the most appropriate solution, supporting the triple play bundle. The evaluation of schedulers is based on simulations designed taking into account existing triple play networks, while trace files are used for the simulation of video flows.     

数据中心基于服务满足度的网络流避让方法

越来越多的行业开始利用云以降低成本提高生产力,支撑多样化的服务对数据中心的网络性能提出了更高的要求,如何高负载下优先保证各项服务的质量变得至关重要,云服务提供商同时也关注如何提高数据中心的网络资源利用率并降低能耗。结合上述问题,提出一种基于服务满足度对非服务网络流进行调度的方法。引入服务满足度这一概念,评估网络状态能否满足服务需要,然后依据网络流所支撑的不同服务将其分类,网络负载激增时基于服务满足度调整非用户服务依赖的网络流,降低网络负载缓解拥塞。仿真结果表明,主动避让方法在网络高负载时能优先保证服务的质量,同时提高网络性能。     

基于两阶段随机仿真优化算法的体检顾客预约调度

针对体检机构顾客排队等待时间长的问题，研究随机服务时间下的体检顾客调度，采用多人时间槽预约策略，并在预约调度策略的基础上优化每位顾客的体检项目顺序，提出一种包含粗糙仿真评估和精确仿真评估两阶段随机仿真优化算法。运用序优化思想将基于亲和度评估的多种群遗传算法作为迭代优化策略，并利用改进的最优计算量分配方法排除超级个体的影响，形成仿真资源的全局和自适应优化分配机制。实验结果表明，与不进行任何调度及使用体检顺序启发式调度规则的离散事件结果相比，该算法获得了更好的调度解。     

An IoT-Aware System for Managing Patients’ Waiting Time Using Bluetooth Low-Energy Technology

It is a common observation that whenever patients arrives at the front desk of a hospital, outpatient clinic, or other health-associated centers, they have to first queue up in a line and wait to fill in their registration form to get admitted. The long waiting time without any status updates is the most common complaint, concerning health officials. In this paper, UrNext, a location-aware mobile-based solution using Bluetooth low-energy (BLE) technology is presented to solve the problem. Recently, a technology-oriented method, the Internet of Things (IoT), has been gaining popularity in helping to solve some of the healthcare sector’s problems. The implementation of this solution could be illustrated through a simple example of when a patient arrives at a clinic for a consultation. Instead of having to wait in long lines, that patient will be greeted automatically, receive a push notification of an admittance along with an estimated waiting time for a consultation session. This will not only provide the patients with a sense of freedom but would also reduce the uncertainty levels that are generally observed, thus saving both time and money. This work aims to improve the clinics’ quality of services, organize queues and minimize waiting times, leading to patients’ comfort while reducing the burden on nurses and receptionists. The results demonstrate that the presented system is successful in its performance and helps achieves a pleasant and conducive clinic visitation process with higher productivity.     

Conceptual frame for development of optimized simulation-based scheduling systems

The detailed system architecture of the optimized simulation-based scheduling system (OSBSS) is presented to generate an optimized scheduling. In OSBSS environment, a simulation optimizer interactively communicates with a simulation model to improve the current scheduling with respect to the performance criteria. The performance of a simulation engine is very important issue in developing OSBSS since it is necessary to simulate multiple alternatives until given criteria are satisfied. The integration of the discrete event simulation with a rule-based system is suggested to effectively handle condition-based events within a rule-based environment. The capability to change the dispatching rules during simulation is also desirable to reduce OSBSS development efforts. Database-driven simulation model generation concept is presented to effectively generate and maintain the simulation model with updated domain data. The generic development procedures for OSBSS, using IDEF modeling methods, are presented, which may serve as a template for actual development.     

Job shop scheduling—A systematic approach

A systematic analysis of shop performance versus different scheduling rules is presented in this paper using a computer simulation model of a conventional job shop as a basis. The simulation model is used to identify dependent parameters such as shop load and service distribution for a set of scheduling rules. These conditions should represent different existing shop conditions. Six different scheduling rules and two different performance measures were used in the analysis. A complete statistical analysis and categorization of the simulation results were performed which produced some interesting anticipated as well as unanticipated results.