Probabilistic Priority Research Articles

A Net Present Value Analysis of Opportunity-Based Age Replacement Models in Discrete Time

Two important opportunistic age replacement models, under replacement first and last disciplines, are generalized in discrete time. The net present value (NPV) is applied to formulate the expected total costs. The priority of multiple replacement options is considered to classify the cost model with discounting into six cases. Since the NPV method accurately calculates the expected replacement costs over an infinite horizon in an unstable economic environment, we discuss some optimal opportunistic age replacement policies which minimize the expected total discounted costs over an infinite time horizon. Furthermore, we formulate a unified model under each discipline, merging six discrete time replacement models with probabilistic priority. Finally, a case study on optimal replacement first and last policies for pole air switches in a Japanese power company is presented.

Effects of Probabilistic Priority on Delay at Permissive Left-Turn Signalized Intersections

Effects of Probabilistic Priority on Delay at Permissive Left-Turn Signalized Intersections

Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic.

This research analyses the relations between anxiety symptoms from the network perspective to deepen the understanding of anxiety in front-line medical staff during the COVID-19 pandemic and can also provide a reference for determining potential goals of clinical interventions. A convenience sampling was adopted, and the Generalized Anxiety Disorder 7-item scale (GAD-7) was administered to front-line medical staff through online platforms. A regularized partial correlation network of anxiety was constructed and then we evaluated its accuracy and stability. The expected influence and predictability were used to describe the relative importance and the controllability, using community detection to explore community structure. The gender-based differences and the directed acyclic graph were implemented. The connections between A1 "Feeling nervous, anxious or on edge" and A2 "Not being able to stop or control worrying", A6 "Becoming easily annoyed or irritable" and A7 "Feeling afraid as if something awful might happen", etc., were relatively strong; A2 "Not being able to stop or control worrying" and A3 "Worrying too much about different things" had the highest expected influence, and A2 "Not being able to stop or control worrying" had the highest predictability. The community detection identified two communities. The results of the gender network comparison showed the overall intensity of the anxiety network in women was higher than that in men; DAG indicated that A2 "Not being able to stop or control worrying" had the highest probabilistic priority; the lines from A2 "Not being able to stop or control worrying" to A1 "Feeling nervous, anxious or on edge" and A2 "Not being able to stop or control worrying" to A7 "Feeling afraid as if something awful might happen" represented the most important arrows. There exist broad interconnections among anxiety symptoms of front-line medical staff on the GAD-7. A2 "Not being able to stop or control worrying" might be the core symptom and a potential effective intervention target. It was possible to bring an optimal result for the entire GAD symptom network by interfering with A2 "Not being able to stop or control worrying". GAD may have two "subsystems". The modes of interconnection among anxiety may be consistent between genders.

Traffic Queue Length Estimation at Permissive Left-Turn Signalized Intersections with Probabilistic Priority

Probabilistic yielding behaviors are often observed at permissive left-turn signalized intersections in some countries, which tend to affect the operational performance of intersections such as queue length of left-turn traffic. Based on queuing theory, this research developed an analytical queue length estimation model that took into account the probabilistic priority phenomenon at permitted left-turn signals. The service time distributions for both queued and nonqueued left-turn vehicles were derived, and the M/G2/1 queuing model was applied to determine the queue length of left-turn traffic. To validate the developed queue length model, stochastic simulations with different combinations of left-turn yielding rates, left-turn traffic saturation ratios, and through traffic flow rates were performed. It was found that the left-turn traffic saturation ratio is the most critical factor for the estimation of left-turn traffic queue length. In addition, for permitted intersections with a single through lane and a single left-turn lane, when the opposite through traffic flow rate is greater than 0.3 veh/s, the impact of left-turn yielding rate on left-turn queue length tends to be more significant.

Distance-Based Service Priority: An Innovative Mechanism to Increase System Throughput and Social Welfare

Problem definition: The main goal of many nonprofit or nongovernmental organizations is to increase the number of customers who receive service (i.e., service coverage) and social welfare. However, the limited number of employees, volunteers, and service locations results in long service wait. In addition, getting customers living in remote areas to receive services by traveling long distances is difficult. We propose an innovative distance-based service priority policy that would reduce the service waiting time for customers who must travel farther for the service by giving them higher service priority, thereby providing them with a new incentive to seek service. Methodology/results: Using a game-theoretic queueing model, we show that the proposed policy can significantly attract more customers to a service. The increase can be up to 50% compared with the ordinary first-come-first-served service discipline. The policy can also achieve higher social welfare, however, that may come at the cost of reduced customer welfare. We therefore propose a possible remedy for a social planner to coordinate welfare under such circumstance. It ensures all stakeholders, including the service provider, customers, and society, can benefit from the policy at the same time. Finally, we compare our distance-based service priority policy with two existing strategies from the literature—namely, the price discrimination strategy and the probabilistic priority strategy. Managerial implications: Our proposed policy can play a pivotal role in a nonprofit service provider’s mission to increase service coverage and social welfare, especially when customers’ travel costs to obtain service are significant. Furthermore, our policy may create fewer implementation and fairness concerns compared with related strategies. Funding: Z. Wang acknowledges financial support from the National Natural Science Foundation of China [Grants 72001118 and 72132007], and L. Fang acknowledges financial support from the National Natural Science Foundation of China [Grant 72271129]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1157 .

Capacity Modeling of Permitted Left-Turn Signalized Intersections with Probabilistic Priority

Probabilistic yielding behavior is commonly observed at permitted left-turn signalized intersections in China. Nevertheless, its impact on traffic capacity has not been explored in existing research. The uniqueness of this traffic operation is that neither left-turn traffic nor through traffic holds an absolute priority. Based on queuing theory, this research developed an analytical capacity model that took into account the probabilistic priority phenomenon at permitted left-turn signals. To validate the developed capacity model, stochastic simulations with different combinations of left-turn traffic yielding rates and through-traffic flow rates were performed. It was found that modeling results from the analytical model precisely matched the stochastic simulation results. In comparison with traditional capacity estimation models that assumed through traffic holds an absolute priority, this research revealed that when through-traffic flow rate is around 1000 vehicles per hour (vph), the capacity of left-turn traffic increased from 233 vph to 536 vph when left-turn traffic yielding rates decrease from 1 to 0.2; whereas when left-turn traffic yielding rates decreased from 0.2 to 0, left-turn capacity increased sharply to 1200 vehicles per hour (vph). In this regard, it is critical to take into account the probabilistic left-turn yield behavior when developing permitted left-turn signal warrants.

Some Parameterized Dynamic Priority Policies for Two-Class M/G/1 Queues

Completeness of a dynamic priority scheduling scheme is of fundamental importance for the optimal control of queues in areas as diverse as computer communications, communication networks, supply/value chains, and manufacturing systems. Our first main contribution is to identify the mean waiting time completeness as a unifying aspect for four different dynamic priority scheduling schemes by proving their completeness and equivalence in two-class M/G/1 queues. These dynamic priority schemes are earliest due date based, head of line priority jump, relative priority, and probabilistic priority. We discuss major challenges in extending our results to three or more classes. In our second main contribution , we characterize the optimal scheduling policies for the case studies in different domains by exploiting the completeness of the above dynamic priority schemes. The major theme of the second main contribution is resource allocation/optimal control in revenue management problems for contemporary systems such as cloud computing, high performance computing, ans so forth, where congestion is inherent. Using completeness and the theoretically tractable nature of relative priority policy, we study the impact of approximation in a fairly generic data network utility framework. Next, we simplify a complex joint pricing and scheduling problem for a wider class of scheduling policies.

Task Popularity-Based Energy Minimized Computation Offloading for Fog Computing Wireless Networks

Fog computing is a new systematic paradigm which provides low latency enabled cloud services to mobile and Internet of Things (IoT) networks by provisioning the computation capability within the radio access network (RAN) assignable to mobile end users. In this letter, an energy optimal offloading scheme based on a probabilistic priority model of cloud tasks is investigated over fog computing networks. The optimization problem jointly minimizes the energy consumption of user equipment (UE) and the fog server. Simulation results show that the proposed joint UE and fog server energy optimization (JUFO) scheme provides a better performance compared to the conventional offloading scheme, which operates strictly within the determined delay bound.

On the power series approximations of a structured batch arrival two-class retrial system with weighted fair orbit queues

We provide power series approximations for a structured batch arrival single server retrial system with probabilistic priority and two infinite capacity weighted fair orbit queues, under a queue-based constant retrial policy. We consider both exponential and arbitrary distributed service times. In both cases we obtain power series expansions of the generating functions of the stationary joint orbit queue-length distributions, and provide a recursive approach to calculate their coefficients. Numerical results show the accuracy of our approach, and useful insights in the system performance.

Predictive Triggering for Distributed Control of Resource Constrained Multi-agent Systems

A predictive triggering (PT) framework for the distributed control of resource constrained multi-agent systems is proposed. By predicting future communication demands and deriving a probabilistic priority measure, the PT framework is able to allocate limited communication resources in advance. The framework is evaluated through simulations of a cooperative adaptive cruise control system and experiments on multi-agent cart-pole systems. The results of these studies show its effectiveness over other event-triggered designs at reducing network utilization, while also improving the control error of the system.

Machine learning-based discrete event dynamic surrogate model of communication systems for simulating the command, control, and communication system of systems

Command and control (C2) and communication are at the heart of successful military operations in network-centric warfare. Interoperable simulation of a C2 system model and a communication (C) system model may be employed to interactively analyze their detailed behaviors. However, such simulation would be inefficient in simulation time for analysis of combat effectiveness of the C2 model against possible input combinations while considering the communication effect in combat operations. This study proposes a discrete event dynamic surrogate model (DEDSM) for the C model, which would be integrated with the C2 model and simulated. The proposed integrated simulation reduces execution time markedly in analysis of combat effectiveness without sacrificing the accuracy reflecting the communication effect. We hypothesize the DEDSM as a probabilistic priority queuing model whose semantics is expressed in a discrete event systems specification model with some characteristic functions unknown. The unknown functions are identified by machine learning with a data set generated by interoperable simulation of the C2 and C models. The case study with the command, control, and communication system of systems first validates the proposed approach through an equivalence test between the interoperable simulation and the proposed one. It then compares the simulation execution times and the number of events exchanged between the two simulations.

Traffic queuing at unsignalized crosswalks with probabilistic priority

Probabilistic yielding behavior is often observed at unsignalized crosswalks, but its impacts on the traffic flow in terms of traffic capacity and associated vehicular delay have not been examined before. The uniqueness of this problem is that neither traffic nor pedestrian flow holds the absolute priority as normally assumed in existing literature. Based on queuing theory, this paper developed traffic capacity and delay formulas considering the probabilistic priority. The proposed capacity equation reduces to the classic formula when the yielding rate equals one. The service time distributions for both queuers and non-queuers were derived, and M/G2/1 queuing model was applied to determine queue length and vehicular delay. Stochastic simulations showed that the proposed capacity and delay formulas precisely match the simulation results. For ease of practical applications, simpler queuing formulas including M/G/1, M/M/1 and M/D/1 were also examined. Noting that the differences between M/G2/1 and M/G/1 model are only marginal, especially for low yielding rates, we recommend the M/G/1 model for practical applications. In addition, when the pedestrian volume is relatively low, the M/M/1 model is also applicable due to its sufficient accuracy and simplicity.

Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features.

The essence of image-based localization lies in matching 2D key points in the query image and 3D points in the database. State-of-the-art methods mostly employ sophisticated key point detectors and feature descriptors, e.g., Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT), to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast. The naive usage of binary features, however, does not lend itself to significant speedup of localization, since existing indexing approaches, such as hierarchical clustering trees and locality sensitive hashing, are not efficient enough in indexing binary features and matching binary features turns out to be much slower than matching SIFT features. To overcome this, we propose a much more efficient indexing approach for approximate nearest neighbor search of binary features. This approach resorts to randomized trees that are constructed in a supervised training process by exploiting the label information derived from that multiple features correspond to a common 3D point. In the tree construction process, node tests are selected in a way such that trees have uniform leaf sizes and low error rates, which are two desired properties for efficient approximate nearest neighbor search. To further improve the search efficiency, a probabilistic priority search strategy is adopted. Apart from the label information, this strategy also uses non-binary pixel intensity differences available in descriptor extraction. By using the proposed indexing approach, matching binary features is no longer much slower but slightly faster than matching SIFT features. Consequently, the overall localization speed is significantly improved due to the much faster key point detection and descriptor extraction. It is empirically demonstrated that the localization speed is improved by an order of magnitude as compared with state-of-the-art methods, while comparable registration rate and localization accuracy are still maintained.

A semi-analytical non-iterative primary approach based on priority list to solve unit commitment problem

For many years, the UC (unit commitment) problem has been solved by complex numerical techniques or intelligent search algorithms, due to nonlinear and complex constraints. Many of the applied algorithms employ random searches, which leads to production of different solutions in different program runs. Priority list-based methods are a way out to this, as they produce robust results during a non-iterative procedure, and without help of trial and error efforts. Nevertheless, they have all proven inefficient. This paper introduces a new approach that generates the solutions using algorithm-specific constraint handling techniques, based on the priority list concept. The solution-making stages include: 1. Minimum up/down time establishment using a probabilistic priority list-oriented selection mechanism, 2. Spinning reserve constraint handling through a deterministic priority list-based process, 3. Power balance handling and a ramp rate modification procedure for generating efficient ramp-constrained solutions. The different steps are designed such that efficient modifications are applied in each step without violating the previously established constraints. Simulation results on different test systems reveal that the approach obtains robust and competitive results. A new 140-unit large-scale test system based on Korean power system is also presented for verifying applicability of the proposed approach on real world power systems.

Probabilistic Priority Assessment of Nurse Calls

Current nurse call systems are very static. Call buttons are fixed to the wall, and systems do not account for various factors specific to a situation. We have developed a software platform, the ontology-based Nurse Call System (oNCS), which supports the transition to mobile and wireless nurse call buttons and uses an intelligent algorithm to address nurse calls. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff and patients into account by using an ontology. This article describes a probabilistic extension of the oNCS that supports a more sophisticated nurse call algorithm by dynamically assigning priorities to calls based on the risk factors of the patient and the kind of call. The probabilistic oNCS is evaluated through implementation of a prototype and simulations, based on a detailed dataset obtained from 3 nursing departments of Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls among nurses, and the assignment of priorities to calls are compared for the oNCS and the current nurse call system. Additionally, the performance of the system and the parameters of the priority assignment algorithm are explored. The execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the probabilistic oNCS significantly improves the assignment of nurses to calls. Calls generally result in a nurse being present more quickly, the workload distribution among the nurses improves, and the priorities and kinds of calls are taken into account.

A Process Model for Route Choice in Risky Traffic Networks

The accurate modeling of travelers’ route choice decision making when faced with unreliable (risky) travel times is necessary for the assessment of policies aimed at improving travel time reliability. Compared with econometric models, process models have not been investigated in travel decision making under risk. A process model aims to describe the actual decision making procedure and could potentially provide a better explanation to route choice behavior. A process model, the priority heuristic (Brandstatter et al. , 2006), is introduced to the travel choice context and its probabilistic version, the probabilistic priority heuristic (PPH) model, is developed in this study. With data collected from a stated preference survey, a rank-dependent expected utility (RDEU) model and two other alternative models are compared with the PPH model through cross validation. Results showed that the PPH model outperforms the RDEU model in both data- fitting and predictive performances. This suggests that the process modeling paradigm could be a promising new area in travel behavior research. Major drawbacks of the PPH model include the discontinuity of the choice probability with respect to outcomes and associated probabilities, the limited applicability in situations where one alternative dominates or almost dominates the other, and the non-trivial extension to multiple-alternative situations.

Probabilistic priority numbers for failure modes and effects analysis

PurposeThe purpose of this paper is to propose a method, derived from numerical evaluations on the criteria of security, frequency and detectability, of Failure Modes and Effects Analysis (FMEA), a probabilistic priority measure for potential failures; and to evaluate the use of this method when combined with subjective evaluations to decide on improvement actions.Design/methodology/approachThe method proposed is based on treating the numerical initial measurements as estimates of location parameters of probability distributions. This allows for objectively taking into account the uncertainty inherent in such measurements and to compute probabilities of each potential failure being the most important according to each criterion. These probabilities are then combined into a global quality measure, which can be interpreted as a joint probability of choice of the potential failure.FindingsThe results obtained in the cases studied show the suitability of the changes proposed. Thresholds levels proposed for the discretization of the probabilistic scores are also shown to be able to allow for an efficient combination with experts' evaluations.Research limitations/implicationsThe approach here developed allows for the introduction of statistical parameters in the first stage of FMEA modeling. Employing a more complete model leads to greater reliability of the methodology.Practical implicationsThe more precise modeling asks for a certain degree of practical knowledge of the factors effectively introducing variability in the measurements. This need may be surpassed by the choice of distributions such as those here employed.Originality/valueThe evaluation of the potential failures by the probability of being the most important, according to each criterion, is new in FMEA.

An ontology-based nurse call management system (oNCS) with probabilistic priority assessment

BackgroundThe current, place-oriented nurse call systems are very static. A patient can only make calls with a button which is fixed to a wall of a room. Moreover, the system does not take into account various factors specific to a situation. In the future, there will be an evolution to a mobile button for each patient so that they can walk around freely and still make calls. The system would become person-oriented and the available context information should be taken into account to assign the correct nurse to a call.The aim of this research is (1) the design of a software platform that supports the transition to mobile and wireless nurse call buttons in hospitals and residential care and (2) the design of a sophisticated nurse call algorithm. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff members and patients into account. Additionally, the priority of a call probabilistically depends on the risk factors, assigned to a patient.MethodsThe ontology-based Nurse Call System (oNCS) was developed as an extension of a Context-Aware Service Platform. An ontology is used to manage the profile information. Rules implement the novel nurse call algorithm that takes all this information into account. Probabilistic reasoning algorithms are designed to determine the priority of a call based on the risk factors of the patient.ResultsThe oNCS system is evaluated through a prototype implementation and simulations, based on a detailed dataset obtained from Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls amongst nurses and the assignment of priorities to calls are compared for the oNCS system and the current, place-oriented nurse call system. Additionally, the performance of the system is discussed.ConclusionsThe execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the oNCS system significantly improves the assignment of nurses to calls. Calls generally have a nurse present faster and the workload-distribution amongst the nurses improves.

Optical MAN ring performance with traffic aggregations

We propose to study end to end delays in slotted optical MAN (Metropolitan Area Networks) ring networks. Edge nodes interconnect the fibre of the MAN and the client layer. We propose a mechanism based on IP traffic aggregations with QoS requirements in order to build optical packets. IP packet aggregation is performed in a loop manner by always beginning the aggregation cycle with the highest priority class. In order to overcome the drawbacks of the strict priority discipline, an algorithm depicting a probabilistic priority discipline is presented. The aggregation technique leads to increasing the filling ratio of an optical packet, and consequently, the bandwidth efficiency of an optical network. We present an analytical model in order to evaluate the performance of the aggregation mechanism. Secondly, we study the performance of the optical MAN ring with different packet formats. The MAC protocol is based on the “empty slot” procedure. Different queueing models based on the GI/G/1 queues with server interruptions are presented in order to compute mean packet delays according to the rank nodes.

BMAP/G/1/∞ system with last come first served probabilistic priority

Consideration was given to the BMAP/G/1/∞ single-server queuing system with batch Markov arrivals, arbitrary servicing, finite-capacity buffer, and last come first served probabilistic priority. Equations for determination of the stationary probabilities of states and stationary characteristics associated with the time of customer sojourn in the system were determined.