Set Of Schedules Research Articles

Methodology and analytical approach to investigate the impact of building temperature setpoint schedules

This paper presents a method for generating a large set of stochastically varying temperature setpoint schedules for building performance simulations. It analyses tradeoffs resulting from specific changes to those schedules in terms of three quantities: total electricity consumption per day; maximum hourly electricity consumption per day and predicted percentage dissatisfied (occupant thermal comfort). The method for generating schedules requires a single base schedule as the starting point and, using a few clearly defined parameters, transforms it into a set of schedules that can be used for modelling an existing building stock or for performing parametric studies. Temperature setpoint schedules are generated and simulated for a small office building in a cool-dry climate in three different case studies pertaining to changing temperature setpoint schedules. Tradeoffs between the three output metrics are significant and vary based on the temperature setpoint schedules and the time of the year.

Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

Most existing evolutionary approaches to satellite range scheduling seek optimal solution in terms of the request satisfaction. The scheduling demand of managing ground station resource is seldom considered, which restricts their real-world applications. To effectively generate a set of more rational satellite range schedules, this paper establishes the multi-objective satellite range scheduling mathematical model. Unlike existing approaches, we propose a general population generation approach to solve the problem without relying on any specific kind of evolutionary algorithm, so different types of evolutionary algorithms can be extended to satellite range scheduling without modifying the original framework and search strategy. The idea is to utilize the request satisfaction and resource utilization knowledge learnt from parent schedules to guide the generation and updating of new solutions. Furthermore, an iterative rewriting operator is designed to guide a biased faster convergence towards the low request failure region in objective space. The proposed approach has been applied to five different types of classical and state-of-the-art evolutionary algorithms and examined on benchmark problems. Experimental results illustrate the search efficiency enhancement and good adaptability to different evolutionary algorithms, which show the broad application prospect for satellite range scheduling.

Multiple-Cytokine Profiling: A Novel Method for Early Prediction of the Efficacy of Sublingual Immunotherapy in Allergic Rhinitis Patients.

BackgroundAllergic rhinitis (AR) is a common inflammatory airway disease, and allergen-specific immunotherapy (AIT) is the only disease-modifying treatment for it. However, not all AR patients respond to AIT, and early prediction of patient response is extremely important. This study aimed to example serum levels of multiple cytokines in AR and explore their association with the efficacy of AIT.MethodsA total of 74 AR patients treated with sublingual immunotherapy (SLIT) were prospectively recruited. Serum samples were obtained before the onset of SLIT and cytokine levels detected by multiplex analysis. All patients were followed for >1 year, and associations between cytokine levels and the early efficacy of SLIT were evaluated. Significantly distinctive cytokines were further verified in another independent cohort.ResultsSixty patients completed the visit schedule set: 35 patients were put into a responder group and 25 a nonresponder group. Multiple-cytokine profiling showed that cytokine levels differed significantly between the two groups. The responder group had higher concentrations of BAFF and CCL11 and lower levels of CCL2, CCL7, IFNγ, IL8, IL10, IL16, and IL33 than the nonresponder group (P<0.05). Receiver-operating characteristic curves highlighted that serum BAFF, IFNγ, IL10, and IL33 levels were strongly predictive of the efficacy of SLIT (area under the curve <0.7, P<0.05). Serum IL10 and IL33 were overexpressed in nonresponders in the validation cohort. Patients in the responder group exhibited significantly higher IL10 levels and lower IL33 post-SLIT than pre-SLIT (P<0.05), but no statistical difference was found in nonresponders (P<0.05).ConclusionOur data indicated that serum multiple-cytokine profiling was associated with response to SLIT and that IL10 and IL33 might serve as novel biomarkers for early prediction of efficacy and be involved in the therapeutic mechanisms of SLIT in AR patients.

Design of coordinated transmission mechanism of heterogeneoustime-sensitive data flow for industrial internet

<p indent="0mm">Industrial internet needs to realize the integration of IT (information technology) and OT (operation technology). The construction of the field-level industrial network is key to truly realizing seamless access of production line data from OT to IT and the intelligent operation and maintenance of the production line from IT to OT. For OT, the data in the industrial system is highly time-sensitive, and the deterministic behavior of perceived data and control instructions in time is the foundation for reliable and stable industrial system operation. However, the industrial field is plagued by problems such as heterogeneous perceived data, incompatible protocols, and varying transmission requirements, which pose significant challenges to the deterministic transmission of heterogeneous time-sensitive flow. One feasible solution to this problem is to design a unified time-aware gating mechanism with low delay and jitter, allowing precise control of data flow transmission behavior; this is one of the key technologies in the time-sensitive networking (TSN) standard. This paper mainly studies the coordinated transmission of heterogeneous time-sensitive flow in multi-hop TSN networks. First, heterogeneous time-sensitive traffic is modeled into two categories: periodic time-sensitive (PTS) flow and sporadic time-sensitive (STS) flow. Then, the schedulability of the PTS flow set and the design method of the gate control lists are analyzed, the sufficient and necessary conditions of schedulability are defined, and a schedulability discriminant algorithm is designed to determine the schedulability of any decision set. Then, a coordinated transmission framework of heterogeneous time-sensitive flow is proposed, followed by the construction of the constraint set of heterogeneous data scheduling. This paper employs the design method of no-wait slot scheduling and constructs the heterogeneous time-sensitive flow scheduling problem with the optimization goal of minimizing the maximum slot occupation in the scheduling unit to achieve the minimum end-to-end deterministic delay of PTS flow and to reduce the impact of PTS scheduling mechanism on STS flow transmission as much as possible. Then, a uniform arrangement and filling (UAF) algorithm is developed to minimize slot occupation of scheduling units by uniformly arranging the flow of the same period and filling the idle time slots generated during pre-period scheduling. Finally, simulation and experiment results corroborate the effectiveness of the two kinds of heterogeneous time-sensitive flow coordinated transmission framework and the efficiency of the UAF scheduling algorithm based on ensuring the same scheduling performance.

An exact solution approach for an electric bus dispatch problem

We study how to efficiently plan a bus dispatch operation within a public transport terminal working with a mixed fleet of electric and diesel buses and a restricted number of chargers. To meet the daily trip demand, the terminal dispatcher has to assign a trip schedule and a battery charge plan to each bus and also feasibly sequence charging tasks at each charger. We model this problem as an extension of the Vehicle Scheduling Problem, which we later reformulate via a Benders’ type decomposition approach into two sub-problems; (1) a master problem assigning bus trip schedules and (2) a satellite problem sequencing charging tasks for a given set of bus trip schedules. Our exact decomposition approach dynamically injects feasibility cuts into the branch-and-bound tree to remove bus trip schedules leading to an infeasible bus charging operation. We assess the effectiveness of our approach and its advantage over a single-stage model in computational experiments inspired by a bus operator from Santiago, Chile. Finally, we provide several managerial insights for planners such as the marginal benefit per additional charger or electric bus and the value added by a mixed fleet compared to a pure electric one.

A Comparison of Patient Outcomes Using Multimodal Analgesia Versus Opioid-Based Pain Management in Total Joint Arthroplasty.

Multimodal analgesia (MMA) pain management following total joint arthroplasty (TJA) is gaining momentum as a best practice. Many forces in healthcare are coming together challenging orthopaedic teams to reevaluate postoperative pain management following surgery including the opioid crisis and pressures to improve patient experience with early discharges following surgery. Measuring the effect of adjustments to pain management is an important step. This retrospective, observational study evaluated the effect of a multimodal postoperative analgesia regimen on patient outcomes and opioid use at a Midwest academic medical center. Two cohorts of patients were compared. Those who underwent TJA from November 2016 to April 2017 and received pain management by the traditional supplemental "as-needed" opioid-based pain management order set and patients who underwent TJA from September 2017 to February 2018 whose pain was managed by the scheduled multimodal pain management order set. For patients in the MMA group, there was a significant difference in pain control on postoperative day 1 (p = .04) in addition to decreased hospital length of stay (LOS) (p = .0001). Opioid consumption in the MMA group was lower compared to the traditional supplemental "as-needed" opioid-based pain management cohort. Implementation of the MMA regimen at this institution led to improved postoperative pain control, reduced LOS, less consumption of opioids, antiemetic, and antipruritic medications in TJA patient population.

Technical Note: Break-even dose level for hypofractionated treatment schedules.

PurposeTo derive the isodose line R relative to the prescription dose below which irradiated normal tissue (NT) regions benefit from a hypofractionated schedule with an isoeffective dose to the tumor. To apply the formalism to clinical case examples.MethodsFrom the standard biologically effective dose (BED) equation based on the linear‐quadratic (LQ) model, the BED of an NT that receives a relative proportion r of the prescribed dose per fraction for a given α/β‐ratio of the tumor, (α/β)T, and NT, (α/β)NT, is derived for different treatment schedules while keeping the BED to the tumor constant. Based on this, the “break‐even” isodose line R is then derived. The BED of NT regions that receive doses below R decreases for more hypofractionated treatment schedules, and hence a lower risk for NT injury is predicted in these regions. To assess the impact of a linear behavior of BED for high doses per fraction (>6 Gy), we evaluated BED also using the LQ‐linear (LQ‐L) model.ResultsThe formalism provides the equations to derive the BED of an NT as function of dose per fraction for an isoeffective dose to the tumor and the corresponding break‐even isodose line R. For generic α/β‐ratios of (α/β)T = 10 Gy and (α/β)NT = 3 Gy and homogeneous dose in the target, R is 30%. R is doubling for stereotactic treatments for which tumor control correlates with the maximum dose of 100% instead of the encompassing isodose line of 50%. When using the LQ‐L model, the notion of a break‐even dose level R remains valid up to about 20 Gy per fraction for generic α/β‐ratios and DT=2(α/β).ConclusionsThe formalism may be used to estimate below which relative isodose line R there will be a differential sparing of NT when increasing hypofractionation. More generally, it allows to assess changes of the therapeutic index for sets of isoeffective treatment schedules at different relative dose levels compared to a reference schedule in a compact manner.

Joint Scheduling and Resource Allocation for Efficiency-Oriented Distributed Learning Over Vehicle Platooning Networks

The limited communication and computing resources, as well as the rising concerns about the privacy protection, bring significant challenges to the massive data training and analysis in vehicular networks. To address these challenges, in this paper a platoon-based distributed learning framework design for data learning is carried out, where the vacant computation resources of vehicle platooning networks are leveraged. In the proposed framework, a 2-phase Markovian stochastic process is utilized to depict the learning service heterogeneity for each participating vehicle. Meanwhile, we propose a joint scheduling and resource allocation scheme for efficiency-oriented distributed learning to maximize the learning accuracy subject to a given learning time constraint. The optimization problem is solved as follows. First, given the scheduled vehicles, the communication resource allocation is modeled as a minimum-maximum problem to minimize the learning delay of each learning round. Subsequently, an efficiency-oriented unbiased global aggregation policy is proposed to explore the convergence difference between partial scheduling and total scheduling. Considering the learning convergence and remaining time, an on-demand scheduling scheme is introduced to determine the number of scheduled vehicles. Finally, combining the learning efficiency of each vehicle with the scheduled number of vehicles, the scheduled vehicle set is selected. Simulations results show that the proposed scheduling policy can schedule the number of participating vehicles on demand based on the trade-off between learning performance and learning latency.

Emerging approaches for nonlinear parameter varying systems

Emerging approaches for nonlinear parameter varying systems

Research on Intelligent Scheduling and Monitoring Method of Workshop Logistics System

This paper proposes a complete set of workshop scheduling and monitoring methods for the current problems of the inability to intelligently schedule logistics tasks in the workshop and the difficulty in monitoring the workshop site. First, to realize the intelligent scheduling of workshop logistics tasks, this paper proposes an artificial bee colony learning (ABC-L) algorithm based on reinforcement learning (RL), and realizes the route optimization and adjustment of the scheduling scheme through three-dimensional simulation. Secondly, to track and monitor the logistics progress of the workshop in time, this paper builds the sensor network of the workshop and establishes the virtual-real mapping model of the workshop site. Finally, to deal with emergencies in the workshop, the intelligent decision of scheduling is realized through the knowledge base of the workshop environment and behavior decision. In addition, this article combines the above-mentioned scheduling, simulation, monitoring, and intelligent decision functions to develop an integrated software platform and test it.

The initialization of initial generating equipment maintenance schedules while a heuristic method is used

Currently, heuristic methods based on iterative changing of feasible solutions set provide a perspective tool for generation equipment maintenance scheduling in power systems. Wherein effectiveness of a heuristic method depends significantly on the initial set of possible schedules or in other words quality of the method initialization. In this case, a widely used methodology of building the initial array of solutions on the basis of pseudorandom uniform generation of control variables seems to be only palliative way to access the problem.&#x0D; This paper proposes alternative initialization procedure drawing on the example of generating units maintenance planning with heuristic differential evolution method. The principle of this method is to get initial set of solutions utilizing normal probability distribution to generate pseudorandom deviations from the suboptimal maintenance schedule which is to be preliminarily formed using directed search method. Following this approach allows to improve probabilistic characteristics of resultant maintenance schedule in particular to decrease median value of an objective function and its coefficient of variation, and to maximize probability to get the combination of units outage moments completely suiting operational constraints.

Heuristic fragmentation-aware scheduling for a multicluster time-sensitive passive optical LAN

With the development of Industry 4.0, optical networks are playing a key role in carrying large amounts of data traffic in industrial networks. Multicluster time-sensitive passive optical local area networks (POLs) use passive optical networks (PONs), which are important optical network segments, as local area networks (LANs); such networks can provide high-capacity real-time communications for large-scale industrial systems and have the potential to replace legacy LANs in industrial networks. To satisfy dynamic application requirements, POLs for future industrial networks need to be sufficiently flexible to adapt to frequent changes. Hence, this paper proposes a heuristic fragmentation-aware scheduling (FAS) method based on fast list scheduling for the scheduling of real-time traffic in multicluster time-sensitive POLs. The FAS algorithm selects a scheduling plan in accordance with a fragmentation calculation, schedules one time-triggered (TT) flow at a time from the pending scheduled TT flow set, and incrementally updates the scheduling table. The list processing mechanism of list scheduling is modified to improve scheduling performance, while the fast speed of list scheduling is inherited. Simulation results show that compared with a basic rate monotonic scheduling (RMS) list scheduler, FAS significantly increases the schedulable scale and schedulable load of the TT flow set while markedly reducing the TT communication overhead. With the help of FAS, multicluster time-sensitive POLs can support more critical tasks with higher network efficiency, allowing them to better cope with future dynamic and complex industrial application scenarios.

Hybrid Estimation of Distribution Algorithm for Solving Three-Stage Multiobjective Integrated Scheduling Problem

Aiming at reducing the total energy consumption of three stages processing-transportation-assembly in the assembly manufacturing industry, a three-stage multiobjective integrated scheduling problem with job batch transportation considering the energy consumption (3sMISP_JBTEC) is proposed, and a comprehensive energy consumption model of multistage of 3sMISP_JBTEC with an improved turn off/on strategy in the processing stage and considering speed in the transportation stage is formulated. Then, a hybrid estimation of distribution algorithm with variable neighborhood search (HEDA_VNS) is developed to solve the scheduling problem. In the HEDA_VNS, the reasonable coding/decoding rules and speed scheduling scheme (SSS) are designed. Moreover, two local search strategies are designed to further enhance the performance of HEDA_VNS. Among them, three types of neighborhood search strategies are devised in Local Search I to improve the search efficiency while retaining the structure of the original high-quality solution. A variable neighborhood hybrid operation based on the speed scheduling set is designed in Local Search II to further improve the quality of the solution while balancing the optimization goals. Finally, simulations and comparisons show the efficiency of the proposed HEDA_VNS.

On Optimistic and Pessimistic Bilevel Optimization Models for Demand Response Management

This paper investigates bilevel optimization models for demand response management, and highlights the often overlooked consequences of a common modeling assumption in the field. That is, the overwhelming majority of existing research deals with the so-called optimistic variant of the problem where, in case of multiple optimal consumption schedules for a consumer (follower), the consumer chooses an optimal schedule that is the most favorable for the electricity retailer (leader). However, this assumption is usually illegitimate in practice; as a result, consumers may easily deviate from their expected behavior during realization, and the retailer suffers significant losses. One way out is to solve the pessimistic variant instead, where the retailer prepares for the least favorable optimal responses from the consumers. The main contribution of the paper is an exact procedure for solving the pessimistic variant of the problem. First, key properties of optimal solutions are formally proven and efficiently solvable special cases are identified. Then, a detailed investigation of the optimistic and pessimistic variants of the problem is presented. It is demonstrated that the set of optimal consumption schedules typically contains various responses that are equal for the follower, but bring radically different profits for the leader. The main procedure for solving the pessimistic variant reduces the problem to solving the optimistic variant with slightly perturbed problem data. A numerical case study shows that the optimistic solution may perform poorly in practice, while the pessimistic solution gives very close to the highest profit that can be achieved theoretically. To the best of the authors’ knowledge, this paper is the first to propose an exact solution approach for the pessimistic variant of the problem.

Optimal algorithms for scheduling under time-of-use tariffs

We consider a natural generalization of classical scheduling problems to a setting in which using a time unit for processing a job causes some time-dependent cost, the time-of-use tariff, which must be paid in addition to the standard scheduling cost. We focus on preemptive single-machine scheduling and two classical scheduling cost functions, the sum of (weighted) completion times and the maximum completion time, that is, the makespan. While these problems are easy to solve in the classical scheduling setting, they are considerably more complex when time-of-use tariffs must be considered. We contribute optimal polynomial-time algorithms and best possible approximation algorithms. For the problem of minimizing the total (weighted) completion time on a single machine, we present a polynomial-time algorithm that computes for any given sequence of jobs an optimal schedule, i.e., the optimal set of time slots to be used for preemptively scheduling jobs according to the given sequence. This result is based on dynamic programming using a subtle analysis of the structure of optimal solutions and a potential function argument. With this algorithm, we solve the unweighted problem optimally in polynomial time. For the more general problem, in which jobs may have individual weights, we develop a polynomial-time approximation scheme (PTAS) based on a dual scheduling approach introduced for scheduling on a machine of varying speed. As the weighted problem is strongly NP-hard, our PTAS is the best possible approximation we can hope for. For preemptive scheduling to minimize the makespan, we show that there is a comparably simple optimal algorithm with polynomial running time. This is true even in a certain generalized model with unrelated machines.

Set-based guaranteed active fault diagnosis for LPV systems with unknown bounded uncertainties

This paper focuses on designing, via the minimization of a suitable cost function, an input sequence to guarantee active fault diagnosis (AFD) of discrete-time linear parameter-varying systems regardless of how the scheduling variables change in the scheduling set. Convex polyhedrons are used to characterize the sets of system uncertainties such that the designed input sequence leads to outputs which are consistent with a unique fault mode or healthy mode. Considering the smoothness and energy of the input sequence simultaneously, the optimal input sequence is obtained by solving a family of linear constrained quadratic programming (LCQP) problems in a brute-force way, which will lead to inefficiency when the number of facets of the interest region becomes large. In order to handle this problem, by analyzing a geometric property of the objective function, the original optimization problem is shown to be equivalent to the minimization of the radius of a hypersphere containing the origin based on a simple linear mapping, which is much more efficient than the brute force-based resolution. At the end of this paper, a numerical example and a vehicle model are used to illustrate the effectiveness of the proposed method.

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis.

Analysis of biomarkers in peripheral blood is becoming increasingly important in clinical trials to establish proof of mechanism to evaluate effects of treatment, and help guide dose and schedule setting of therapeutics. From a single blood draw, peripheral blood mononuclear cells can be isolated and processed to analyze and quantify protein markers, and plasma samples can be used for the analysis of circulating tumor DNA, cytokines, and plasma metabolomics. Longitudinal samples from a treatment provide information on the evolution of a given protein marker, the mutational status and immunological landscape of the patient. This can only be achieved if the processing of the peripheral blood is carried out effectively in clinical sites and samples are properly preserved from the bedside to bench. Here, we present an optimized general-purpose protocol that can be implemented at clinical sites for obtaining PBMC pellets and plasma samples in multi-center clinical trials, that will enable clinical professionals in hospital laboratories to successfully provide high quality samples, regardless of their level of technical expertise. Alternative protocol variations are also presented that are optimized for more specific downstream analytical methods. We apply this protocol for studying protein biomarkers against DNA damage response (DDR) on X-ray irradiated blood to demonstrate the suitability of the approach in oncology settings where DDR drugs and/or radiotherapy have been practiced as well as in preclinical stages where mechanistic hypothesis testing is required.

Cyclical Nurse Scheduling in Shah Alam Hospital Using Goal Programming

A shift work schedule is extremely important to obtain the optimum result of work allocation since it involves 24 hours of continuous services. Every nurse could not avoid shift work schedule since their services are very important towards the patients in the hospital. The major objective of the study is to propose a cyclical nurse scheduling in the Coronary Care Unit (CCU) at Shah Alam hospital using Goal Programming. It is to help the head nurse to spend less effort on building new schedules periodically and increase the satisfaction among nurses by providing fairness towards their schedules. There were nine hard constraints and three soft constraints for the nurse scheduling model. The results presented the optimal solution where all goals were achieved thus, it provided a fair schedule for 15 nurses in 15 days. Then, the schedule pattern was rotated among nurses based on the 15 schedules set in 225 days. The first schedule set will be used by the nurse for the first 15 days, then will be continuously rotated for another 15 days until all the nurses reached and experienced every 15 sets of the schedule. The schedule was generated using LINGO software which it took a short time to solve the problem.

A Spatial–Temporal Network Perspective for the Propagation Dynamics of Air Traffic Delays

Intractable delays occur in air traffic due to the imbalance between ever-increasing air traffic demand and limited airspace capacity. As air traffic is associated with complex air transport systems, delays can be magnified and propagated throughout these systems, resulting in the emergent behavior known as delay propagation. An understanding of delay propagation dynamics is pertinent to modern air traffic management. In this work, we present a complex network perspective of delay propagation dynamics. Specifically, we model air traffic scenarios using spatial–temporal networks with airports as the nodes. To establish the dynamic edges between the nodes, we develop a delay propagation method and apply it to a given set of air traffic schedules. Based on the constructed spatial-temporal networks, we suggest three metrics—magnitude, severity, and speed—to gauge delay propagation dynamics. To validate the effectiveness of the proposed method, we carry out case studies on domestic flights in the Southeastern Asia region (SAR) and the United States. Experiments demonstrate that the propagation magnitude in terms of the number of flights affected by delay propagation and the amount of propagated delays for the US traffic are respectively five and ten times those of the SAR. Experiments further reveal that the propagation speed for US traffic is eight times faster than that of the SAR. The delay propagation dynamics reveal that about six hub airports in the SAR have significant propagated delays, while the situation in the United States is considerably worse, with a corresponding number of around 16. This work provides a potent tool for tracing the evolution of air traffic delays.

Flexible decision support tool for dynamic single machine scheduling problems

ABSTRACT This paper addresses dynamic single machine scheduling problems. It proposes a flexible decision support tool based on a non-preemptive version of the previously-established overlapping load adjustment approach. The proposed tool adopts a two-phase proactive-reactive tactic where the first phase only characterizes the set of feasible schedules. In the second reactive phase, however, actual short-term scheduling takes place through a user-induced dynamic-event module, which progressively builds the schedule as the horizon rolls forward while ensuring the feasibility of the obtained schedule at all times. The performance of the tool is assessed using 500 randomly generated test instances of various sizes. The obtained results illustrate the flexibility of the tool as measured by the high number of potential schedule alternatives. Furthermore, the results highlight the tool’s capability of yielding a large number of high-quality schedules with respect to three performance measures.