Multiple Time Constraints Research Articles

Using interoperable nursing care data to improve outcomes for multiple traumas patients with Impaired Physical Mobility.

To demonstrate how interoperable nursing care data can be used by nurses to create a more holistic understanding of the healthcare needs of multiple traumas patients with Impaired Physical Mobility. By proposing and validating linkages for the nursing diagnosis of Impaired Physical Mobility in multiple trauma patients by mapping to the Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC) equivalent terms using free-text nursing documentation. A descriptive cross-sectional design, combining quantitative analysis of interoperable data sets and the Kappa's coefficient score with qualitative insights from cross-mapping methodology and nursing professionals' consensus. Cross-mapping methodology was conducted in a Brazilian Level 1 Trauma Center using de-identified records of adult patients with a confirmed medical diagnosis of multiple traumas and Impaired Physical Mobility (a nursing diagnosis). The hospital nursing free-text records were mapped to NANDA-I, NIC, NOC and NNN linkages were identified. The data records were retrieved for admissions from September to October 2020 and involved medical and nursing records. Three expert nurses evaluated the cross-mapping and linkage results using a 4-point Likert-type scale and Kappa's coefficient. The de-identified records of 44 patients were evaluated and then were mapped to three NOCs related to nurses care planning: (0001) Endurance; (0204) Immobility Consequences: Physiological, and (0208) Mobility and 13 interventions and 32 interrelated activities: (6486) Environmental Management: Safety; (0840) Positioning; (3200) Aspiration Precautions; (1400) Pain Management; (0940) Traction/Immobilization Care; (3540) Pressure Ulcer Prevention; (3584) Skincare: Topical Treatment; (1100) Nutrition Management; (3660) Wound Care; (1804) Self-Care Assistance: Toileting; (1801) Self-Care Assistance: Bathing/Hygiene; (4130) Fluid Monitoring; and (4200) Intravenous Therapy. The final version of the constructed NNN Linkages identified 37 NOCs and 41 NICs. These valid NNN linkages for patients with multiple traumas can serve as a valuable resource that enables nurses, who face multiple time constraints, to make informed decisions efficiently. This approach of using evidence-based linkages like the one developed in this research holds high potential for improving patient's safety and outcomes. In this study, there was no direct involvement of patients, service users, caregivers or public members in the design, conduct, analysis and interpretation of data or preparation of the manuscript. The study focused solely on analysing existing de-identified medical and nursing records to propose and validate linkages for nursing diagnoses.

Towards time-constrained task allocation in semi-opportunistic mobile crowdsensing

Mobile crowdsensing (MCS) is an emerging sensing paradigm that leverages mobile users carrying smart devices to perform sensing tasks. On the one hand, as the sensing scenarios become more realistic and complex, the impact of time attributes on task allocation is significantly increased. However, most of the existing works only treat time attributes as supplementary constraints and ignore the existence of multiple time constraints. On the other hand, considering the exploration and application of emerging models, making MCS more adaptive and diversified also becomes a research focus. In this paper, we propose the Multiple Time constrained Task Allocation problem in Semi-Opportunistic mobile crowdsensing (SO-MTTA), with the goal of maximizing the sensing value obtained by the platform. We prove that the SO-MTTA problem is NP-hard and design a Multi-Layer Genetic Algorithm (MLGA), which uses a low space complexity encoding method for three layers: User-path-task, and adds a conflict elimination operation to correct possible conflicts of constraints. Finally, we conduct experiments on both synthetic and real-world datasets to successfully verify the effectiveness of our proposed algorithm.

An improved ant colony optimization for solving the flexible job shop scheduling problem with multiple time constraints.

The flexible job shop scheduling problem is important in many research fields such as production management and combinatorial optimization, and it contains sub-problems of machine assignment and operation sequencing. In this paper, we study a many-objective FJSP (MaOFJSP) with multiple time constraints on setup time, transportation time and delivery time, with the objective of minimizing the maximum completion time, the total workload, the workload of critical machine and penalties of earliness/tardiness. Based on the given problem, an improved ant colony optimization is proposed to solve the problem. A distributed coding approach is proposed by the problem features. Three initialization methods are proposed to improve the quality and diversity of the initial solutions. The front end of the algorithm is designed to iteratively update the machine assignment to search for different neighborhoods. Then the improved ant colony optimization is used for local search of the neighborhood. For the searched scheduling set the entropy weight method and non-dominated sorting are used for filtering. Then mutation and closeness operations are proposed to improve the diversity of the solutions. The algorithm was evaluated through experiments based on 28 benchmark instances. The experimental results show that the algorithm can effectively solve the MaOFJSP problem.

Project Resource Input Optimization Problem with Combined Time Constraints Based on Node Network Diagram and Constraint Programming

AbstractThe traditional resource-constrained project scheduling problem makes the amounts of resource input fixed and ignores the joint effect of multiple time constraints, which may lead to the fa...

Resource-Constrained Project Scheduling Problem under Multiple Time Constraints

This paper introduces a new practical scheduling problem called the resource-constrained project scheduling problem under multiple time constraints, which involves a duration constraint of activity, temporal constraint, and resource calendar constraint. The duration constraint of the activity exists widely in real-life projects, and it is first proposed as a resource-constrained project scheduling problem in this paper. We prove the defects of the traditional temporal constraint and improve it. The new problem combines three types of time constraints for the first time, which makes it closer to the actual scheduling problem. We developed a constraint programming optimization model for the new problem and used the IBM ILOG CPLEX-CP version 12.9.0 optimizer to solve it. Computational experiments are carried out to show that the CP optimizer is fast and provides a near-optimum solution to the new problem for projects with hundreds of activities within minutes compared to other metaheuristic methods. The results reported in this paper can be used as a benchmark for other researchers to compare and improve. The new problem contributes to developing a practical decision support system for resolving real-life constraints in projects.

Runtime Verification of Business Cloud Workflow Temporal Conformance

Business cloud workflows are often designed with multiple time constraints for timely response to business requests. To ensure on-time completion of workflow instances, workflow temporal conformance state needs to be constantly monitored and verified at runtime. Considering the fact that there are a large number of workflow instances running in a parallel fashion in many business scenarios, conventional verification approaches for time-related properties using such as temporal logic or timed Petri nets are not feasible due to the limitation of low efficiency at runtime. To address this issue, we propose a new approach to automated runtime verification of temporal conformance for parallel workflow instances in a cloud environment. Instead of using response time to verify temporal conformance of every single workflow as in conventional strategies, workflow throughput is employed as the performance measurement to efficiently monitor a large number of parallel workflow instances. On this basis we present a novel conformance verification strategy. This strategy considers the effect of time delay propagation in the cloud workflow systems to accurately verify workflow runtime temporal conformance. Our verification strategy is implemented in a prototype cloud workflow system and the evaluation results show that it outperforms the state-of-the-art workflow temporal verification strategy.

A data-driven approach for trajectory-based aircraft operation with controlled time of arrival and along-track wind effects

Trajectory-based aircraft operation and control is one of the hot issues in air traffic management. However, the accurate mechanism modeling of aircraft is tough work, and the operation data have not been effectively utilized in many studies. So, in this work, we apply the model-free adaptive iterative learning control method to address the time-of-arrival control problem in trajectory-based aircraft operation. This problem is first formulated into a trajectory tracking problem with along-track wind disturbance. Through rigorous analysis, it is shown that this method, combined with point-to-point iterative learning control (ILC) strategy, can effectively deal with the arrival time control problem with multiple time constraints. Then, the terminal ILC strategy is applied, aiming to resolve the same problem with a time constraint at the end point. Compared with the PID (Proportional Integral Derivative) type ILC, the proposed method improves control performance by 11.15% in root mean square of tracking error and 9.32% in integral time absolute error. The sensitivity and flexibility of the data-driven approach is further verified through numerical simulations.

An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints

Abstract The flexible job shop scheduling problem is a very important problem in factory scheduling. Most of existing researches only consider the processing time of each operation, however, jobs often require transporting to another machine for the next operation while machines often require setup to process the next job. In addition, the times associated with these steps increase the complexity of this problem. In this paper, the flexible job scheduling problem is solved that incorporates not only processing time but setup time and transportation time as well. After presenting the problem, an improved genetic algorithm is proposed to solve the problem, with the aim of minimizing the makespan time, minimizing total setup time, and minimizing total transportation time. In the improved genetic algorithm, initial solutions are generated through three different methods to improve the quality and diversity of the initial population. Then, a crossover method with artificial pairing is adopted to preserve good solutions and improve poor solutions effectively. In addition, an adaptive weight mechanism is applied to alter mutation probability and search ranges dynamically for individuals in the population. By a series of experiments with standard datasets, we demonstrate the validity of our approach and its strong performance.

Robust Optimization of Multiple Timing Constraints

Modern field-programmable gate array (FPGA) circuit designs often contain multiple clocks and complex timing constraints, and achieving these constraints requires timing optimization at all stages of the computer-aided design (CAD) flow. To our knowledge, no prior published work has either described or quantitatively evaluated how to compute connection timing criticalities for circuits with multiple timing constraints in order to best guide CAD optimization algorithms. While single-clock techniques have a simple extension to multi-clock circuits, this formulation is not robust for circuits with multiple constraints of different magnitudes, or impossible constraints. We describe a robust method of timing optimization for circuits with multiple timing constraints, implemented in the open-source versatile place and route FPGA CAD tool. Our formulation can optimize multiple constraints well, even in the case where some constraints are impossible, and achieves over 20% greater clock speed with aggressive constraints than a straight-forward extension of single-clock work.

A soft dynamic programming approach for on-line aircraft 4D-trajectory optimization

During the last decade, Flight Management Systems (FMS) have been introduced on board modern aircraft to reduce operational costs, and flight profiles have been optimized in order to minimize fuel and time-related costs. Today, the integration of next generation FMS in the future air traffic management environment requires the development of new capabilities and among them, on-line real time 4D-trajectory generation has become mandatory. This paper presents a method based on dynamic programming (DP) to generate optimal 4D-trajectories in the presence of multiple time constraints. Soft computing techniques are introduced to improve the DP oriented optimization process by on-line selection of the search space and by cutting down computation time in repetitive performance evaluations.

RTsynchronizer

We argue that the specification of an object's functional behavior and the timing constraints imposed on it may be separated. Specifically, we describe RTsynchronizer, a high-level programming language construct for specifying real-time constraints between objects in a distributed concurrent system. During program execution, RTsynchronizers affect the scheduling of distributed objects to enforce real-time relations between events. Objects in our system are defined in terms of the actor model extended with timing assumptions. Separation of the functional behaviors of actors and the timing constraints on patterns of actor invocation provides at least three important advantages. First, it simplifies code development by separating design concerns. Second, multiple timing constraints can be independently specified and composed. And finally, a specification of timing constraints can be reused even if the representation of the functional behavior of actors has changed, and conversely.A number of examples are given to illustrate the use of RTsynchronizers. These examples illustrate how real-time constraints for periodic events, simultaneous events, exception handling, and producer-consumer may be specified.