Schedule Quality Research Articles

Comprehensive evaluation of train schedule on urban rail network based on multilayer grey relational analysis

The train schedule is the basis and foundation for the staff of all departments of urban rail transit to cooperate with the train operation. The quality of train schedule directly affects the efficiency and safety of the transportation organization. The compilation of train schedule should consider not only the train movement in the operation level, but also the passenger flow in the passenger service level. Therefore, this paper firstly uses the expert consultation method to filter the relevant indicators of the train schedule, and constructs the comprehensive evaluation index from five aspects: basic operation index, service quality, robustness, adjustability, and operational effect. Secondly, the multi-level evaluation method is constructed by the tomographic analysis method, and the grey comprehensive evaluation model is then established. The weights of each level of the indicators and the grey correlation coefficient are calculated to evaluate the quality of train schedule, and to select optimal scheme. Finally, Shenzhen Metro is taken as an example for case study. The result shows that the comprehensive quality evaluation of the train schedule of line 11 is good, and that of line 1, line 7 and line 9 are general. Case study reveals that the proposed method can reasonably and effectively evaluate the quality of train schedules.

Final Products: a regional integrated output assessment in Public Health services, 2017-19 Italy

Abstract Issue/problem The classification of Public Health services (PHS) and related healthcare information systems (HIS) in Friuli Venezia Giulia, Italy, are complex and non-homogeneous. Since 2017 the Region has been realigning HIS according to the new Final Products (FP) System to classify PHS. The aim is to implement an integrated FP-based HIS, easing data transmission and improving the quality of planning, scheduling and expense reporting activities. Description of the problem From June 2017 to October 2018 68 Public Health professionals (PHP) from 10 Public Health areas outlined 102 PF from HIS through flowcharts and technical dossiers. Afterwards (10/5th-10/29th 2018) they assessed HIS through a questionnaire of 24 items. Statistical analysis was performed with Fisher’s Exact Test, stratifying 4 categories (physician, veterinarian, prevention technician, other professional). In June-July 2019, through a matrix-based assessment, PHP of regional PHS were invited to weight each FP activity on the basis of involved personnel and possible required transfer (valorisation phase). Results The questionnaire got 45/68 responses (66%). Among responders, 27% reported a HIS for their PHS not being available at all; 65% of responders agreed on the usefulness of an integrated regional HIS for PHS. Fisher’s Test showed significant differences in HIS features according to profession, as interoperability with national/regional databases (p = 0.0144), mobile usability for external activities (p = 0.0302), real correspondence between monitored data and performed activities (p = 0.0215). Valorisation was performed by 445/557 PHP (80%). Lessons PHS currently often deal with functional flaws or inappropriateness of HIS when compared to real activities. Valorisation data will be submitted to the attention of policymakers in October 2019 in order to apply an integrated classification of PHS considering both effectiveness and generation of value of activities performed in Public Health. Key messages Healthcare is a multilayered product and measuring healthcare outputs requires managerial skills and tools, to assess need of resources and to plan activities. The regional Final Products project defines a uniform classification of Public Health services and implements a standardised accounting system for activities performed in Public Health.

On scheduling quality of precise resource measure sets which consist of tasks of circular type in GRID systems

To serve parallel tasks of multiple users of Grid systems require the development of efficient methods for allocating processors and time resources. Optimal resource management is practically unrealizable due to the exponential complexity. In the situation, when the Grid-system is modeled by the resource quadrant and the user’s task is represented by the resource rectangle, heuristic polynomial algorithms are proposed with the quality assessment of scheduling by a Non-Euclidean heuristic measure. Using the example of arrays with an exact resource measure and induced by partridge linings, the adaptation of level polynomial algorithms is investigated, and a comparative analysis of the proposed algorithms is carried out.

Proactive and reactive strategies for football league timetabling

Due to unforeseen events (e.g. bad weather conditions), association football league schedules are not necessarily played as they were announced in the beginning of the season. This paper analyses the impact of uncertainty on the quality of football league schedules by examining fifteen seasons of ten major European football leagues. We describe several quality measures, related to breaks, the fairness of the ranking, and cancelled matches. The empirical study reveals that matches that were rescheduled to another date have a profound impact on the quality of the resulting schedule, indicating that football schedules in Europe deal poorly with uncertainty. Moreover, we present several proactive and reactive approaches in order to mitigate this problem. The former determine where to insert so-called catch-up rounds as buffers in the schedule, while the latter reschedule matches to these catch-up rounds when uncertain events occur. We evaluate combinations of proactive and reactive approaches, and provide recommendations to practitioners (e.g. four catch-up rounds usually suffice, and immediate irrevocable rescheduling is not beneficial).

104 Exploring the Utility of the VERA Framework to Improve Communication between Nursing Students and People with Dementia in Long-term Care

Abstract Background Developed by Blackhall et al. (2011), the Validation, Emotion, Reassure, Activity (VERA) communication framework seeks to provide student nurses with a guide to provide person-centred care to people living with dementia. The framework aims to enable the caregiver to communicate with the person living with dementia in a respectful manner, and accept that there is both value and meaning to the person’s speech and behaviour. This paper presents the findings from a small pilot control pre-post study which aimed to explore the impact of the VERA framework to promote person-centred communication between student nurses and older people with dementia in long term care. Methods A control group of six students, received standard communication training and ten students received additional communication training using the VERA framework. Data was collected using the non-participant observational tool Quality of Interactions Schedule (QUIS) and three self-assessment questionnaires to assess student nurses’ confidence, dementia knowledge and communication skills. Results The results of this study found that those students who received the additional VERA communication training were observed to have a significantly (Mann-Whitney U test; p = <0.05) reduced number of negative interactions with people with dementia than those students who had received standard communication training alone. In addition, student nurses trained in the VERA framework showed a significant increase (Mann-Whitney U test; p = <0.05) in their confidence to care for people with dementia than those who had received standard communication skills training. Conclusion The study finding need to be interpreted with caution given the small sample size and the pilot nature of the study. Nevertheless, these findings indicate that the VERA framework has the potential to improve (1) person-centred communication between student nurses and older people with dementia and (2) students’ confidence to care for people with dementia.

Recursive heuristic scheduling method for multi-crop plant factory with solar panel roof

Plant factory can provide stable crop cultivation environment, shorten cultivation duration, and deliver better produce quality by controlling temperature, humidity, lighting, nutrient supply, and other cultivating factors. Due to the high cost of operation, how to select crops and plan the cultivation schedule to enhance profitability is an important issue. In this research, the plant factory scheduling problem was formulated as a mixed integer linear programming (MILP) problem. The objective function is to seek the maximum revenue by considering several practical operating conditions such as (1) crop market value per unit and time, (2) crop adjacency issue, (3) cleaning and maintenance, and (4) sunlight blocking by solar panel on the roof. Although the scheduling problem can be solved by the optimization solver, it presents challenges of rack-level modification when applying the optimization method on the field. Therefore, this study further developed a heuristic algorithm, named Heuristic Plant Factory Scheduler (HPFS), which applies a recursive technique to schedule crops rack by rack. This approach provides ease of implementation and modification. The experimental results show that HPFS is able to accelerate the computational performance with acceptable scheduling quality when the problem space is large.

A decomposition-based heuristic for large employee scheduling problems with inter-department transfers

We consider a personalized employee scheduling problem with characteristics present in retail stores consisting of multiple departments. In the setting under study, each department generally covers its demand in employees over the planning horizon of a week by assigning shifts to its own staff. However, the employees can also be transferred to other departments under certain conditions for executing entire shifts or parts of shifts there. The transfer feature enables to improve the overall schedule quality considerably when compared to the nontransfer case. Given the complexity of the problem, we propose a three-phase decomposition-based heuristic. In the first phase, we consider each department separately and solve a simplified version of the mono-department scheduling problems. From the obtained solutions, we deduce inter-department shifts that could potentially reduce the overall cost. This is examined in the second phase by resolving the scheduling problem of the first phase where the deduced inter-department shifts are included. In this phase, however, we decompose the scheduling problem by time, looking at each day separately. From the obtained schedules, we then devise inter-department demand curves, which specify the number of transfers between departments over time. In the third phase, we decompose the initial scheduling problem into mono-department problems using these inter-department demand curves. Consequently, our approach makes it possible to solve mono-department optimization problems to get an overall schedule while still benefiting from the employee transfer feature. In all three phases, the scheduling problems are formulated as mixed-integer linear programs. We show through extensive computational experiments on instances with up to 25 departments and 1000 employees that the method provides high-quality solutions within reasonable computation times.

The Quality of Interactions Between Staff and Residents With Cognitive Impairment in Nursing Homes.

Positive and effective staff-resident interactions are imperative to adequately assess and meet the needs of cognitively impaired residents in nursing homes and optimize their quality of life. The purpose of this study was to quantify, describe, and analyze the interaction between staff and cognitively impaired residents in nursing homes, using the Quality of Interaction Schedule (QuIS). This descriptive analysis utilized baseline data from the first 2 cohorts in a randomized clinical trial including 341 residents from 35 nursing homes. Five hundred fifty-six staff-resident interactions were evaluated; majority were positive (n = 466, 83.8%) and the remaining were either neutral (n = 60, 10.8%) or negative (n = 30, 5.4%). The quality of interactions varied by interaction location, interpersonal distance, and resident participation. Future research should focus on decreasing the negative/neutral interactions and explore staff characteristics (eg, gender, level of experience) and facility factors (eg, size, ownership) that might influence the quality of interactions.

Optimal approach to game scheduling of multiple round-robin tournament: Korea professional baseball league in focus

This study evaluates the quality of the current schedule of a professional baseball league based on the total travel distance of teams, match fairness, and attendance fairness. Alternatives that prioritize the minimization of the total travel distance and satisfy other factors are presented. Moreover, minimization results are compared with the current schedule of the Korea Baseball Organization League, whose requirements differ in certain aspects from those of other baseball leagues and sports. Although numerous studies have examined the sports scheduling problem, this study develops a new integer programming model for baseball leagues, proposes a new problem-specific heuristic, and applies this to the Korea Baseball Organization League, which is the focus of this study. Using quantitative methodologies to analyze the source of match generation and adjustment problems, our research demonstrates that there is ample room for improvement in baseball game scheduling in terms of enhancing the aforementioned factors.

Two-level task scheduling with multi-objectives in geo-distributed and large-scale SaaS cloud

With the exploding of data-intensive web applications and requests (tasks), geo-distributed and large-scale data centers (DCs) are widely deployed in Software as a Service (SaaS) cloud, but server failures continue to grow at the same time. In this context, task scheduling problems become more intricate and both scheduling quality and scheduling speed raise further concerns. In this paper, we first propose a virtualized & monitoring SaaS model with predictive maintenance to minimize the costs of fault tolerance. Then with the monitored and predicted available states of servers, we focus on dynamic real-time task scheduling in geo-distributed and large-scale DCs with heterogeneous servers. Multiple objectives, including the long-term performance benefits, energy and communication costs, are taken into consideration in order to improve scheduling quality. For inter-DC and intra-DC task scheduling, two dynamic programming problems are formulated respectively, but there exists the problem that both state and action spaces are too large to be solved by simple iterations. To address this issue, we introduce the idea of reinforcement learning theory into solving traditional stochastic dynamic programming problems in the large-scale SaaS cloud, and put forward a cascaded two-level (inter-DC and intra-DC level) approximate dynamic programming (ADP) task-scheduling algorithm. The computation complexity can be significantly reduced and scheduling speed can be greatly improved. Finally, we conduct experiments with both random simulation data and Google cloud trace-logs. QoS evaluations and comparisons demonstrate that two ADP algorithms can work cooperatively, and our two-level ADP algorithm is more effective under large quantity of bursty requests.

Hospital nurse staffing and staff–patient interactions: an observational study

BackgroundExisting evidence indicates that reducing nurse staffing and/or skill mix adversely affects care quality. Nursing shortages may lead managers to dilute nursing team skill mix, substituting assistant personnel for registered...

Applying queue theory for modeling of cloud computing: A systematic review

SummaryThe cloud computing paradigm is an important service in the Internet for sharing and providing resources in a cost‐efficient way. Modeling of a cloud system is not an easy task because of the complexity and large scale of such systems. Cloud reliability could be improved by modeling the various aspects of cloud systems, including scheduling, service time, wait time, and hardware and software failures. The aim of this study is to survey research studies done on the modeling of cloud computing using the queuing system in order to identify where more emphasis should be placed in both current and future research directions. This paper follows the goal by investigating the articles published between 2008 and January 2017 in journals and conferences. A systematic mapping study combined with a systematic literature review was performed to find the related literature, and 71 articles were selected as primary studies that were classified in relation to the focus, research type, and contribution type. We classified the modeling techniques of cloud computing using the queuing theory in seven categories based on their focus area: (1) performance, (2) quality of service, (3) workflow scheduling, (4) energy savings, (5) resource management, (6) priority‐based servicing, and (7) reliability. A majority of the primary articles focus on performance (37%), 15% of them focus on resource management, 14% of them focus on quality of service, 13% of them focus on workflow scheduling, 13% of them focus on energy savings, 4% of them focus on priority‐based servicing for requests, and 4% of them focus on reliability. This work summarizes and classifies the research efforts conducted on applying queue theory for modeling of cloud computing (AQTMCC), providing a good starting point for further research in this area.

Limiting the memory footprint when dynamically scheduling DAGs on shared-memory platforms

Scientific workflows are frequently modeled as Directed Acyclic Graphs (DAGs) of tasks, which represent computational modules and their dependences in the form of data produced by a task and used by another one. This formulation allows the use of runtime systems which dynamically allocate tasks onto the resources of increasingly complex computing platforms. However, for some workflows, such a dynamic schedule may run out of memory by processing too many tasks simultaneously. This paper focuses on the problem of transforming such a DAG to prevent memory shortage, and concentrates on shared memory platforms. We first propose a simple model of DAGs which is expressive enough to emulate complex memory behaviors. We then exhibit a polynomial-time algorithm that computes the maximum peak memory of a DAG, that is, the maximum memory needed by any parallel schedule. We consider the problem of reducing this maximum peak memory to make it smaller than a given bound. Our solution consists in adding new fictitious edges, while trying to minimize the critical path of the graph. After proving that this problem is NP-complete, we provide an ILP solution as well as several heuristic strategies that are thoroughly compared by simulation on synthetic DAGs modeling actual computational workflows. We show that on most instances we are able to decrease the maximum peak memory at the cost of a small increase in the critical path, thus with little impact on the quality of the final parallel schedule.

Schedule Quality Assessment by Utilization of Working Resources

Schedule optimization is closely associated with the utilization of resources. Basing on Adamiecki’s Law of Harmony, it is proposed that the schedule quality should be assessed by the effects of resources’ downtime. According to authors’ approach, scheduling consists in allocation of resources’ work. All available resources generate total availability of work. Schedule quality assessment should be made by evaluating under-allocated work. It is a difference between total work available and work that is actually allocated as a result of the analysed schedule. Therefore, under-allocated work stands for resources’ underutilization. Under-allocated work should be cost-evaluated over time with regard to particular resources or whole groups of resources (i.e. particular contractor’s resources – if construction is performed by several contractors). The goal of the schedule optimization is to minimize cost of under-allocated work, especially in short-term planning horizon. The proposed method of harmonization assessment can be used in order to assess and compare any schedules, even those planned by independent schedulers for the same scope of works.

Evaluation of the Maths Arcade initiative at five U.K. universities

The Maths Arcade is a regular, optional drop-in session to play strategy games and puzzles, taking place at a range of U.K. universities. The main aims are to support students, create a maths-themed staff-student community and develop mathematical thinking through strategy game-play. At some universities, the Maths Arcade has additional aims, including motivating curricular work, linking to peer assisted learning and promoting cross-disciplinary interaction.The games used are strategy games with simple rules which have no explicit link to mathematics but appeal to logical thinking. The games and activities typically used at a Maths Arcade are described and an example of a deep investigation is given as a case study. An evaluation of the Maths Arcade at five U.K. universities was completed by distributing a questionnaire to attenders and non-attenders. Students who attend more sessions were more likely to report making friends at the Arcade, and a majority of students said that staff presence is helpful and they would like staff to attend more often, results which support the aim of creating a mathematical community. Students report liking the games, and those who attend more often are more likely to prefer games that are more open to analysis, supporting the aim to develop mathematical thinking through analysis of gameplay. Negative feedback focused mostly on practicalities, including quality of advertising and scheduling of Maths Arcade sessions.

Search for Drugs Used in Hospitals to Treat Stomatitis.

Stomatitis is an inflammatory disease of the oral mucosa, often accompanied by pain. Usually it is represented by aphthous stomatitis, for which treatment steroid ointment is commonly used. However, in the cases of refractory or recurrent stomatitis, traditional herbal medicines have been used with favorable therapeutic effects. Chemotherapy, especially in the head and neck region, induces stomatitis at higher frequency, which directly affects the patient’s quality of life and treatment schedule. However, effective treatment for stomatitis has yet to be established. This article presents the clinical report of Kampo medicines on the stomatitis patients in the Nihon university, and then reviews the literature of traditional medicines for the treatment of stomatitis. Among eighteen Kampo medicines, Hangeshashinto has been the most popular for the treatment of stomatitis, due to its prominent anti-inflammatory activity. It was unexpected that clinical data of Hangeshashinto on stomatitis from Chinese hospital are not available. Kampo medicines have been most exclusively administered to elder person, as compared to pediatric population. Supplementation of alkaline plant extracts rich in lignin-carbohydrate complex may further extend the applicability of Kampo medicines to viral diseases.

Controlling understaffing with conditional Value-at-Risk constraint for an integrated nurse scheduling problem under patient demand uncertainty

Nursing workforce management is a challenging decision-making task in hospitals. The decisions are made across different timescales and levels from strategic long-term staffing budget to mid-term scheduling. These decisions are interconnected and impact each other, therefore are best taken by considering staffing and scheduling together. Moreover, this decision-making needs to be made in a stochastic setting to meet uncertain patient demand. A sufficient and cost-efficient staffing level with desirable schedule is essential to provide good working conditions for nurses and consequently good quality of care. On the other hand, understaffing can severely deteriorate the quality of care thus should be strictly controlled.To help with the decision making, based on our previous research we formulate in this paper an integrated nurse staffing and scheduling model under patient demand uncertainty into a two-stage stochastic programming model with an emphasis on understaffing risk control. Conditional Value-at-Risk (CVaR), a risk control measure primarily used in the financial domain, is integrated in the stochastic programming model to control understaffing risk. The IBM ILOG CPLEX solver is applied to solve the stochastic model. The model and solution approaches are tested using a case study in a real-world environment setting. We have evaluated the performance of the stochastic model and the benefit of CVaR in terms of impact on schedule quality.

A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

This paper aims at scheduling bag-of-tasks (BoT) applications under budget constraints on hybrid clouds for minimizing the makespan. To solve this NP-hard problem, we propose a novel firefly algorithm (NFA) in which the evaluation of a firefly consists of two steps: (1) mapping a firefly to a scheduling solution (a task sequence); (2) calculating the solution's objective (its corresponding makespan). In the first step, different from the well-known ranked-order value (ROV) rule, we propose a distance-based mapping operator that relies on the distance between a firefly and the brightest one to determine the mapping relationship between a firefly and a solution. We use a probability model in which solutions corresponding to fireflies closer to the brightest one would have higher probabilities to inherit tasks from the current best solution. In this manner, these solutions can inherit more “good genes” hidden inside the current best solution to evolve into more high-quality solutions. In the second step, we employ an effective heuristic to evaluate solution objectives. We further develop a composite heuristic to generate the initial best solution, providing the proposed NFA with a good start. We also establish a new movement scheme such that fireflies distant from the brightest one can explore a wide range in the search space, whereas fireflies nearby the brightest one can search in a small neighborhood. Experimental results show that, by employing the above-mentioned strategies, NFA outperforms the standard firefly algorithm and the existing best algorithm, in terms of scheduling effectiveness and computational efficiency. Specifically, the distance-based mapping operator is verified to be both more effective and more efficient than the ROV rule. The composite heuristic is capable of generating a good initial solution, leading to the high quality of the final schedule. The movement scheme can further reduce the makespan of BoT applications.

Requirements’ Complexity Ranking using Natural Language Processing and Complexity Class Correlation with Defect Severity

This paper addresses the risks to delivery schedule and product quality from non-periodic temporal detection of high severity defects in software projects. The non-periodicity in time and lack of a time boundary in detecting severe defects primarily stems from subjective scheduling of development and testing of product features. One solution is to objectively determine complexity and ranking of requirements to drive the project development and test sequence that uncovers high severity defects early in the life cycle phases. Requirements complexity is strongly correlated with defect severity as measurements show. Applying natural language processing, key words are identified in the given set of requirements, their weights measured to determine the complexity class distribution and ranking that drives the scheduling. The complexity and defect correlation-based sequencing mitigates the risks by discovery of high severity defects in a temporal saw tooth pattern providing the project team sufficient time to fix defects and mitigate the risks.

Online Scheduling: Understanding the Impact of Uncertainty

Abstract We present a framework to study quality of schedules obtained iteratively and online (real-time) in the presence of demand uncertainty Using this framework, we carry out a computational study, and make interesting observations. First, we find that uncertainty plays a less important role as a manufacturing facility is operated close to capacity. Second, the choice of the horizon for the online iterations, is dependent on the mean load, but independent of the accuracy of the forecasts. Finally, feedback, in the form of re-optimization, plays a very important role in mitigating the impact of uncertainty. Thus, through the analysis presented in this work, we gain insights that are applicable to all general rescheduling approaches.