Offering time windows to receivers of last-mile delivery is becoming a distinguishing factor. However, we see that in practice carriers have to create routes for their vehicles based on destination information, that is just being revealed when a parcel arrives in the depot. The parcel has to be assigned directly to a vehicle, making this a Dynamic Assignment Vehicle Routing Problem. Incorporating time windows is hard in this case. In this paper an approach is presented to solve this problem including Time Windows. A comparison is made with a real observation and with a solution method for the base problem
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Introduction: Retrospective studies suggest that a rapid initiation of treatment results in a better prognosis for patients in the emergency department. There could be a difference between the actual medication administration time and the documented time in the electronic health record. In this study, the difference between the observed medication administration time and documentation time was investigated. Patient and nurse characteristics were also tested for associations with observed time differences. Methods: In this prospective study, emergency nurses were followed by observers for a total of 3 months. Patient inclusion was divided over 2 time periods. The difference in the observed medication administration time and the corresponding electronic health record documentation time was measured. The association between patient/nurse characteristics and the difference in medication administration and documentation time was tested with a Spearman correlation or biserial correlation test. Results: In 34 observed patients, the median difference in administration and documentation time was 6.0 minutes (interquartile range 2.0-16.0). In 9 (26.5%) patients, the actual time of medication administration differed more than 15 minutes with the electronic health record documentation time. High temperature, lower saturation, oxygen-dependency, and high Modified Early Warning Score were all correlated with an increasing difference between administration and documentation times. Discussion: A difference between administration and documentation times of medication in the emergency department may be common, especially for more acute patients. This could bias, in part, previously reported time-to-treatment measurements from retrospective research designs, which should be kept in mind when outcomes of retrospective time-to-treatment studies are evaluated.
Stroke is the second most common cause of death and the third leading cause of disability worldwide,1,2 with the burden expected to increase during the next 20 years.1 Almost 40% of the people with stroke have a recurrent stroke within 10 years,3 making secondary prevention vital.3,4 High amounts of sedentary time have been found to increase the risk of cardiovascular disease,5–11 particularly when the sedentary time is accumulated in prolonged bouts.12–15 Sedentary behavior, is defined as “any waking behavior characterized by an energy expenditure ≤1.5 Metabolic Equivalent of Task (METs) while in a sitting, reclining or lying posture”.16,17 Studies in healthy people, as well as people with diabetes and obesity, have shown that reducing the total amount of sedentary time and/or breaking up long periods of uninterrupted sedentary time, reduces metabolic risk factors associated with cardiovascular disease.6,9,10,12–15 Recent studies have shown that people living in the community after stroke spend more time each day sedentary, and more time in uninterrupted bouts of sedentary time compared to age-matched healthy peers.18–20 Reducing sedentary time and breaking up long sedentary bouts with short bursts of activity may be a promising intervention to reduce the risk of recurrent stroke and other cardiovascular diseases in people with stroke. To develop effective interventions, it is important to understand the factors associated with sedentary time in people with stroke. Previous studies have found associations between self-reported physical function after stroke and total sedentary time, but inconsistent results with regards to the relationship of age, stroke severity, and walking speed with sedentary time.20,21 These results are from secondary analyses of single-site observational studies, not powered to address associations, and inconsistent in the methods used to determine waking hours; thus making direct comparisons between studies difficult.20,21 Individual participant data pooling, with consistent processing of wake time data, allows novel exploratory analyses of larger datasets with greater power. By pooling all available individual participant data internationally, this study aimed to comprehensively explore the factors associated with sedentary time in community-dwelling people with stroke. Specifically, our research questions were: (1) What factors are associated with total sedentary time during waking hours after stroke? (2) What factors are associated with time spent in prolonged sedentary bouts during waking hours?
Lastmile.info contributes to livable urban environments and efficient deliveries. LastMile.info is set to become the essential platform for finding and monitoring all the necessary information so that you can optimally plan and execute the final stage of the route during store deliveries:> Clear overview of regulations (such as restrictions and time windows)> Shorter waiting times: reduced financial and environmental burden> Greater driver satisfaction thanks to insight into delivery locations
Examining in-class activities to facilitate academic achievement in higher educationThere is an increasing interest in how to create an effective and comfortable indoor environment for lecturers and students in higher education. To achieve evidence-based improvements in the indoor environmental quality (IEQ) of higher education learning environments, this research aimed to gain new knowledge for creating optimal indoor environmental conditions that best facilitate in-class activities, i.e. teaching and learning, and foster academic achievement. The academic performance of lecturers and students is subdivided into short-term academic performance, for example, during a lecture and long-term academic performance, during an academic course or year, for example. First, a systematic literature review was conducted to reveal the effect of indoor environmental quality in classrooms in higher education on the quality of teaching, the quality of learning, and students’ academic achievement. With the information gathered on the applied methods during the literature review, a systematic approach was developed and validated to capture the effect of the IEQ on the main outcomes. This approach enables research that aims to examine the effect of all four IEQ parameters, indoor air quality, thermal conditions, lighting conditions, and acoustic conditions on students’ perceptions, responses, and short-term academic performance in the context of higher education classrooms. Next, a field experiment was conducted, applying the validated systematic approach, to explore the effect of multiple indoor environmental parameters on students and their short-term academic performance in higher education. Finally, a qualitative case study gathered lecturers’ and students’ perceptions related to the IEQ. Furthermore, how these users interact with the environment to maintain an acceptable IEQ was studied.During the systematic literature review, multiple scientific databases were searched to identify relevant scientific evidence. After the screening process, 21 publications were included. The collected evidence showed that IEQ can contribute positively to students’ academic achievement. However, it can also affect the performance of students negatively, even if the IEQ meets current standards for classrooms’ IEQ conditions. Not one optimal IEQ was identified after studying the evidence. Indoor environmental conditions in which students perform at their best differ and are task depended, indicating that classrooms should facilitate multiple indoor environmental conditions. Furthermore, the evidence provides practical information for improving the design of experimental studies, helps researchers in identifying relevant parameters, and lists methods to examine the influence of the IEQ on users.The measurement methods deduced from the included studies of the literature review, were used for the development of a systematic approach measuring classroom IEQ and students’ perceived IEQ, internal responses, and short-term academic performance. This approach allowed studying the effect of multiple IEQ parameters simultaneously and was tested in a pilot study during a regular academic course. The perceptions, internal responses, and short-term academic performance of participating students were measured. The results show associations between natural variations of the IEQ and students’ perceptions. These perceptions were associated with their physiological and cognitive responses. Furthermore, students’ perceived cognitive responses were associated with their short-term academic performance. These observed associations confirm the construct validity of the composed systematic approach. This systematic approach was then applied in a field experiment, to explore the effect of multiple indoor environmental parameters on students and their short-term academic performance in higher education. A field study, with a between-groups experimental design, was conducted during a regular academic course in 2020-2021 to analyze the effect of different acoustic, lighting, and indoor air quality (IAQ) conditions. First, the reverberation time was manipulated to 0.4 s in the intervention condition (control condition 0.6 s). Second, the horizontal illuminance level was raised from 500 to 750 lx in the intervention condition (control condition 500 lx). These conditions correspond with quality class A (intervention condition) and B (control condition), specified in Dutch IEQ guidelines for school buildings (2015). Third, the IAQ, which was ~1100 ppm carbon dioxide (CO2), as a proxy for IAQ, was improved to CO2 concentrations under 800 ppm, meeting quality class A in both conditions. Students’ perceptions were measured during seven campaigns with a questionnaire; their actual cognitive and short-term academic performances were evaluated with validated tests and an academic test, composed by the lecturer, as a subject-matter-expert on the taught topic, covered subjects discussed during the lecture. From 201 students 527 responses were collected and analyzed. A reduced RT in combination with raised HI improved students’ perceptions of the lighting environment, internal responses, and quality of learning. However, this experimental condition negatively influenced students’ ability to solve problems, while students' content-related test scores were not influenced. This shows that although quality class A conditions for RT and HI improved students’ perceptions, it did not influence their short-term academic performance. Furthermore, the benefits of reduced RT in combination with raised HI were not observed in improved IAQ conditions. Whether the sequential order of the experimental conditions is relevant in inducing these effects and/or whether improving two parameters is already beneficial, is unknownFinally, a qualitative case study explored lecturers’ and students’ perceptions of the IEQ of classrooms, which are suitable to give tutorials with a maximum capacity of about 30 students. Furthermore, how lecturers and students interact with this indoor environment to maintain an acceptable IEQ was examined. Eleven lecturers of the Hanze University of Applied Sciences (UAS), located in the northern part of the Netherlands, and twenty-four of its students participated in three focus group discussions. The findings show that lecturers and students experience poor thermal, lighting, acoustic, and IAQ conditions which may influence teaching and learning performance. Furthermore, maintaining acceptable thermal and IAQ conditions was difficult for lecturers as opening windows or doors caused noise disturbances. In uncomfortable conditions, lecturers may decide to pause earlier or shorten a lecture. When students experienced discomfort, it may affect their ability to concentrate, their emotional status, and their quality of learning. Acceptable air and thermal conditions in classrooms will mitigate the need to open windows and doors. This allows lecturers to keep doors and windows closed, combining better classroom conditions with neither noise disturbances nor related distractions. Designers and engineers should take these end users’ perceptions into account, often monitored by facility management (FM), during the renovation or construction of university buildings to achieve optimal IEQ conditions in higher education classrooms.The results of these four studies indicate that there is not a one-size fits all indoor environmental quality to facilitate optimal in-class activities. Classrooms’ thermal environment should be effectively controlled with the option of a local (manual) intervention. Classrooms’ lighting conditions should also be adjustable, both in light color and light intensity. This enables lecturers to adjust the indoor environment to facilitate in-class activities optimally. Lecturers must be informed by the building operator, for example, professionals of the Facility Department, how to change classrooms’ IEQ settings. And this may differ per classroom because each building, in which the classroom is located, is operated differently apart from the classroom location in the building, exposure to the environment, and its use. The knowledge that has come available from this study, shows that optimal indoor environmental conditions can positively influence lecturers’ and students’ comfort, health, emotional balance, and performance. These outcomes have the capacity to contribute to an improved school climate and thus academic achievement.
