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The study aimed to investigate the Effect of Academic Stress on Students' Academic Achievements and Its Implications for Their Future Lives. A correlational research design was employed through stratified random sampling (n[subscript i] = 270, where female = 80 and male =190) to collect pertinent data through a questionnaire. Descriptive and inferential statistics were employed. Firstly, academic stress highly affects students' academic achievement, with females being more stressed (65.20%) than males (59.60%), which leads to a high dropout rate and low graduation rate. Secondly, about 69% were accustomed to drinking alcohol, chewing Khat, absenteeism, and smoking whereas about 31% were regularly engaging in unsafe sex, physical fights, smoking shisha, and poor personal sanitation. Thirdly, there were significant positive relationships among sex, age, academic batch, and student's cumulative grade point average. To conclude, academic stress hinders good academic achievement, increases college dropout rates, low graduation rates, and lack of self-confidence. It leads to drinking alcohol, absenteeism, chewing khat, reduced academic morale of students, and failure in completing assignments on time. Therefore, university authorities should ensure a good academic environment for the students and minimize academic stress through appropriate course load, appropriate lecture hours, proper lecture schedules, and non-congested lecture halls.

Early identification of at-risk students for timely intervention is critical to prevent non-completion of study programs. This article proposes a flipped class framework to support the academic success of at-risk students in an undergraduate Calculus course. It comprises three main components of setting, conduct, and monitoring. A flipped support class was implemented as periodic sessions throughout the learning semester over six consecutive semesters for the selected total of 560 at-risk students. At-risk students who attended the flipped support class reported a higher passing percentage than those who did not, in each of the six semesters. A strong mathematics foundation contributed to the likelihood of passing the course; however, it can be further increased by attending more hours of the flipped support class.

The benefits of afterschool activity participation for youth development are well-documented. An interesting question dominating this field is whether there is a threshold at which point participating in too many activities (breadth) and spending too much time in those activities (intensity) is negatively associated with desirable outcomes. Using 9th grade student data (N=115,731) from three administrations of a state-wide school survey, we explore whether students' breadth and intensity of afterschool participation is associated with GPA and perceived family and community support. Findings corroborate prior research in demonstrating the association between breadth and intensity. Importantly, we extend the discussion, with three important observations. First, a linear model is insufficient for modeling these complex associations with outcomes. Second, there is a threshold at which too much participation has a negative impact in these outcomes. Third, variations in activities, time windows and indices have small or no influence in the association with outcomes.

Internationalization in higher education is essential, and although active learning methodologies are increasing and allow students to develop transversal skills, most still have a very local scope. In this context, the Collaborative Online International Learning (COIL) methodology is an interesting approach to benefit the students' development. It consists of an online program that involves creating multicultural teams to develop a specific learning project. Although this methodology is expanding, its use in physiology is still scarce. This paper aims to show an example of applying COIL methodology in physiology topics to enhance higher-education students' innovation and business skills. Our example project developed a sports-assessment service concept focused on physiology and biomechanics assessments. The program involved teams from Brazil, Germany, and Spain, comprising undergraduate and master students. Over 7 weeks, these teams, mentored by professors and researchers, engaged in workshops covering COIL methodology, business model design, executive summary planning, economic analyses, and communication techniques. Key outcomes included learning new concepts, developing soft skills, building confidence in innovative solution proposals, and experiencing diverse cultures. Challenges faced were language barriers, scheduling, task complexity, and logistical issues. This experience confirms the effectiveness of incorporating programs using COIL methodology into educational curriculums. Doing so exposes physiology students to innovation, entrepreneurship, and business creation while strengthening their professional connections and opening up postgraduation opportunities.

Cohort scheduling intentionally places students in the same sections of several classes (e.g., biology, algebra, and writing) with a consistent peer group and is typically done for small groups (<30 students) to enable better interaction among students. The goal of this study was to compare cohort scheduling to traditional scheduling methods among freshmen in a physiology-related program. Outcomes included retention to the university and major, semester grades, and institutional integration and perceived group cohesion. Incoming freshmen (n = 209) were randomized into control (n = 43; scheduled with traditional methods) and intervention (n = 166; coenrolled in first-year seminar course, biology, and medical terminology) groups. Outcomes were collected via surveys or requested from the university registrar. There was no significant difference in the likelihood of retention to the university or major and no differences between groups in pass/fail rates for the first-year seminar or biology courses. At the end of the semester, there were no differences between groups in Perceived Cohesion for Small Groups (P = 0.102) or the Institutional Integration Scale (P = 0.357). However, the intervention group scored higher on the Institutional Integration Scale's subscales related to social integration and faculty. Cohort scheduling did not impact retention to the university or major but improved secondary outcomes related to retention, specifically social integration and student perceptions of faculty.

The syllabus is a required document for all courses to provide students with course information, policies, and assignment deadlines. The goals of this study were to investigate students' perception of the role of the syllabus, preferred location of assignment deadlines, and preferred style of receiving deadline notifications. Faculty (n = 14) and students (n = 324) from community colleges through professional schools were invited to participate and complete a survey. We used a mixed-method design of survey questions, and the results demonstrated that students defined a syllabus as both a contract and a learning tool and that it should be flexible. Students ranked assignment deadlines as the most important part of the syllabus, yet a follow-up question indicated that most referred to four distinct locations in their learning management system (LMS) to find these deadlines. Although students preferred to receive deadline notifications on their smartphones, they also wanted to be reminded by faculty in class. This study helps faculty to obtain a glimpse of current student practices. We recommend that faculty communicate with students the role and expected use of the syllabus in their course while emphasizing its use to identify deadlines. This renewed practice will be time well spent to avoid student confusion and missed deadlines.

Public transport plays a fundamental role in providing access to urban opportunities. People with mobility restrictions face greater obstacles than other members of society. This article seeks to explore the obstacles faced by wheelchair users in Montevideo, Uruguay. To do so, we designed a set of trips to actual destinations for five types of activities to compare time costs between wheelchair users and people without mobility restrictions. We estimated time costs for sidewalk segments, waiting time, onboard bus travel time, and coupling time (i.e., idle time due to the lack of bus provision closer to the activity starting times) and found that, relative to people without mobility restrictions, each of these transit components poses high costs for wheelchair users. From a policymaking perspective, our findings point out the importance of expanding universal design in public transport. They also call attention to other factors that explain travel time such as sidewalks segments and the temporal organization of urban activities that may turn into schedule mismatches.

Reduced hours of instruction are reported within the gross anatomy education literature. Anatomy instruction continues to be challenged with motivating and inspiring learners to value the contribution of gross anatomy knowledge to their career development alongside increased organizational demands for efficiency and effectiveness. To address these demands, this retrospective study sought to understand how the relative timing and amount of gross anatomy instruction were related to examination performance. Undergraduate and graduate students between 2018 and 2022 were assigned to three cohorts determined by enrollment in prosection-based anatomy only (n = 334), concurrent enrollment in prosection- and dissection-based anatomy in the same semester (n = 67), or consecutive enrollment in the courses one year apart (n = 43). Concurrent students had higher prosection-based anatomy examination scores than prosection-only and consecutive students. Consecutively, enrolled students outperformed concurrently enrolled students on the first two dissection examinations but showed no performance differences on the third and fourth dissection examinations. While the results on the timing and presentation of anatomical instruction were inconclusive, the results do support increased instructional time using both prosection and dissection modalities concurrently to improve performance on identification-based gross anatomy examinations.

The convenience and flexibility of online doctoral programs make them popular with working adults. However, online degree programs may lack relationship-building features, making students feel disconnected and isolated. These feelings are more pronounced as students enter their program's dissertation portion as they become responsible for setting schedules and deadlines. This reflection discusses one way to combat student isolation-- establishing community through virtual meetings where doctoral candidates can share and offer support to encourage members as they work toward dissertation completion.

The European Credit Transfer and Accumulation System (ECTS) is based on a quantitative and accumulative understanding of time, which increasingly frames academic practices and notions of learning in higher education (HE). By example of a recent Danish policy reform, the article explores the connections between the ECTS, new institutional practices, and altered ideas of learning and academic achievement in HE. First, it shows how the ECTS is entangled with a notion of learning and academic achievement as a linear and well-ordered practice, which is determined by the clock. Second, it exemplifies how institutions in Denmark have used the ECTS as a framework for micro-governing students' time by prescribing activities that students should undertake on an hourly basis to meet the requirements of a 42-hour study week. Third, it calls for discussion of the functionalist image of learning and academic achievement as a clean and univocal process that is rational, foreseeable and finite, which the ECTS imposes on HE.

Medical education continually adapts to the needs of future health care professionals, with student motivation in research being a pivotal aspect. This study at Alfaisal University aimed to explore the motivations, benefits, and challenges faced by medical students in extracurricular research activities. Using a mixed-method approach, we combined quantitative surveys with qualitative group interviews. Findings revealed that both extrinsic (e.g., enhancing postgraduate training prospects) and intrinsic (e.g., personal interest and skill refinement) factors significantly motivate students to be involved in research activities. Participants unanimously acknowledged skill enhancement, particularly in literature comprehension, creative ideation, and networking. However, challenges such as conflicts with course scheduling, lack of hands-on experiences, and mentorship issues were identified as potential barriers to research participation. Addressing these barriers and understanding motivations can inform the design of research programs, enhancing the overall student research experience. This study underscores the importance of research in medical education, emphasizing the need for institutions to prioritize addressing challenges and leveraging benefits to prepare medical students for a research-integrated clinical future.

The present study evaluated the use of computerized equivalence-based instruction (EBI) to teach classes representing schedules of reinforcement to staff members. Four, 5-member classes representing fixed ratio, fixed interval, variable ratio, and variable interval schedules of reinforcement were taught. The participants were 16 staff members between the ages of 22 and 30 years of age that were employed at a private school for individuals with autism. Participants were randomly assigned to either an experimental or control group. A pretest-training-posttest-maintenance design was used to evaluate the effects of EBI on participants' performance during both a computer-based test and a written multiple-choice test. Participants in the control group were only exposed to pretest and posttests. All participants in the experimental group acquired the baseline trained relations during match-to-sample instruction. Test scores improved from pretest to posttest and derived (untrained) relations emerged across all participants in the experimental group following training but did not change for the participants in the control group. In addition, participants maintained the learned relations one week after EBI was completed. The present study demonstrated that EBI is an effective teaching procedure to teach schedules of reinforcement to staff members in the field of applied behavior analysis.

The COVID-19 pandemic has forced the rapid adoption of remote teaching modalities including "hyflex" where students attend some class sessions in person and some online. Managing the hyflex course requires faculty to quickly generate several reports and to update these reports rapidly when the authorities adjust the rules, students add/drop, or the number of course sessions is changed. The creation of these reports is tedious and error-prone, so they need to be automated. However, the nature of the task precludes traditional Excel programming approaches. We use Excel's new "dynamic array" capabilities (which are available only in Excel 365) to automate the creation and updating of the reports needed to manage the hyflex course. We show how to program the reports, and we discuss the importance of taking an iterative approach to creating effective, error-free cell formulas. To help the reader acquire genuine access to the dynamic array functions, we provide a practical tutorial on the principles and new concepts of dynamic arrays, explain how they relate to legacy array functions, and present selected dynamic array functions including SEQUENCE, FILTER, and XLOOKUP.

Student evaluation of teaching (SET) involves numerous conceptual and methodological problems. This paper focuses on a specific methodological dilemma: whether to conduct SET surveys before or after the final examination. This decision is a critical administrative and practical issue that can affect the level and quality of SET results. To address this problem, a systematic literature search was initially performed. However, it found only 17 studies and inconclusive results. In the empirical component, a replication survey (n = 5,077) was conducted. It posed seven course-related questions to the same students before and after their final examination. A slightly higher but statistically significant (p < 0.001) average score was obtained in the after-examination survey (a 0.06 difference on a 1-5 scale). This increase was partly attributed to a positive examination experience. The overall test-retest correlation (r = 0.61) indicated relatively low reliability, suggesting that the before-examination survey better reflected students' attitudes. The data quality was also higher in the before-examination survey, although the before-examination survey could not properly assess the examination-related aspects of the course. From a methodological perspective, the most comprehensive insight seems to be derived from conducting SET surveys before the examination and following up with a brief after-examination survey.

This study investigated English first language (L1) speakers' motivation for learning Chinese as a second language (L2) in Australia and the links of underlying motivational components to learner sociobiographical (e.g. gender and reasons for learning) and language learning variables (i.e. experience abroad, hours of self-study, and speaking with Chinese L1 speakers). A total of 164 Australian university students participated in the study. The findings show that instrumentality (e.g. employment and travel) tops their orientations to learn L2 Chinese, followed by integrative orientations (e.g. interest in the Chinese language and culture and desire to communicate with the Chinese people), with mandatory requirements as the least agreed reason. Statistical analyses reveal that the underlying structure of their motivation consists of six components: intrinsic motivation, orientation, anxiety, integrativeness, instrumentality, and sociality. Some of them are linked to certain learner background variables. Females demonstrated stronger instrumental orientation than their male counterparts. Visiting China and self-study hours have positive connections with intrinsic motivation. The findings shed light on the complexities and multifaceted characteristics of motivation and their links with learner sociobiographical and learning variables by providing evidence for global language speakers' motivation to learn other languages.

Responding to a recent call for interdisciplinary research into 'night studies', the present study attempts to put the nighttime at the centre of the sociolinguistic enquiry, seeking to explore how the nocturnal linguistic landscape (LL) differs from the diurnal LL by drawing on Singapore's Chinatown as the research site. A total of 1091 LL items constitute the database. Altogether 808 LL items were collected during the daytime and 283 of these LL items were found to be illuminated during nighttime site visits. The quantitative analysis reveals that the nocturnal LL differs from the diurnal LL in several ways: at the top-down level, the nocturnal LL shows a strong monolingual English tendency, while the diurnal LL has a tendency towards multilingualism; at the bottom-up level, fewer languages are used in the nocturnal signs than in the diurnal signs and Chinese is used as the prominent language in nocturnal signs, whereas English has a leading position in the diurnal signs. Our findings complicate previous scholarly understanding of bilingual and multilingual configurations in Singapore, suggesting that the LL has its own unique representation at night. The potential for integrating nighttime as a new dimension to examine LL is discussed .

In times of decreasing response rates and survey participation, many 'best-practices' have been developed for increasing survey recruitment. However, most of these have never been adequately and experimentally scrutinized for their efficacy. Therefore, in this research note, we draw on probability-based data of an online panel and experimentally examine whether it is possible to increase survey participation behavior by communicating a deadline extension. Overall, our results show that it is possible to influence participation behavior by deadline communication. While overall response speed was significantly faster when a deadline extension was communicated, the overall response rate was slightly lower, compared to scenarios in which no deadline extension was communicated. Thus, we refrain from recommending a deadline extension unless there are specific reasons to do so.

In this study, we introduce a critical assessment of methodological assumptions made by researchers when using correspondence tests to measure ethnic discrimination. We aim to investigate whether (1) the order in which the applications are sent out, between the test and control person, (2) the conducted matched triad tests, in comparison to matched duo tests, and (3) the timing of application within the week, have an effect on discrimination rates. For this purpose, we made use of 2.984 matched correspondence tests across Flanders, the Dutch-speaking part of Belgium. Our findings suggest that the correspondence tests are rather robust for the variability in methodology. While these different methodological choices do affect the general invitation rates, they do not affect discrimination rates.

The evidence-based parenting program Video-Feedback Intervention to promote Positive Parenting and Sensitive Discipline (VIPP-SD) may have potential to also support teachers in primary schools in their interaction with children with behavior problems. We therefore adapted the intervention for use with primary school teachers (VIPP-School). Here we examined the feasibility of VIPP-School, using qualitative analyses of the perceptions of teachers and VIPP-interveners on their experiences with VIPP-School. Three teacher-child dyads from kindergarten and 2nd grade participated in six sessions of VIPP-School. Perceptions of the participants were collected using semi-structured interviews, logbooks, observations and a questionnaire. The data were qualitatively analysed. Thematic analysis showed five main themes: 1) scheduling of appointments, 2) experiences with intervention elements, 3) working alliance, 4) changes in the teacher-child interaction, 5) defining the target group. Teachers mentioned notable time investment, but appreciated the video feedback as a great way to become more aware of subtle signals of the child and their own behaviors. This study shows that with some adaptations VIPP-School is an acceptable and deliverable intervention for teachers in primary education. It has potential for supporting teachers in the interaction with children with behavioral problems. Further research on the effectiveness of VIPP-School is recommended.

Coaching is increasingly being used as a mechanism to improve the quality of early childhood education. Yet, for coaching outside of researcher-controlled interventions, limited information details coaches' reports of their practices' professional learning needs. We addressed this gap via an exploratory study utilizing online questionnaires of 91 coaches working with educators in a Midwestern US state across 12 coaching initiatives. Most participants had less than 5 years of experience working as an early childhood coach. Almost a third coached for multiple initiatives. Coaching occurred via multiple formats and often addressed behavior management and social emotional development regardless of the coaching initiative. Coaches identified challenges regarding scheduling and working with difficult learners and reported that peer support and being coached themselves were the most beneficial learning experiences. Findings suggest that the perspective of the coach is important in informing training and professional learning for both coaches and educators.

Not enough attention is paid to the instructional design decisions that define students' experiences. Lisa Dover Kingsley and Jennifer Perry Cheatham contend that while quality teaching is crucial, the structural decisions supporting meaningful engagement often go unexamined. Using innovations in North Kansas City, Missouri, as a throughline, they review key themes present in research on four elements of instructional design. They urge leaders to reconsider outdated and unexamined design practices with an equitable approach, centering the voices of students typically left out of such decisions. They offer practical shifts and entry points for leaders to consider as they build a more inclusive and effective instructional design.

Immersive scheduling engages students in one or two subjects at a time over short teaching periods. While there is incipient evidence that immersive delivery can heighten academic outcomes in undergraduate education, much less is known about its effectiveness in pathways or access programs. This exploratory mixed-methods study investigated how a 6-week immersive model influenced academic performance and success in an Australian pathways program. Analysis of final grades (N = 6,613) over two years revealed a highly statistically significant improvement in success rates in the immersive model. Particularly strong gains were observed for students studying online and students aged 40 and over. Focus group participants indicated three elements of the immersive model that supported their learning and success: a manageable study load; a converged, media-rich and aligned curriculum; and opportunities for respectful and dialogic interactions. These attributes were linked with enhanced focus, confidence, independence and critical thinking.

This case study explores the transformation of study room reservations at the Oklahoma State University (OSU) Libraries. Initially reliant on manual methods, the library transitioned to vendor-supported software and would later develop a custom, open source user interface (UI) to streamline booking efficiency. These improvements help demonstrate the effectiveness of tailored solutions in meeting user needs and improving system functionality for all stakeholders.

The aim of this exploratory sequential mixed methods study was to examine the beliefs of Early Childhood Education teachers regarding the enculturation process of children. Eleven 2-year-old classroom teachers (M[subscript age]= 40.64; DT = 7.43; 81.81% female) participated in the qualitative phase, and 256 teachers (M[subscript age] = 42.25; DT = 9.78; 90.3% female) participated in the quantitative one. Based on interviews, four main themes were identified: Routines and habits, Values, Models of behavior, and Knowledge. Based on the qualitative results, an instrument was created to measure the teachers' beliefs regarding enculturation in Early Childhood Education and Care. High convergence was obtained between the qualitative and quantitative results, supporting the idea that teachers are active agents of enculturation and social role models for children. We conclude that enculturation should be a curricular objective and that it deserves a greater presence in professional development programs for Early Childhood Education teachers.

Ship maintenance service optimisation is of great significance for improving the competitiveness of shipbuilding enterprises. In this paper, we investigate a ship maintenance service scheduling problem considering the deteriorating maintenance time, distributed maintenance tasks, and limited maintenance teams. The objective is to minimise the service span. First, we construct an initial mixed-integer programming model for the studied problem. Then, through the property analysis of the problem with a single maintenance team, an exact scheduling algorithm is proposed. In addition, the lower bound of the problem with multiple maintenance teams is derived. A scheduled rule is developed to obtain the lower bound for the problem. Based on the property analysis, the original mixed-integer programming model is simplified to an improved mathematical programming model. Since the studied problem is NP-hard, this paper proposes two heuristic algorithms and an integrated metaheuristic algorithm based on the variable neighbourhood search to obtain approximate optimal solutions in a reasonable time. In computational experiments, the two models can solve problems on small scale, while metaheuristics can find approximately optimal solutions in each problem category. Moreover, the computational results validate the performance of the proposed integrated metaheuristic in terms of convergence and stability. [ABSTRACT FROM AUTHOR]

We study scheduling problems on parallel dedicated machines. Thus, each job can be processed on one specific machine only. The option of job-rejection is considered, and the total permitted rejection cost of all the jobs is bounded. Six scheduling problems are solved: ( $ i $ i) minimising makespan, ( $ ii $ ii) minimising makespan with release-dates, ( $ iii $ iii) minimising total completion time, ( $ iv $ iv) minimising total weighted completion time, ( $ v $ v) minimising total load, and ( $ vi $ vi) minimising maximum tardiness. Pseudo-polynomial dynamic programming algorithms are introduced for all these NP-hard problems. [ABSTRACT FROM AUTHOR]

This paper deals with a problem of tactical planning of production activities. It considers the case of the world's largest phosphate production site, with several mines, several washing plants, and a drying plant, with transport of merchantable phosphate ores (MOs) by rail or pipeline. The tightening of product quality requirements by customers prevents from continuing to plan its production with the current rules. Currently, the plants are managed quite independently, source ores (SOs) are pre-assigned to the MOs for blending, and the MO production routings in the washing plants are predetermined. We propose a model that ensures flexibility in the production of MOs through the choice of dynamic blends and routings. Our approach goes beyond any pre-allocation of resources and aims at synchronising the flows of the washing plants. This specific problem of blending production and planning has not been addressed in the literature. Still, it is widely encountered in many mining industries and offers perspectives for the application of the approach proposed. Numerous tests show that this model ensures the quality constraints related to the composition of several chemical components, with a generalised use of the most economical routings while ensuring horizontal and vertical alignment of decisions. [ABSTRACT FROM AUTHOR]

Transporting ore from mines to ports is of significant interest in mining supply chains. These operations are commonly associated with growing costs and a lack of resources. Large mining companies are interested in optimally allocating resources to reduce operational costs. This problem has been previously investigated as resource constrained job scheduling (RCJS). While a number of optimisation methods have been proposed to tackle the deterministic problem, the uncertainty associated with resource availability, an inevitable challenge in mining operations, has received less attention. RCJS with uncertainty is a hard combinatorial optimisation problem that is challenging for existing optimisation methods. We propose an adaptive population-based simulated annealing algorithm that can overcome existing limitations of methods for RCJS with uncertainty, including pre-mature convergence, excessive number of hyper-parameters, and the inefficiency in coping with different uncertainty levels. This new algorithm effectively balances exploration and exploitation, by using a population, modifying the cooling schedule in the Metropolis-Hastings algorithm, and using an adaptive mechanism to select perturbation operators. The results show that the proposed algorithm outperforms existing methods on a benchmark RCJS dataset considering different uncertainty levels. Moreover, new best known solutions are discovered for all but one problem instance across all uncertainty levels. [ABSTRACT FROM AUTHOR]

In modern manufacturing and service industries, urgent orders and service tasks are common, and the speed of handling such urgent tasks is an important indicator of production and service efficiency. In this study, we consider scheduling jobs on two parallel machines with the random arrival of an emergency job. The objective is to minimise the makespan, subject to a given maximum waiting time of the emergency job. We first show that the worst-case ratio of the existing algorithm LPT $ _l $ l -SPT $ _{m-l} $ m − l is at least 3/2 when m = 2. We then analyse some properties of the optimal schedule and derive lower bounds on the optimal makespan. Finally, we present an improved approximation algorithm with a tight worst-case ratio of 4/3. We also provide numerical results showing that our proposed algorithm outperforms algorithm LPT $ _l $ l -SPT $ _{m-l} $ m − l . [ABSTRACT FROM AUTHOR]

This paper explores the multitasking scheduling problem via alternate periods, which is common in manufacturing and service industries. The shift pattern comprises regular shifts interspersed with breaks, allowing job switching. We focus on two alternate periods consisting of odd periods and even periods. Each job is not only processed completely within odd periods or completely within even periods, but job switching is also allowed between alternate periods with additional overhead. For this problem, we propose two job switching models. The first model is alternate periods multitasking scheduling with switching time, where a fixed switching time is incurred whenever a job is switched from one period to its adjacent period. The second model is alternate periods multitasking scheduling with diverse speeds, where an urgent switching cost is produced if a job is processed through the alternate periods with different speeds. For the two models, we study three scheduling objectives including minimising the total completion time, minimising the maximum lateness, and minimising the number of tardy jobs, respectively. For each of the problems under consideration, we investigate the structural properties of the optimal schedule and develop the corresponding pseudo-polynomial time dynamic programming algorithm. Furthermore, we give examples to effectively illustrate the proposed algorithms. [ABSTRACT FROM AUTHOR]

The purpose of this article is to investigate a practical scheduling problem in which a group of elective surgical cases are scheduled over time, while considering their unpredictable durations and potential delays in the sterilisation of surgical instruments. The primary objectives were to schedule the maximum number of surgeries and decrease overtime for the surgical staff, as well as limit the number of instruments requiring emergency sterilisation. The study was conducted in collaboration with the University Hospital of Angers in France, which also contributed historical data for the experiments. We propose two robust mixed integer linear programming models, which are then solved iteratively through a rolling horizon approach, in which the objective functions are taken into account in lexicographic order. Experiments on randomly generated instances indicated which of the two approaches had better performance. Comparison of the results for a real-world scenario involving actual planning at the hospital indicated a greater than 69% decrease in overtime, and a minimum of 92% fewer stressful situations in the sterilising unit. [ABSTRACT FROM AUTHOR]

The purpose of the research was to improve the quality of information process control in the interaction of mobile object grouping. This proposal is based on developing and implementing new combined models with algorithms for optimal planning and scheduling of information processes during interaction among mobile objects. The proposed approach would guarantee the required level of aircraft maintenance at the airport. The novel aspect of the proposed solution is that it divides the spatially distributed task of selecting a plan and a schedule for information processes in mobile objects into two subtasks, describing the spatial and temporal features of the original task. The spatial component of the original problem is described as a static model. Using this model, the optimal distribution of information operations over the resources of mobile objects is carried out without binding these operations to time. The time component is described as a dynamic model, within which the information operations are tied to time and the corresponding resources. Examples of construction and research of synthesized plans and schedules are given. Abbreviations: DM, Dynamic model; FIFO, First-In First-Out; Gr, Grouping; HSC, Hardware and software complex; ITS, Intelligent Transport System; IP, Information Processes; MO, Mobile object; PC, Programmed control; SM, Static model; SMS, Special mathematic support; LDM, Logical-dynamic models [ABSTRACT FROM AUTHOR]

Genetic programming has achieved great success in project scheduling by generating Priority Rules (PRs) through evolution. However, the frequent disturbance factors in practice not only lead to the appropriate PR changes in different states, but also increase the calculation consumption in evaluation. In this paper, a novel Hyper-heuristic-based Surrogate-Assisted Dual-Tree Genetic Programming (HSDGP) framework is proposed for the Dynamic Resource Constrained Multi-Project Scheduling Problem with new project Insertions and resource Disruptions (DRCMPSP-ID). Uniquely, the proposed method automatically evolves two PRs for scheduling DRCMPSP-ID under normal and disruptive states respectively, and an expansion search mechanism based on neighbourhood is designed to improve PR generation ability by generating a large number of offspring and implement the search of dual-tree encoding. Furthermore, in order to estimate the fitness of new individuals, an activity-sequence based surrogate is proposed to deal with the input of activity sequence during schedule generation and reduce the evaluation calculation consumption. Based on the instances constructed by the existing benchmark, the experimental result shows the superiority of HSDGP and the impact of key parameters on its performance. [ABSTRACT FROM AUTHOR]

The paper presents a discrete-time mixed-integer linear programming (MILP) model for a generalised flexible job-shop scheduling problem as represented by a state-task network. The problem is characterised by reentrant flow, sequence-dependent changeover time, machine downtime, and skilled labour requirements. Two preprocessing procedures are proposed to reduce the size of the MILP model, and represent a major contribution of the research. The procedures reduce the number of assignment variables by exploiting job precedence and workforce qualifications. Machine availability for each task is determined as a function of possible start and end times, given duration, and maintenance schedule. The overall objective is to maximise the number of scheduled tasks while minimising their total finish time. Computational experiments are conducted with real and randomly generated instances. The results show that optimal solutions can be obtained for medium-size problems within a reasonable amount of time, primarily due to the use of the preprocessing procedures. [ABSTRACT FROM AUTHOR]

This paper deals with an overview of flowshop group scheduling problems in the manufacturing environment. The aim of this paper is twofold: (i) making a comprehensive survey of research on flowshop group scheduling problems in manufacturing systems, and (ii) presenting a bibliometric analysis. We address the general definition of flowshop group scheduling problems and provide a taxonomy of methodologies used in previous literature. The papers are presented from several perspectives, including the utilised objective functions, a transformation of problem structure, benchmarks in existing literature, and solution approaches. Additionally, bibliometric analysis, including keyword and journal analyses, is conducted for articles published between 1986 and 2022. Finally, suggestions for future developments are listed to further consolidate this area. [ABSTRACT FROM AUTHOR]

This paper addresses a serial distributed permutation flowshop scheduling problem (SDPFSP) inspired by a printed circuit board assembly process that contains two production stages linked by a transportation stage, where the scheduling problem in each production stage can be seen as a distributed permutation flowshop scheduling problem (DPFSP). A sequence-based mixed-integer linear programming model is established. A solution representation consisting of two components, one component per stage, is presented and a makespan calculation method is given for the representation. Two suites of accelerations based on the insertion neighbourhood are proposed to reduce the computational complexity. A cooperative iterated greedy (CIG) algorithm is developed with two subloops, each of which optimises a component of the solution. A collaboration mechanism is used to conduct the collaboration of the two subloops effectively. Problem-specific operators including the NEH-based heuristics, destruction, reconstruction and three local search procedures, are designed. Extensive computational experiments and statistical analysis verify the validity of the model, the effectiveness of the proposed CIG algorithm and the superiority of the proposed CIG over the existing methods for solving the problem under consideration. [ABSTRACT FROM AUTHOR]

A solution approach is proposed for the interday problem of assigning chemotherapy sessions at a network of treatment centres with the goal of increasing the cost-efficiency of system-wide capacity use. This network-based scheduling procedure is subject to the condition that both the first and last sessions of a patient's treatment protocol are administered at the same centre the patient is referred to by their oncologist. All intermediate sessions may be administered at other centres. It provides a systematic way of identifying effective multi-appointment scheduling policies that exploit the total capacity of a networked system, allowing patients to be treated at centres other than their home centre. The problem is modelled as a Markov decision process which is then solved approximately using techniques of approximate dynamic programming. The benefits of the approach are evaluated and compared through simulation with the existing manual scheduling procedures at two treatment centres in Santiago, Chile. The results suggest that the approach would obtain a 20% reduction in operating costs for the whole system and cut existing first-session waiting times by half. A key conclusion, however, is that a network-based scheduling procedure brings no real benefits if it is not implemented in conjunction with a proactive assignment policy like the one proposed in this paper. [ABSTRACT FROM AUTHOR]

Dynamic task scheduling problem in cloud manufacturing (CMfg) is always challenging because of changing manufacturing requirements and services. To make instant decisions for task requirements, deep reinforcement learning-based (DRL-based) methods have been broadly applied to learn the scheduling policies of service providers. However, the current DRL-based scheduling methods struggle to fine-tune a pre-trained policy effectively. The resulting training from scratch takes more time and may easily overfit the environment. Additionally, most DRL-based methods with uneven action distribution and inefficient output masks largely reduce the training efficiency, thus degrading the solution quality. To this end, this paper proposes an improved DRL-based approach for dynamic task scheduling in CMfg. First, the paper uncovers the causes behind the inadequate fine-tuning ability and low training efficiency observed in existing DRL-based scheduling methods. Subsequently, a novel approach is proposed to address these issues by updating the scheduling policy while considering the distribution distance between the pre-training dataset and the in-training policy. Uncertainty weights are introduced to the loss function, and the output mask is extended to the updating procedures. Numerical experiments on thirty actual scheduling instances validate that the solution quality and generalization of the proposed approach surpass other DRL-based methods at most by 32.8% and 28.6%, respectively. Additionally, our method can effectively fine-tune a pre-trained scheduling policy, resulting in an average reward increase of up to 23.8%. [ABSTRACT FROM AUTHOR]

As a new production pattern, the hybrid seru system (HSS) originated from the actual production scenario. In the HSS, the implementation of the worker transfer strategy can further enhance the system's flexibility but is rarely studied at present. In this paper, we develop a reinforcement learning driven two-stage evolutionary algorithm (RL-TEA) to address the hybrid seru system scheduling problem with worker transfer (HSSSP-WT). To conquer this complex problem, the HSSSP-WT is divided into worker assignment-related subproblems (WS) and batch scheduling-related subproblems (BS) according to the problem characteristics. To effectively solve the subproblems, a probability modelbased exploration and a lower bound-guided heuristic are presented for the WS, and a greedy search is designed for the BS. Meanwhile, to improve search efficiency and effectiveness, a knowledgebased selection mechanism is proposed to determine which subproblem group to optimise in each generation by fusing a reinforcement learning technique and a lower bound filtering strategy. Moreover, an elite enhancement strategy inspired by the problem property is designed to improve the solution quality. Experimental results demonstrate the effectiveness of the worker transfer strategy and the superior performance of the RL-TEA compared with the state-of-the-art algorithms in solving the HSSSP-WT. [ABSTRACT FROM AUTHOR]

High charging loads associated with fleets of commercial electric vehicles (EVs) are expected to significantly stress electric power distribution networks, leading to high costs seen by fleet operators. To address these challenges, this report presents a highly flexible smart charging (SC) algorithm for managing EV fleets that arrive and depart from a common depot on a schedule. The algorithm features (i) primary consideration of multiple fleet operator preferences (e.g. minimizing cost, using carbon-free energy), (ii) secondary consideration of grid impact that leverages the existence of multiple optimal (or near-optimal) ways to satisfy fleet operator preferences, and (iii) automatic detection and handling of infeasibility due to large energy demands (characteristic of fleet charging). Provided in this document are two numerical impact assessment studies in which the SC algorithm is shown to be superior to conventional rapid charging, and conventional ‘smart’ charging solutions on the market. These studies utilize a set of synthetic, but realistic fleet charging requirements, a physics-based model of a real feeder and one year of real, hourly load data for that feeder. The first numerical study shows that the proposed SC algorithm can lead to significant (up to 44%, but scenario-dependent) reductions in a fleet operator’s annual electricity bill. The second numerical study shows that significant transformer overloading and voltage drop issues can be associated with conventional fleet charging methods, and that the proposed SC algorithm eliminates these issues, thereby enabling higher EV penetration levels and offsetting infrastructure upgrades. View the NCST Project Webpage