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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.

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.

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.

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]

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 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]

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 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]

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]

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]

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]

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]

A production plan gives the quantity of products to release on the shop floor in each period, where a period may represent a week or a month. The plan is the basis for negotiating order acceptance and delivery dates with customers and suppliers. The production plan must respect the available capacity on the shop floor, as underloading the shop floor leads to a loss of opportunity, and thus, a loss of competitiveness for the company. To properly manage the production capacity while negotiating with suppliers and customers, the production planners need a tool to accurately estimate the capacity consumption in each period. The computation of capacity consumption requires creating a detailed production schedule which is a complex task. Algorithms that find close to optimal schedules in a complex manufacturing environment are often time-consuming, which is impractical in a negotiation context. We investigate machine learning models to predict capacity consumption. We consider a flexible job-shop as commonly encountered in practice, and proposed several machine learning models. Namely, several variants of linear regression, decision trees, and artificial neural networks. Numerical experiments showed that our models outperform those found by both an exact approach and dispatching rules when computation time is short. [ABSTRACT FROM AUTHOR]

Reconfigurable manufacturing systems (RMS) intend to bridge the gap between dedicated and flexible manufacturing systems. If the literature is mainly focused on the design step and tactical planning of such systems, few research projects have addressed scheduling at the operational level. While setup times may occur in flexible manufacturing systems, reconfiguration times considered in RMS may affect several resources at once, and hence require specific modelling and solving approaches to be considered. This paper first formalises the problem at hand through integer linear programming. An iterative search method is then provided to obtain solutions to larger-scale instances. Results obtained on generated instances show that managing even few possible configurations can yield significant improvements in solutions' quality. Meanwhile, the extended search space implied by the increase in available configurations hinders the convergence to a good solution in a reasonable computation time, which suggests further investigations. [ABSTRACT FROM AUTHOR]

This work presents four mixed integer linear programming (MILP) models for the assembly flow shop problem in order to minimize the makespan. This production environment has two stages: production and assembly. The first stage consists of different machines designed to manufacture parts of a product. The second stage is intended for a final assembly. The performance measure considered is highly essential for industries from different segments, as it focuses on the best use of the time available for production. Statistical analysis with different tools was used to assess the performance and efficiency of mathematical models, emphasizing the analysis of performance profiles. Results showed that mathematical models are efficient, and the position-based model presented the best results for small and large instances during computational experimentation. All mathematical models can be used as direct tools in decision-making for the production sequencing problem in the approached environment. [ABSTRACT FROM AUTHOR]

We analyse the joint scheduling of spare parts production and service engineers in a multi-site maintenance system. In the system, for each failure, a service engineer with the required spare part needs to be allocated for on-site maintenance. If one of the required resources is not readily available, the maintenance task can be satisfied via an external channel with quick responsiveness but high involved costs. Service providers need to make an efficient plan, including production scheduling of spare parts, allocation of service engineers and outsourcing strategy. First, a mathematical model with the objectives of minimum total outsourcing costs and minimum weighted tardiness is formulated. Then, a novel and knowledge-based heuristic algorithm is developed, namely progressive Pareto algorithm based on step-size (PPAS). To verify the performance of PPAS, it is compared against other well-known algorithms, including NSGA-II, MOEA/D, NNIA, RIPG, MMOIG and MNNIA. Finally, the effectiveness of the joint scheduling mode is demonstrated by comparing it with the separate optimisation mode of spare parts production and workforce allocation. Highlights Analyse the joint scheduling of spare parts production and service engineers Construct a bi-objective optimisation model Develop a progressive Pareto algorithm based on step-size Verify the superiority of the proposed algorithm and joint scheduling mode [ABSTRACT FROM AUTHOR]

We study a common due-date assignment problem on two parallel uniform machines. The jobs are assumed to have identical processing times, and job-dependent and asymmetric earliness and tardiness unit costs. The scheduler may process only a subset of the jobs, i.e. the option of job-rejection is allowed. The objective function consists of three cost components: the total earliness-tardiness cost of all scheduled jobs, the cost of the common due-date, and total rejection cost. For a given number of rejected jobs and a given due-date, the problem is reduced to a non-standard linear assignment problem. Consequently, the optimal solution is shown to be obtained in polynomial time in the number of jobs. The case of a given (possibly restrictive) due-date, the extension to a setting of more than two machines, and a number of special cases, are also discussed. [ABSTRACT FROM AUTHOR]

This paper addresses the semi-online scheduling problem of minimising the total weighted completion time on a single machine, where a combination of information on jobs release dates and processing times is considered. In this study, jobs can only arrive at known future times and a lower bound on jobs processing times is known in advance. A new semi-online algorithm is presented and is shown to be the best possible for the considered problem. In order to make this statement, a new lower bound on the competitive ratio of any semi-online algorithm for the problem is developed and, using competitive analysis, the proposed semi-online algorithm is shown to have a competitive ratio that matches the lower bound. [ABSTRACT FROM AUTHOR]

Production companies are facing increasing production complexity and demographic change. Therefore, with its decreasing physiological and mental performance prerequisites, the aging workforce is often confronted with overload at the workplace leading to fatigue and work errors in the short term and increasing sick days in the long term. Integrating physiological and mental aspects into existing employee scheduling is one way to maintain employees' health and performance. Incorporating these aspects is often very complex due to the need for multiple disciplines and the lack of supporting methods and tools. Therefore, this paper provides a practical-oriented overview for including physiological and mental aspects in existing employee scheduling. The intercorrelation between workload, individual performance prerequisites and strain, their measurement methods, and standardised alternatives are explained to support industrial practitioners in production management, like planners. A four-step method for integrating physiological and mental aspects in employee scheduling is presented. With the help of this method, the interaction between the ergonomics/human factors departments and production management can be improved, resulting in a more human-oriented employee scheduling. A practical example from manual assembly illustrates the application of the presented approach. [ABSTRACT FROM AUTHOR]

Inspired by real-life applications, mainly in hand-intensive manufacturing, the incorporation of learning effects into scheduling problems has garnered attention in recent years. This paper deals with the flowshop scheduling problem with a learning effect, when minimising the makespan. Four approaches to model the learning effect, well-known in the literature, are considered. Mathematical models are providing for each case. A solver allows us to find the optimal solution in small problem instances, while a Simulated Annealing algorithm is proposed to deal with large problem instances. In the latter, the initial solution is obtained using the well-known Nawaz-Enscore-Ham algorithm, and two local search operators are evaluated. Computational experiments are carried out using benchmark datasets from the literature. The Simulated Annealing algorithm shows a better result for learning approaches with fast learning effects as compared to slow learning effects. Finally, for industrial decision makers, some insights about how the learning effect model might affect the makespan minimisation flowshop scheduling problem are presented. [ABSTRACT FROM AUTHOR]

We consider a single-machine scheduling problem with due-date assignment and stochastic processing times, where only the mean and support (i.e. an interval bounded with lower and upper values) of processing times are known to the decision maker. The objective is to jointly determine a scheduling policy and a set of due dates for all jobs, so as to minimise the total expected individually weighted costs of earliness, tardiness and due-date assignment. By identifying an upper bound with the robust optimisation approach and a lower bound, and using a linear function of them to approximate the studied objective function, we establish an approximated problem. Then, a branch-and-bound algorithm is proposed to find an optimal solution for the approximated problem. Finally, a series of computational experiments are conducted to examine the performance of problem approximation and two developed heuristic algorithms. HIGHLIGHTS Study due-date assignment scheduling problem with stochastic processing times. Apply the mean and support (interval data) to model stochastic processing times. Use a linear function of identified lower and upper bounds to make an approximation. Derive optimal due-date assignment and develop branch-and-bound algorithms. Evaluate the efficiency of branch-and-bound algorithm and heuristic algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, the manufacturing sector needs higher levels of flexibility to confront the extremely volatile market. Accordingly, exploiting both machine and workforce reconfigurability as two critical sources of flexibility is advantageous. In this regard, for the first time, this paper explores an integrated production scheduling and workforce planning problem in a Reconfigurable Manufacturing System (RMS) benefiting from reconfigurable machines and human-robot collaboration. A new Mixed-Integer Linear Programming (MILP) model and an efficient Constraint Programming (CP) model are developed to formulate the problem, minimising the makespan as the performance metric. Due to the high complexity of the problem, the MILP model cannot handle large-sized instances. Hence, to evaluate the performance of the CP model in large-sized instances, a lower bound is derived based on the relaxation of the problem. Finally, extensive computational experiments are carried out to assess the performance of the devised MILP and CP models and provide general recommendations for managers dealing with such a complex problem. The results reveal the superiority of the CP model over the MILP model in small- and medium-sized instances. Moreover, the CP model can find high-quality solutions for large-sized instances within a reasonable computational time. [ABSTRACT FROM AUTHOR]

This paper studies the customer order scheduling problem in the context of additive manufacturing. The study discusses an integrated problem involving the nesting of parts as well as the scheduling of batches of nested parts onto unrelated parallel machines. A mixed-integer programming model is presented, based on existing formulations from the literature, that integrates different materials and sequence-dependent setup times. Additionally, a metaheuristic based on an iterated local search is proposed for the problem configuration under consideration. Focusing on minimizing the total weighted tardiness of orders, the efficiency of the heuristic approach is evaluated using comprehensive test data. Further, we show the importance of the considered order-related objective by using qualitative analysis. [ABSTRACT FROM AUTHOR]

This paper delves into the impacts of an ongoing global crisis on the resilience of supply chains. Furthermore, it proposes measures to address and mitigate the disruptions caused by the prevailing uncertainties. For example, while the economy has started to recover after the pandemic and demand has increased, companies have not fully returned to their pre-pandemic levels. To enhance their supply chain resilience and effectively manage disruptions, one viable strategy is the implementation of flexible/hybrid manufacturing systems. This research is motivated by the specific requirements of Vestel Electronics, a household appliances company, which seeks a flexible/hybrid manufacturing production setup involving dedicated machinery to meet regular demand and the utilisation of flexible manufacturing system (FMS) to handle surges in demand. We employ a scenario-based approach to model demand uncertainty, enabling the company to make immediate and adaptive decisions that take advantage of the cost-effectiveness of standard production and the responsiveness of FMS. To solve the problem, we propose a heuristic algorithm based on column generation. The numerical results demonstrate that our optimisation model provides solutions with an average optimality gap of less than 6% while also reducing the average cost of standard production schemes without FMS by over 12%. [ABSTRACT FROM AUTHOR]