STARC 2.0: An improved pert network simulation tool

Lean strategies have been developed to eliminate or reduce waste and thus improve operational efficiency in a manufacturing environment. However, in practice, manufacturers encounter difficulties to select appropriate lean strategies within their resource constraints and to quantitatively evaluate the perceived value of manufacturing waste reduction. This paper presents a methodology developed to quantitatively evaluate the contribution of lean strategies selected to reduce manufacturing wastes within the manufacturers’ resource (time) constraints. A mathematical model has been developed for evaluating the perceived value of lean strategies to manufacturing waste reduction and a step-by-step methodology is provided for selecting appropriate lean strategies to improve the manufacturing performance within their resource constraints. A computer program is developed in MATLAB for finding the optimum solution. With the help of a case study, the proposed methodology and developed model has been validated. A ‘lean strategy-wastes’ correlation matrix has been proposed to establish the relationship between the manufacturing wastes and lean strategies. Using the correlation matrix and applying the proposed methodology and developed mathematical model, authors came out with optimised perceived value of reduction of a manufacturer's wastes by implementing appropriate lean strategies within a manufacturer's resources constraints. Results also demonstrate that the perceived value of reduction of manufacturing wastes can significantly be changed based on policies and product strategy taken by a manufacturer. The proposed methodology can also be used in dynamic situations by changing the input in the programme developed in MATLAB. By identifying appropriate lean strategies for specific manufacturing wastes, a manufacturer can better prioritise implementation efforts and resources to maximise the success of implementing lean strategies in their organisation.

In this work, we address the multiple voltage scheduling problem to minimize power consumption under both timing and resource constraints. We develop a tabu search-based algorithm with a general vector representation of solution and an effective performance estimation. Moreover, our approach tends to solve a series of scheduling problems with respect to multiple voltage designs. Specifically, our method represents each solution by a vector of operation types and solves the scheduling subproblem corresponding to a certain solution vector. To get feasible solutions of the subproblem, a two-stage method is presented by first adopting the PLNSM algorithm [3] to generate delay assignment satisfying the timing constraint and then performing delay adjustment iteratively until the resource constraints are met. A heuristic estimation is introduced to predict the total power and resource usage of the neighborhood. Our proposed method achieves near-optimal solutions with only an average power increase of 0.84% compared with ILP for small-size designs, and a 24.1% reduction over a previous tabu search-based algorithm [7] for a set of benchmarks.

Code generation methods for DSP applications are hampered by the combination of tight timing constraints imposed by the performance requirements of DSP algorithms, and resource constraints imposed by a hardware architecture. In this paper, we present a method for register binding and instruction scheduling based on the exploitation and analysis of resource- and timing constraints. The analysis identifies sequencing constraints between operations additional to the precedence constraints. Without the explicit modeling of these sequencing constraints, a scheduler is often not capable of finding a solution that satisfies the timing, resource and register constraints. The presented approach results in an efficient method of obtaining high quality instruction schedules with low register requirements.

Nowadays, there appeared a tendency of expanding scope and scale of activities in the Arctic. This is accompanied by the increasing number of security threats and potential emergencies factors in the region. Arctic region development involves implementing the goals of reducing risks to global and regional security. An important role in implementing the strategy of creating a comprehensive security system in the Arctic belongs to efficient protection of population and territories, critically important and potentially dangerous facilities from natural and man-made emergencies. All dangerous factors characterizing emergencies require generation of a complex approach to ensure security in the Arctic territories, increasing the rescue services resource potential and providing monitoring the environment components state and pollution. This article identifies ways to solve the problem of strategic planning and deployment of forces and facilities when creating an integrated security system under resource constraints and dynamically changing situation in the Arctic. An organization chart of strategic resource planning for emergencies management in the Arctic was developed. A decision-making algorithm is proposed to elaborate forces and facilitate composition for the Arctic integrated search-and-rescue centers based on the developed chart. Achieving results in strategic planning involves elaboration of a program for developing forces and facilities of the integrated security system in the Arctic region on the basis of results obtained within the simulation process. The program should reflect step-by-step solution of strategic planning tasks in accordance with goals, resource and time constraints.

In this paper, a scheduling algorithm for multi-processor real-time systems with relative timing constraints and resource constraints is proposed. The proposed algorithm has the capability to satisfy any timing constraints expressed as inequalities in two variables and a constant. This implies that scheduling problems imposed on various timing constraints such as end-to-end timing constraints and/or relative timing constraints, which are known to be difficult to handle, can be solved. Since the problem addressed in this paper is a generalization of some NP-complete problems, it is difficult to solve efficiently. Thus, the goal of the authors is to derive a heuristic algorithm which performs well on many instances of the problem, although in some cases it will take enormous space and time. Experimental results show that the proposed algorithm achieves a success rate of 80% in 3 minutes of computation time.

This study explored the use of blended learning in a fashion design class, working within the constructivist orientation with a mixed methods case study and using a sequential exploratory approach in order to focus on the effectiveness of the blended learning design and how it is experienced by the participants. The case study was based on a group of second year fashion design students from a private design institution in South Africa, for the combined subject, pattern making and garment construction. The purpose of this research was to investigate the ways in which face-to-face learning and online learning could best be blended to facilitate instruction in this particular class. The study focused on establishing what combination of online and face-to-face delivery could be seen to be most effective in achieving the project learning outcomes within the time and resource constraints available for the combined pattern making and garment construction subject. A blended learning curriculum model (Huang, Ma and Zhang, 2008) was used to assist in preparation for the blended learning programme, with online learning delivery being effected by means of Google Classroom, the learning management system recommended by the private design institution. The sequential exploratory approach involved two projects, so that insights gained from the first project could be applied to modify the blended learning design for the second project, if and as required. Quantitative data were used to establish a baseline of students’ prior understanding of blended learning, pattern making and garment construction processes. Qualitative data were collected in the form of interviews, questionnaires and observations, allowing for a descriptive understanding of how students as well as the lecturer (who was the researcher) experienced the blended learning classroom, and how effective that blend was in achieving the desired learning outcomes; opportunities were also offered for reflection. The results revealed that, not only did blended learning delivery need to be applied in a developmental way, with ongoing adjustments to accommodate best teaching/learning practice, but also that different fashion ‘briefs’ (that is, the tasks involved) might require modifications to the blended learning delivery design. This suggests that there is no ideal ‘blend’ in blended learning, but that, like other forms of learning, it is exploratory, tentative and constantly evolving. It is hoped that this study will offer insights into how face-to-face and online learning can be combined to best facilitate the learning process in this particular case, where practical assistance with students’ individual design projects traditionally requires a large amount of the instructional time available. As many vocational university subjects have similar time and resource constraints, particularly in facilitating practical application of theory at a professional standard, it is also hoped that, to some extent, the results will be generalizable to other disciplines.

A window-assisted video partitioning strategy for partitioning and caching video streams in distributed multimedia systems

This study aimed to explore what happened in the experiences of eight Grade 3-5 teachers when they provided small group strategies to students with learning disabilities at United Cerebral Palsy of Central Florida, Pine Hills. The research employed semi-structured interviews to investigate difficulties and perceptions. Teachers discussed positive effects on students' performance, engagement, and confidence. But there were many hurdles too, like managing a wide range of behaviors and skill levels, toggling between small-group and whole-group instruction, and meeting the emotional needs of each student. Resource constraints (material, technology, support personnel) only compounded these challenges. Thematic analysis highlighted the following themes: Classroom management, time constraints, different learning requirements, resource constraints, and social-emotional challenges. This study adds to the fund of knowledge by identifying the challenges and successes of small group strategies for LD. The importance of improving teacher preparation about inclusive practice, differentiation, and behaviour management, as well as providing more resources and support staff, is highlighted. Small group instruction significantly improves academic performance among students with learning disabilities by allowing for personalized instruction and increased engagement. However, its success depends on adequate support, including time, staffing, and behavioral strategies, to ensure effective and sustainable implementation. Continuous professional development and strong institutional backing are crucial to empower teachers in delivering dynamic, differentiated instruction tailored to diverse learner needs. Based on the conclusions, it is recommended that schools formally adopt small-group instruction as a core strategy in inclusive education and provide dedicated time and support staff for its implementation. Training in differentiated instruction, behavior management, and collaborative planning should be regularly conducted to equip teachers with inclusive practices. Adequate learning resources, assistive tools, and data-informed grouping strategies are essential to address diverse student needs. Lastly, applying the Enhanced Support Program for Students with Learning Disabilities (ESP-SLD) and fostering teamwork among educators, parents, and peers will strengthen inclusive efforts and improve student outcomes.

Customized Supply Chain Management (CSCM) has been made much account of attention in Flexible Manufacturing Systems (FSM). Products are customized by customer’s special needs at stochastic time. Then Manufacturing Oriented to Customization (MOC) would inevitably be faced by those enterprises that had to meet the changeable and complex needs of customers. However, there were large quantities of difficulties would appear in MOC supply chains, such as information overload, technology and resource constraints. All of these problems made it hard to quickly and effectively respond to customers’ needs. Therefore, we need to find a new way to deal effectively with the problems about how to respond quickly to supply chains oriented to customization. Petri Nets have been extensively used for modeling and simulating of the dynamics of flexible manufacturing systems. However, Petri Nets have not been very popular for molding of workflow in CSCM environmental. In this paper Stochastic Timed Petri Nets (STPN) were used to solve the above problems. The enterprises’ workflow was analyzed, and then the STPN model was constructed according to the characteristics of its internal supply chain. In order to minimize total tardiness time of workflow, the structure of STPN was optimized in a proper way. The results suggest that the STPN approach is also valid to minimize due date of each transition in CSCM and responses to customized needs more quickly than before.

An improved genetic algorithm based on reinforcement learning for aircraft assembly scheduling problem

In the rapidly evolving telecommunications industry, organizations face the challenge of ensuring that a diverse range of products perform effectively across various configurations. The emergence of new technologies, particularly in radio access and frequency bands, complicates testing and validation processes. Companies must strike a balance between comprehensive testing to guarantee quality and the constraints of time and resources, necessitating innovative approaches to enhance testing strategies. This thesis proposes a holistic framework that leverages insights from historical test data and domain expert knowledge to derive actionable insights, streamline processes, improve compatibility, and reduce costs while maintaining product quality in a competitive market. We employ various reverse engineering techniques, including deep learning-based natural language processing, to navigate the complexities of testing artifacts and extract a machine-readable mapping between information about test cases and the functions of the system under examination. This machine-readable feature mapping is then fed into an interpretable machine-learning engine to extract actionable insights. The research is structured into two phases: Phase 1: Data collection procedures focus on extracting information from diverse data sources using API access, analyzing test complexity, and mapping features to test cases through deep learning techniques. It involves identifying data sources and creating a mapping between features and test cases. Phase 2: Data analysis procedures analyze the feature mapping to extract actionable insights using interpretable machine learning techniques. This phase classifies mappings based on a classification target, enabling predictions, such as which product configuration combinations new test cases should prioritize. The combined insights from both phases contribute to developing an AI/ML-driven streamlining procedure that enhances testing efficiency through: -Optimized Configuration Selection: Classifying test cases by execution time helps identify configuration combinations that yield the shortest execution time while considering contextual relevance and interdependencies. - Effective Test Effort Allocation: Analyzing classified test cases with a specific classification target identifies test frequency imbalances and informs better resource allocation. -Test Gap Analysis: Data insights reveal gaps in testing coverage, highlighting under-tested configuration combinations. Through this structured approach, this thesis aims to bridge knowledge gaps, mitigate uninformed decision-making, and address challenges stemming from data silos and heterogeneity in test information.

Multi-project Time-cost Optimization in Critical Chain with Resource Constraints

The study of whether life exists, is extinct, or not depends on various sophisticated experimental studies, as many different signatures of life can be used. The experimental procedures that can be performed to identify life can be further restricted by time, resources, and mobility constraints. Therefore, any research analyzing the presence of extraterrestrial life must be precise and unambiguous. This research focuses on the objective of the extraterrestrial life detection domain and seeks to provide an efficient protocol that can produce life detection decisions based on empirical data obtained through chemical analysis under time and resource-constrained conditions. While the majority of existing frameworks in this field are designed to identify biomolecules, our goal is to accomplish the same with minimal operational expense and mission complexity. We argue that the thoughtful integration of multiple biomolecular detections with lesser complexity and a robust framework can improve overall mission performance by satisfying the necessary time and resource constraints. In this study, a rapid multiple biomolecules-based life detection protocol (MBLDP-R) from soil samples is developed from scratch and embedded in an operational scientific rover subsystem targeted for planetary analysis missions. The study uses artificial biomolecule samples and simulated extraterrestrial environments to illustrate the suggested protocol’s end-to-end process. First, we list a few significant biomolecules, including lipids, proteins, carbohydrates, nucleic acids, ammonia, and pigments. Then, a weighted qualitative test scoring is applied to sort out the best test method for the finally selected biomolecules which are used as operational analogue to showcase the protocol’s in-situ analysis and decision-making capabilities. Based on the suitable biomolecules, a scientific exploration subsystem is developed, and the implemented protocol is built to perform onboard sample analysis. Evaluation results show that: (1) the proposed MBLDP-R protocol could effectively predict the classes with an average f1-score of 98.65% (macro) and 90.00% (micro), (2) the area under the Receiver Operating Characteristics (AUC-ROC) curve shows the sample categories to be correctly predicted 92% of the time (97% for Extant, 88% for Extinct, and 92% in the case of NPL), and (3) the protocol is time-efficient with an average completion time of 17.60 min, demonstrating the protocol’s rapid nature in detecting biosignatures in soil samples. The research outcome yields useful additional data for related future studies, particularly in the design of scientific frameworks for mission-specific requirements with limited resources while also serving as a reference point for constraint evaluation methods for similar systems.

This study examined teachers' abilities to implement Prezi-assisted PowerPoint media in elementary classroom instruction within a rural school context. Despite technological availability, effective integration of interactive multimedia remains challenging, particularly in resource-limited settings where infrastructural and capacity constraints impede implementation. A qualitative descriptive case study was conducted at SD Inpres 1 Kaluku Tinggu, Central Sulawesi, Indonesia, involving a Grade V teacher selected through purposive sampling. Data were collected through classroom observations, semi-structured interviews, and documentation analysis, then analyzed using Miles and Huberman's framework involving data reduction, display, and verification. Findings revealed that the teacher demonstrated strong competence in instructional planning, classroom implementation, and formative assessment. Prezi-assisted PowerPoint significantly enhanced student engagement, with documented increases in student-initiated questions (47 versus 12 in conventional lessons) and participation rates. The teacher exhibited self-directed professional development, pedagogical reasoning, and adaptive strategies to overcome infrastructural challenges including limited internet connectivity and time constraints. The study confirms that effective technology integration depends primarily on teacher readiness and pedagogical skills rather than solely on infrastructural availability. Theoretically, findings extend multimedia learning and constructivist theories to authentic elementary contexts with resource constraints. Practically, results underscore the necessity for continuous professional development emphasizing pedagogical technology integration and institutional support systems addressing both technical and attitudinal barriers to sustainable innovation.

Web accessibility is the inclusive practice to overcome all barriers that prevent interaction with websites or web apps on the World Wide Web (WWW) by people with physical disabilities (cognitive/auditory/ visual limitation), situational disabilities (small screen, bright sunlight), and socioeconomic restrictions on bandwidth and speed. Tim Berners-Lee, W3 Director and inventor of the WWW, stated that “the power of the Web is in its universality, access by everyone regardless of disability is an essential aspect” (World Wide Web Consortium, n.d.). Web accessibility is a crucial aspect of modern technology and is playing an increasingly critical role in shaping a more equitable and accessible future for all users. Teaching web accessibility as a topic to undergraduate students in the Bachelor of Computing Systems (BCS) at EIT is not only a technical necessity (meeting legal requirements and enhancing user experiences) but also promotes inclusivity and fosters social responsibility. As a result, students are prepared for successful and rewarding careers in the technology industry. However, this also comes with challenges. These challenges may stem from various factors, such as technical complexities, time constraints, resource constraints, and lack of awareness and understanding. This article discusses in more detail some challenges to teaching this topic to undergraduate students. It then suggests how to use HTML/CSS/Javascript semantics to implement four web accessibility principles – perceivable, operable, understandable and robust (POUR).