Search Results (63)

Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization methods from the field of operations research to improve the efficiency of service workshops for bus maintenance and repair. Based on an analysis of collected data using queueing theory, the authors assessed the current system performance and found that the queueing system still has spare capacity and could be downsized, which aligns with the company’s management goals. Specifically, the company plans to reduce the number of bus repair service stations (servers in a queueing system). The main question is whether the system will continue to function effectively after this reduction. Three specific downsizing solutions were proposed and evaluated using queueing theory methods: extending the daily operating hours of the workshops, reducing the number of arriving buses, and increasing the productivity of a service station (server). The results show that, under high system load, only those solutions that increase the productivity of individual service stations (servers) in the queueing system provide optimal outcomes. Other solutions merely result in longer queues and associated losses due to buses waiting for service, preventing them from performing their intended function and causing financial loss to the company. Full article

This manuscript addresses the issue of irrigation water management with high efficiency and effectiveness and focuses on systems associated with significant water losses, which is sprinkler irrigation. This article presents mathematical modeling that enables the application of precision irrigation using a gun sprinkler robot. The sprinkler robot was fabricated in the Faculty of Agriculture and Natural Resources workshop at As-wan University. The experiments were conducted using 12, 14, and 16 mm nozzle sizes and three gun heights, 1.25, 1.5, and 2 m, at three forward speeds, 25, 50, and 75 m/h. The results revealed that at nozzle 12, the actual wetted diameter would be less than the theoretical diameter by a percentage of 2–5%, while at nozzle 14, it ranged from 2 to 7%, but at nozzle 16, it increased from 6 to 9%. The values of evaporation and wind drift losses were always less than 2.8 mm. The highest efficiency was achieved at the lowest forward speed (25 m/h) and using a 1.5 m gun height. The highest water application efficiency was 81.8, 82.5, and 81.1% using nozzle 12, nozzle 14, and nozzle 16, respectively. Precise irrigation control using sensor and variable rate technology will be the preferred option in the future. Full article

Dueto uncertainties in real-world production, dynamic factors have become increasingly critical in the research of distributed flexible job shop scheduling problems. Effectively responding to dynamic events can significantly enhance the adaptability and quality of scheduling solutions, thereby improving the resilience of manufacturing systems. This study addresses the dynamic distributed flexible job shop scheduling problem, which involves random job arrivals and machine breakdowns, and proposes an effective discrete improved gray wolf optimization (DIGWO) algorithm-based predictive–reactive method. The first contribution of our work lies in its dynamic scheduling strategy: a periodic- and event-driven approach is used to capture the dynamic nature of the problem, and a static scheduling window is constructed based on updated factory and workshop statuses to convert dynamic scheduling into static scheduling at each rescheduling point. Second, a mathematical model of multi-objective distributed flexible job shop scheduling (MODDFJSP) is established, optimizing makespan, tardiness, maximal factory load, and stability. The novelty of the model is that it is capable of optimizing both production efficiency and operational stability in the workshop. Third, by designing an efficacious initialization mechanism, prey search, and an external archive, the DIGWO algorithm is developed to solve conflicting objectives and search for a set of trade-off solutions. Experimental results in a simulated dynamic distributed flexible job shop demonstrate that DIGWO outperforms three well-known algorithms (NSGA-II, SPEA2, and MOEA/D). The proposed method also surpasses completely reactive scheduling approaches based on rule combinations. This study provides a reference for distributed manufacturing systems facing random job arrivals and machine breakdowns. Full article

The workshop is a crucial site for ensuring the smooth operation of production activities within an enterprise, playing a significant role in its long–term development. A well–designed workshop layout can reduce material–handling costs during production and enhance the overall efficiency of the enterprise. This paper establishes a mathematical model for the workshop layout problem, aiming to minimize logistics transportation costs and maximize non–logistics relationships. Using a real–world case study, the White Shark Optimizer (WSO) algorithm is applied to solve the model. The results show that the transportation distance of the layout scheme obtained by the WSO algorithm is reduced by 381 m, 82 m, and 56 m, respectively, compared with the original layout, the Genetic Algorithm (GA), and the Sparrow Search Algorithm (SSA), and the non–logical relationship is increased by 24.84% and 1.6%, respectively. The layout scheme obtained by using the WSO algorithm is more excellent and can effectively improve the production efficiency of enterprises. Full article

Mass customization makes it necessary to upgrade production planning systems to improve the flexibility and resilience of production planning in response to volatile demand. The ongoing development of digital twin technologies supports the upgrade of the production planning system. In this paper, we propose a data-driven methodology for Hierarchical Production Planning (HPP) that addresses the upgrade requests in the production management system of a fuel tank manufacturing workshop. The proposed methodology first introduces a novel hybrid neural network framework with symmetry that integrates a Long Short-Term Memory network and a Q-network (denoted as LSTM-Q network) for real-time iterative demand forecast. The symmetric framework balances the forward and backward flow of information, ensuring continuous extraction of historical order sequence information. Then, we develop two relax-and-fix (R&F) algorithms to solve the mathematical model for medium- and long-term planning. Finally, we use simulation and dispatching rules to realize real-time dynamic adjustment for short-term planning. The case study and numerical experiments demonstrate that the proposed methodology effectively achieves systematic optimization of production planning. Full article

Ethnomathematics, as a field of study, promotes recognizing the diversity in ways of thinking and doing mathematics, challenging the hierarchies and exclusions typical of traditional mathematics education. This research explores the practice of juggling, specifically analyzing three-ball juggling sequences to uncover the mathematical structures and patterns embedded in this ancient art form. In a social association during a workshop, two jugglers and seven juggling learners interact with one of the researchers, a mathematics educator, to co-construct a shared model establishing a symmetrical dialogue based on the Alangui’s principles of “mutual interrogation” between the practice of juggling and the domain of mathematics. The knowledge exchange process is envisioned as a “barter” where both the mathematics educator and the jugglers contribute their unique perspectives to generate new and hybrid understandings. With a qualitative approach, from the analysis of the data collected during the ethnographic field work (notes, audiovisual recordings) emerges how the initial model, created by mathematicians and jugglers, was reinterpreted to better align with the cultural community’s practice. The research revealed that juggling serves as a concrete context for exploring abstract mathematical concepts and that mathematical analysis of juggling sequences helps jugglers gain a deeper understanding of underlying structures, enhancing their creativity. The hybrid model developed in this study offers a promising resource to integrating ethnomathematical perspectives into formal mathematics education, fostering a more situated and engaging learning experience for students. Full article

The pulsating assembly line is widely used in modern manufacturing, particularly in high-precision industries such as aerospace, where it greatly enhances production efficiency. To achieve overall optimization, both product scheduling and Automated Guided Vehicle (AGV) scheduling must be simultaneously optimized. However, existing research predominantly focuses on product scheduling, with limited attention given to AGV scheduling. This paper proposes an optimized solution for the pulsating assembly line scheduling problem, incorporating multifunctional AGV scheduling. A mathematical model is developed and three AGV selection strategies and three AGV standby strategies are designed to optimize AGV scheduling and control. To improve scheduling efficiency, nine heuristic strategies are introduced, along with the Variable Neighborhood Descent (VND) algorithm as a metaheuristic method for product scheduling. The VND algorithm refines the solution through multiple neighborhood searches, enhancing both the precision and efficiency of product scheduling. Our experimental results demonstrate that the proposed strategies significantly improve the production efficiency of pulsating assembly workshops, reduce AGV scheduling costs, and optimize overall production workflows. This study offers novel methods for intelligent scheduling in pulsating assembly workshops, contributing to the advancement of manufacturing toward “multiple varieties, small batches, and customization”. Full article

This paper provides a comprehensive guide for educators on how multirotor UAV platforms can be utilized to achieve various learning outcomes in undergraduate mechatronics education. This study is based on a PX4 ecosystem combined with the MATLAB Simulink programming environment, covering both hardware and software aspects to support engineering education. The paper explains (i) which learning outcomes can be obtained, (ii) how mathematical models can be derived and implemented in simulation software, (iii) which hardware components are essential, their approximate costs, and possible upgrades based on available budgets, and (iv) which experiments students can perform using the UAV platform. A proposed educational prototype integrates airframe parts produced using additive manufacturing technologies with standard multirotor components. Additionally, a series of experiments were designed, including extensive testing of the multirotor control module. Three learning outcomes related to UAV hardware were incorporated into the engineering curriculum, while two software-related outcomes were addressed through student workshops. Future plans include the implementation of multiple UAV platforms in the educational process to further enhance learning outcomes. Full article

This study investigated the value of simulation workshops designed to enhance motivational discourse between mathematics teachers and struggling students who have difficulty keeping up with the curriculum, especially in advanced mathematics. Grounded in the self-determination theory, we examined teachers’ motivational discourse by having them participate in simulated individual dialogues with students, with a focus on the differences in the motivational discourse with male and female students. Twenty-nine middle school mathematics teachers (89.6% female; mean experience = 9.4 years, SD = 8.7) participated in the online simulations, each of which presented a scenario where an actor portrayed a struggling student contemplating dropping out of math class. Based on the observational measures of motivational discourse, the findings reveal significant gender disparities in that teachers tended to provide more support and autonomy to male students. Moreover, they tend to direct more frequent and intense autonomy-suppressing behaviors toward female students. The results highlight the efficacy of simulation-based workshops in uncovering teachers’ hidden behavioral patterns. It also highlights the importance of simulation-based learning to tailor professional development issues and for addressing unconscious gender biases in mathematics education. Full article

To solve the problems of chaotic information management and difficult traceability in the manufacturing process of transformer coils, a traceability and management method oriented towards the manufacturing process of transformer coils has been proposed. This method integrates industrial internet identification resolution and extension of Petri net modeling theory. A comprehensive identification and resolution framework for coil manufacturing processes has been constructed. In this manuscript, the authors proposed an industrial data-sharing space based on the producer-consumer model with unified coding identification. This enables information sharing for all resources, including personnel, machinery, materials, methods, environment, and measurements. A method for modeling extensible identification primitives of coil manufacturing process information was proposed, which formalizes the correlation and data structure of process information. A Petri net model for the comprehensive acquisition and integration of elemental information in coil manufacturing processes, as well as a mathematical model for quality traceability, were constructed, thereby forming a complete path for quality traceability information. Finally, based on the method proposed above, a software and hardware environment for identification and traceability for coil manufacturing was established. Taking a certain type of coil as an example, validation was carried out; the results indicate a significant enhancement in the production management and information traceability capabilities of the coil production workshop. This study provides reference and guidance for the process traceability management of power equipment manufacturing. Full article

Intelligent scheduling of knitting workshops is the key to realizing knitting intelligent manufacturing. In view of the uncertainty of the workshop environment, it is difficult for existing scheduling algorithms to flexibly adjust scheduling strategies. This paper proposes a scheduling algorithm architecture based on deep reinforcement learning (DRL). First, the scheduling problem of knitting intelligent workshops is represented by a disjunctive graph, and a mathematical model is established. Then, a multi-proximal strategy (multi-PPO) optimization training algorithm is designed to obtain the optimal strategy, and the job selection strategy and machine selection strategy are trained at the same time. Finally, a knitting intelligent workshop scheduling experimental platform is built, and the algorithm proposed in this paper is compared with common heuristic rules and metaheuristic algorithms for experimental testing. The results show that the algorithm proposed in this paper is superior to heuristic rules in solving the knitting workshop scheduling problem, and can achieve the accuracy of the metaheuristic algorithm. In addition, the response speed of the algorithm in this paper is excellent, which meets the production scheduling needs of knitting intelligent workshops and has a good guiding significance for promoting knitting intelligent manufacturing. Full article

Craft and e-textile circuits are technologies that bridge the gender gap in Science, Technology, Engineering, and Mathematics (STEM) learning. Acknowledging the need to study girls’ underrepresentation in STEM, this article delves into the identity negotiations of four girls aged eleven to fourteen as they construct craft and e-textiles at a library makerspace. Qualitative analysis of their talk at the workshop found that several factors shaped the girls’ identity work, such as their awareness of their abilities and fellow participants’ projects, their understanding of parents’ expectations, and their strengths in other STEM domains. While all four girls reluctantly participated in making circuits, the reason for their reluctance varied from an interest in craft and the messiness of working with conductive thread to the preference for familiarity and complexity within other STEM domains such as programming and engineering. Further, as the girls questioned their need to engage in circuit-making, their preference for a particular identity became apparent. Overall, this study’s findings underscore the tensions in learning in technology-rich environments such as makerspaces, highlighting maker technologies’ affordances and limitations and emphasizing the need for a deeper understanding of what shapes learners’ participation and identities. Full article

Recognizing the omnipresence of mathematics across various contexts, this paper addresses the untapped potential of museums as rich venues for informal mathematics learning beyond traditional educational settings like classrooms. This paper presents the InformalMath program, designed for the professional development of primary and middle school teachers using integrating mathematics education within art and history museums through designing mathematics visit workshops. Specifically, the focus is placed on Phase 1 of the program, in which teachers participated in two informal mathematics education workshops at two museums in Turin, Italy, and were asked to reflect on their participation through a written essay. The analysis of the essays reveals significant engagement, appreciation of mathematics as a cultural artifact, and the emergence of creativity and inclusion among participating teachers. These findings highlight the benefits of such interdisciplinary approaches in enhancing mathematical understanding and pedagogical strategies. Conclusions emphasize the program’s success in not only enriching teachers’ instructional repertoire but also in promoting a more holistic, engaging, and contextualized approach to mathematics education, suggesting a promising avenue for future educational practices and research in informal learning environments. Full article

This article is a self-narrative of our 18-year research into the Technological Pedagogical Content Knowledge (TPACK)-guided professional development of teachers in ICT-enhanced mathematics learning. Using autoethnography as the methodology to elucidate our transformative personal evolution in implementing the TPACK model, we describe how we conceptualized and enacted the TPACK framework across three distinct phases of our research trajectory. In the first phase, our efforts focused on offering afternoon seminars and workshops on using educational software. Mathematics teachers attended the seminars and workshops voluntarily. In the second phase, we concentrated on designing programs guided by the principles of adult education, which emphasize the importance of learner autonomy and relevance, and socio-constructivist views of teacher professional growth, which stress the role of collaboration and reflection in learning. In the final phase, we adopted a systemic, school-based approach to investigating and expanding TPACK for mathematics and other STEM/STEAM teachers. At the end of each phase’s description, we delve into the profound lessons learned and how these led to a paradigm shift, expanding our perspective on TPACK as practitioners and researchers. Finally, we present a set of recommendations for future research and practice aimed at facilitating the sustainability of STEM/STEAM teacher professional learning initiatives. Full article

Traditional capacity forecasting algorithms lack effective data interaction, leading to a disconnection between the actual plan and production. This paper discusses the multi-factor model based on a discrete manufacturing workshop and proposes a digital twin-driven discrete manufacturing workshop capacity prediction method. Firstly, this paper gives a system framework for production capacity prediction in discrete manufacturing workshops based on digital twins. Then, a mathematical model is described for discrete manufacturing workshop production capacity under multiple disturbance factors. Furthermore, an innovative production capacity prediction method, using the “digital twin + Long-Short-Term Memory Network (LSTM) algorithm”, is presented. Finally, a discrete manufacturing workshop twin platform is deployed using a commemorative disk custom production line as the prototype platform. The verification shows that the proposed method can achieve a prediction accuracy rate of 91.8% for production line capacity. By integrating the optimization feedback function of the digital twin system into the production process control, this paper enables an accurate perception of the current state and future changes in the production system, effectively evaluating the production capacity and delivery date of discrete manufacturing workshops. Full article