Programming Model Research Articles

Development of an Evaluation Model for Industrial Internship Programs

Evaluation of industrial internship programs is crucial to assess their effectiveness and ensure alignment with the needs of the workforce. This study arises from several pressing issues, including the lack of competency alignment between vocational education and industry, significant gaps in the design and implementation of apprenticeship programs, and the reliance on conventional evaluation methods that lack technological integration. The research aims to address these challenges by developing a robust evaluation model tailored for industrial apprenticeship programs in Indonesia and Malaysia. Drawing upon established frameworks such as the CIPP model, Countenance Model, and Kirkpatrick evaluation model, this study incorporates innovative elements, including an adaptive, technology-driven evaluation system designed to enhance assessment accuracy and relevance. The novelty of this research lies in its focus on creating a link-and-match ecosystem between vocational education and industry, leveraging advanced evaluation tools to provide actionable insights for improving program quality and policy decisions. Using interviews, questionnaires, and field observations, data were collected from a diverse group of vocational university students, lecturers, and industry representatives. The findings not only validate the urgent need for an enhanced evaluation model but also demonstrate its potential to elevate the preparedness of graduates, equipping them with the skills and competencies required to succeed in the job market or as entrepreneurs. The final deliverables include a comprehensive evaluation framework, a technology-based assessment application, and a handbook to support stakeholders in adopting and implementing the model.

Developing an Assessment Model of the Independent Campus Internship Program Based on an Expert System

Improving the quality of vocational education is a critical priority to ensure that graduates possess the cognitive abilities, soft skills, and hard skills required by the modern workforce. However, significant challenges remain, such as the inability of graduates to meet workplace demands, outdated and conventional assessment methods, and the absence of expert system-based tools to evaluate the effectiveness of the Independent Learning Campus (MBKM) program. Additionally, the lack of comprehensive assessment guidelines provided by universities has further hindered the optimal implementation of this program. These issues underline the need for an innovative approach to address the gaps in assessment practices for the MBKM internship program. The objective of this research is to develop an expert system-based assessment model for the MBKM internship program that is valid, practical, and effective. The research leverages the Borg and Gall development model, focusing on creating a framework that integrates higher education assessments with industry-specific requirements. The resulting model facilitates theoretical and practical evaluations, using a website-based expert system that incorporates artificial intelligence to monitor and assess learning outcomes in real-time. The novelty of this study lies in its ability to bridge the gap between academic assessments and industry needs through an innovative, expert system-driven approach. This model provides a comprehensive assessment framework, including preparation guidelines, implementation strategies, assessment rubrics, and reporting mechanisms for lecturers, instructors, and students. By enhancing the alignment between vocational education and industry demands, this research offers a transformative contribution to the MBKM program and sets a foundation for further advancements in assessment technology.

Redesign of the Digital Literacy Program for Older Adults, validating Desirable Skills with Generative Artificial Intelligence

The research described in this article aims to evaluate and redesign the digital literacy program for older adults by validating desirable skills with AI. The same redesign is based on the execution of the specific objectives of this research, highlighting among them: 1) identifying the training requirements so that older adults can break the digital gaps in access and use of ICTs; 2) redesign the digital didactic management model of the digital literacy program and 3) validate the redesign proposal by incorporating feedback with artificial intelligence. The applied methodology is based on the analytical observation of three years of experience of the digital literacy program, which takes into account the virtualization factor of the learning experience, to motivate the transformation of the digital culture of older adults, taking into account The validation of the thematic axes includes comparisons made against the results of queries to information search and analysis tools based on generative artificial intelligence. The results have led to maintaining references of the experience worldwide, but also enriched with its own factors and lessons learned that make the digital literacy experience for older adults in Panama innovative.

Learning from the community: iterative co-production of a programme to support the development of attention, regulation and thinking skills in toddlers at elevated likelihood of autism or ADHD

Programmes designed to support children with known, or increased likelihood of, autism or ADHD often focus on reducing behaviours central to a clinical diagnosis. However, supporting children to pursue their own goals and cope with everyday life through fostering executive function (EF) development, without enforcing neuro-normative assumptions, may be more acceptable to neurodivergent people, and more beneficial. The co-production process for this neurodiversity-affirming programme involved: Review of research priorities identified during published public-and-clinician consultations; iterative programme development through two pilot rounds with a general community sample; and consultation with stakeholders (parents with a connection to autism or ADHD, alongside early years specialists, psychologists and therapists) to check acceptability of the proposal, and refine the logic model and materials. The logic model for the resultant programme—Supporting Toddlers with a connection to autism or ADHD to develop strong Attention, Regulation and Thinking skills (START)—involves three mechanisms of change: The child has appropriate play-based opportunities to practise EF skills; Parenting behaviours linked to strong EFs are encouraged; Parents are empowered to improve environmental-fit for their child so that EF stressors are reduced.

Tool wear prediction based on XGBoost feature selection combined with PSO-BP network

To address the challenge of accurately capturing tool wear states in small sample scenarios, this paper proposes a tool wear prediction method that combines XGBoost feature selection with a PSO-BP network. In order to solve the problem of input feature selection and parameter selection in BP neural network, a double-layer programming model of input feature and parameter selection is established, which is solved by XGBoost and PSO. Initially, vibration and cutting force signals from CNC machining are preprocessed using time-domain segmentation, Hampel filtering, and wavelet denoising. Subsequently, time-domain, frequency-domain, and time–frequency domain features are extracted from the preprocessed data using FFT and wavelet packet decomposition, followed by feature screening for tool wear mapping via Pearson correlation and XGBoost feature importance analysis as model input. Finally, PSO is employed to optimize BPNN parameters. Experimental results show that PSO outperforms other algorithms in training the tool wear prediction model, with XGBoost feature selection reducing model construction time by 57.4% and increasing accuracy by 63.57%, demonstrating superior feature selection capabilities over Decision Tree, Random Fores, Adaboost and Extra Trees. These findings suggest that the proposed method can effectively predict tool wear in real-world CNC machining, contributing to improved production efficiency, reduced tool replacement frequency, and lower maintenance costs, thereby providing valuable insights for industrial applications.

A Mixed Integer Linear Programming Model for Rapid Rescheduling in Ship and Offshore Unit Design Projects

Shipbuilding and offshore projects frequently require schedule adjustments due to unforeseen factors such as material supply delays, technical issues, and adverse weather conditions. These adjustments are often managed manually, resulting in significant time consumption and an increased risk of human error. Unlike production scheduling, little attention has been given to design scheduling, particularly in the context of rescheduling. To address this gap, this paper presents an optimization model that automates the rescheduling process for shipbuilding and offshore unit design projects. The model generates updated schedules that accommodate necessary changes while minimizing deviations from the initial schedule. In a real-world case involving 857 tasks, the model generated a schedule in under one second, preserving approximately 80% of the original schedule and achieving a 20% improvement in adherence compared to the original scheduling method. Furthermore, the model demonstrated exceptional scalability by efficiently generating optimized schedules for 108,700 tasks in under three minutes. These results demonstrate the model’s capability to provide rapid, efficient, and scalable rescheduling solutions, enabling quick and iterative refinement processes.

A Mathematical Programming Approach for Joint Optimization of Wind Farm Layout and Cable Routing Based on a Three‐Dimensional Gaussian Wake Model

ABSTRACTWind energy is currently one of the most promising alternative energy sources. The optimization of the wind farm layout and the cable layout are two important elements in the design of wind farms. Since increasing the distance between turbines can reduce wake loss but increase cable cost, these two optimizations are coupled and jointly affect the revenue of wind farms. In this paper, we propose a novel nonlinear mathematical programming model based on the 3D Gaussian wake model and use a mathematical programming approach to optimize the layout of the wind farm and the cable layout together, considering both power generation and cable cost. In this method, some of the constraints were linearized to facilitate the solution process. The optimization results show that profit increased by 9.07% when using annual economic efficiency as the objective function, compared with using energy production as the objective function.

Market-based or policy strategies to increase the cultivation of legumes for livestock? Effects on the cropping pattern and economic performance

Legume crops, the production of which is related to their use in animal nutrition (referred to in this study as “legumes for livestock—LL” and include species such as bean, lupin, pea, vetch and clover), have positive effects on the economic performance of farms and environmental outcomes, although their relation to sustainable weed management practices (SWMP) has received limited attention. The expansion of LL in Europe is limited due to market constraints, despite important policy incentives. This study was designed to investigate whether a policy-oriented Scenario (S1) or a market-based Scenario (S2) could induce the expansion of the cultivation of LL as well as to examine the possible impact on the economic performance of farming systems, including cost savings related to pest management. A farm typology was developed according to the level of expenses of farms for crop protection and their implemented SWMP. Using technical and economic data from 45 farms that cultivate annual arable crops in Greece, with special emphasis on LL, a Linear Programming model was constructed to examine the potential role of each one of the three identified farm types in the expansion of LL in Central Greece (Baseline Solution—BS). Following the BS, the effects of the two Scenarios were examined through a Price Parametric Programming approach. The solutions revealed that the valorization of LL had a significant economic potential for all farm types. In addition, it was found that a substantial policy incentive of increasing the coupled payments of LL by 128% (S1) can have an equivalent impact on the expansion of LL as modest levels of market interventions—through the achievement of higher yields or/and higher selling prices—to increase the revenues of LL by 59% (S2). Farms that relied more on chemical pesticides could play a more decisive role in expanding LL. The novel aspect is related to the investigation of the economic performance of different farm types cultivating LL with a specific focus on potentially higher profitability related to SWMP. Furthermore, this study contributed to the “policy versus market solutions” debate toward the expansion of LL.

Optimal operation and offering strategy for profit‐seeking of a coal‐mine energy prosumer in a liberalised market

AbstractCoal mines consume electricity from grids while making use of various derived energy resources to generate heat and power. This allows coal mines to act as energy prosumers. This paper presents an integrated optimisation model of optimal operation and offers strategies for profit‐seeking of a coal‐mine energy prosumer in a liberalised market. The discussed problem is formulated as a tri‐stage bi‐level programming model. In the upper level, the coal mine‐integrated energy system (CMIES) operator decides the retail contract management, equipment operation plan, and retail electricity price offered to end‐users through three stages to maximise its expected profits at a predefined risk level. In the lower level, the end‐users react to the price bids offered by the CMIES operator under study and other market retailers to minimise their costs for energy procurement. Unlike previous approaches, the endogenous uncertainties associated with market subjects' strategic behaviours are explicitly considered. To solve the proposed problem efficiently, the Karush–Kuhn–Tucker conditions and multicut benders decomposition method are employed. The simulation results show that the collaborative optimisation of the energy cycle and production scheduling can reduce daily operating costs by nearly $3576 and carbon emissions by 6.72 tons, which verifies the effectiveness of the proposed method.

A Markov decision optimization of medical service resources for two-class patient queues in emergency departments via particle swarm optimization algorithm

In the modern healthcare system, the rational allocation of emergency department (ED) resources is crucial for enhancing emergency response efficiency, ensuring patient safety, and improving the quality of medical services. This paper focuses on the issue of ED resource allocation and designs a priority sorting system for ED patients. The system classifies patients into two queues: urgent and routine. Considering different service rates, a multi-server preemptive priority queueing model and a multi-server non-preemptive priority queueing model are constructed. Additionally, the number of beds, K, is introduced as the capacity of the urgent queue. By comprehensively considering the costs associated with patient waiting time, the cost of rejecting the most critical patients, and the total costs of beds and servers, a mixed-integer programming model was constructed with the objective of minimizing the total cost. The particle swarm optimization algorithm was applied to determine the optimal number of servers, service rate, and number of beds. Compared with the model proposed by Alipour-Vaezi et al., our model significantly improves patient waiting times and queue lengths using the same data set: the waiting time decreased by 74.44%, by 5.79%, and by 1.13%; the queue length decreased by 78% and by 3.33%. Our model effectively reduces patient waiting times and queue lengths while controlling costs, identifies the optimal number of beds, and achieves optimized resource allocation. Finally, we conducted a sensitivity analysis and provided some valuable management insights.

Welfare-oriented and public health-aware disaster sheltering network design

While evacuation along with opening public shelters is vital to save the lives of disaster survivors, many of them run the risk of refusing evacuation thereby jeopardising their safety. The primary reason for taking such a risk is the inferior conditions commonly experienced in public shelters, including the noise of crowds, constant lighting, privacy concerns, and safety apprehensions, combined with the absence of other viable sheltering options. Moreover, the potential transmission of infectious diseases, such as tuberculosis and COVID-19, deters disaster victims further from seeking refuge in these public shelters. In this study, we propose a four-echelon network design to allow for small capacity shelters that can enhance welfare and public health. A two-stage stochastic mixed-integer linear programming model is formulated to optimise the selection of opening nodes in each of the four echelons under different disaster scenarios, in addition to determining the logistical operations. This model considers the tradeoff between maximising total welfare-enhanced sheltering occupancy and minimising associated costs. A novel problem-specific scenario-based cutting-plane algorithm is developed to efficiently solve the model while providing practical advantages. The effectiveness of our developed approach is demonstrated through a case study in Texas – the most disaster-prone state in the United States.

Factors Related to Drug Abuse as Perceived by Maranao College Freshmen Students of Mindanao State University

This study aimed to investigate the perceptions of Maranao freshman college students at Mindanao State University (MSU) regarding drug abuse. It examined selected factors related to drug abuse, which serve as the baseline for formulating a training model for the guidance program. The study also explored the relationship between drug abuse and specific factors as perceived by the students. The study sought to answer the following questions: 1. What is the profile distribution of respondents in terms of sex, age, and civil status, knowledge of someone who is a drug user, number of drug users known, and their relation to the user? 2. What is the profile distribution of first-year college students regarding their perceptions of factors affecting drug abuse in terms of relief from problems, sense of security, curiosity, and "pakikisama"? or peer pressure. 3. What are the most common drugs used by drug abusers as perceived by the respondents? 4. Is there a significant relationship between drug abuse and the factors in terms of relief from problems, curiosity, "pakikisama" or sense of security, peer group influence, and pleasure? 5. What training model for a guidance program can be formulated from the findings of the study? The Maranao respondents were college freshmen students of Mindanao State University selected through random sampling. To gather the necessary data, a questionnaire was distributed, and the responses were analyzed using frequency and percentage computations.

Digital Didactic Competencies of Prospective Teachers in Arabian Countries

Objectives: This research aimed to identify the reality of digital didactic competencies of preserve teachers in different Arab teacher education systems in the Arabian context. Method:The researchers followed the descriptive and analytical approach to identify the reality of preserve teachers’ mastery of digital didactic competencies. The research utilized a questionnaire that was validated and used for the purpose of the research; it consisted of five areas and 50 items. Participants of the research were 530 teachers from the Arabian educational contexts. Results and Discussion: Results of the research showed that three areas of digital competencies were reported to be Mid (AI for lesson planning 1.68, AI for classroom management 1.77, and AI for assessment of learning 1.82). Results also showed that AI for Conducting a Lesson (2.80) was reported to be High, while AI for Methodology (1.29) was reported to be low. Based on the obtained results, the study recommends integrating various digital competencies-oriented models in pre-service teacher education programs in the Arabian context. Research Implications: Extensive decisions related to curriculum reform and syllabus-oriented practices are recommended to fulfill the indulgence of pedagogical digital literacies in the Arabian educational contexts. Originality/Value: The study highlighted the need to integrate further pedagogical digital competencies components in pre-service teacher education programs due to the age requirements. Teacher education institutes in the Arab world place a greater emphasis on " digital pedagogy" than they did on imparting specific knowledge that students would eventually be expected to apply.

Multi-Period Operational Modelling and Optimization for Large-Scale Natural Gas Networks Considering Linepack Functions in Long-Distance Transmission Pipelines

As a promising energy resource, offshore natural gas is primarily used for power generation. The comprehensive offshore gas-to-power system, which includes extraction, treatment, compression, pipeline transmission, and power generation, is extensive and operates within various regulatory, operational, and financial constraints. This complexity offers opportunities to optimize one or more system operations to enhance profitability while fulfilling user demands and environmental considerations. In this research, we present a model-based, computer-aided framework that intuitively connects upstream natural gas operations with downstream power generation and distribution. We develop a multi-period Mixed-Integer Nonlinear Programming (MINLP) model that integrates gas treatment, compression, and long-distance transmission with power generation. The model combines first-principle mechanistic process models with a linepack model that calculates the gas volume storable in long-distance pipelines for transmission. The linepack model facilitates gas storage and withdrawal across different periods to accommodate demand scheduling. We apply this framework using the MINLP model in three scenarios: profit maximization, cost minimization, and supply-demand balancing using linepack. The results demonstrate improved economic performance for offshore natural gas-based power generation in China under varying periodic power demands.

Robust Co-Optimization of Medium- and Short-Term Electrical Energy and Flexibility in Electricity Clusters

The increasing penetration of distributed renewable energy sources introduces challenges in maintaining balance within power systems. Civic energy initiatives offer a promising solution by decentralizing balancing responsibilities to local areas, with energy clusters serving as an example of such communities. This article proposes a novel mixed-integer linear programming (MILP) model for optimizing the energy mix within a cluster, addressing both planned balancing (day-ahead) and unplanned real-time adjustments. The proposed approach focuses on mid-term decision-making, including the integration of additional wind energy sources into the cluster and the procurement of new demand-side response (DSR) contracts, that allow for short-term planned and unplanned balancing. While increased wind energy enhances the system’s renewable capacity, it also raises operational stiffness, whereas DSR contracts provide the flexibility necessary for effective system balancing. The model incorporates risk aversion by employing Conditional Value at Risk (CVaR) as a risk measure, enabling a nuanced evaluation of trade-offs between cost and risk. The interactive framework allows decision-makers to tailor solutions by adjusting confidence levels and assigning weights to cost and risk metrics. A representative numerical example, based on a typical energy cluster in Poland, illustrates the model’s applicability. This case study demonstrates that the model responds intuitively to varying decision-maker preferences and can be efficiently solved for practical problem sizes.

Scheduled lightpath demand provisioning in fiber space division multiplexing optical networks

Space division multiplexing (SDM) and spatial super-channel (Spa SCh) technology offer scalable solutions to meet the increasing capacity demands in optical networks. This study focuses on multi-fiber optical networks integrated with the innovative Spa SCh technology, which creates different granularities of Spa SChs by spatially combining the same spectrum across multiple fibers, known as fiber-SDM (FSDM). Most existing studies have concentrated on provisioning dynamic or static lightpath demands in FSDM networks. However, for several applications in real-world networks, scheduled lightpath demands—where setup and teardown times are pre-determined—are more common. To address this, we consider the provisioning of scheduled lightpath demands in FSDM networks and aim to solve the routing, fiber, spectrum, and time-slot assignment (RFSTA) problem for these demands. We propose two integer linear programming (ILP) models: one optimizing only the spectrum resource utilization (SRU) and the other jointly optimizing both SRU and capital expenditure (CAPEX). In addition, we develop several efficient heuristic algorithms for large-scale optical networks, based on the concepts of the spatial spectrum window (SSW), SSW plane, and time-slot window (TW). Furthermore, an evolutionary joint optimization strategy is introduced to explore optimal Spa SCh configurations, aiming to jointly optimize network SRU and CAPEX. Simulation results demonstrate that the proposed algorithms closely match the performance of ILP models in small networks. Among the heuristic approaches, the adaptive least-load algorithm performs best under an optimal Spa SCh configuration, which bundles half of the fibers to form link fiber bundles.

Optimizing Modular Vehicle Public Transportation Services with Short-Turning Strategy and Decoupling/Coupling Operations

In public transportation systems, the passenger demand during peak hours is characterized by over-saturation at intermediate stops and directional imbalances, and the traditional single scheduling strategy and fixed capacity cannot solve the contradiction between the demand and capacity mismatch. In order to accurately match demand and capacity, this paper proposes a method to optimize the service of a public transportation system by using a short-turning strategy combined with decoupled/coupled operation of modular vehicles (MVs). The short-turning strategy is used to alleviate the heavy passenger flow at intermediate stations, and the decoupling/coupling operations of MVs are employed to flexibly adjust the capacity levels in different directions. Considering urban space limitations, depots for storing modular units (MUs) are only set up at the starting and ending stations of bidirectional lines. MVs can not only adjust the departure capacity at the starting station but also consider whether to decouple/couple at turnaround stations for short-turning trips to achieve a more effective supply–demand match, with the decoupled/coupled MUs being deadheaded from or provided by the depot. We formulated this problem as an integer nonlinear programming (INLP) model, jointly optimizing the departure intervals of each trip, the capacity of MVs, the turnaround scheme for short-turning trips, and the decoupling/coupling scheme for MVs at turnaround stations, with the aim of minimizing passenger waiting time costs and vehicle operating costs. To facilitate a solution, we equivalently transformed some nonlinear terms in the model, which was then solved by the commercial solver Gurobi. The numerical study shows that, compared with the traditional full-length strategy combined with conventional buses, the model proposed in this paper can reduce the total system cost by about 19.59%. In particular, it can achieve precise matching between passenger demand and transport capacity, thereby reducing the passenger waiting time cost by about 29.99%. Compared with the full-length strategy combined with MVs, the total system cost is also reduced by about 14.65%. The research results contribute to enhancing the service quality and efficiency of public transportation systems, which is of great significance to the sustainable development of these systems.

Data-Driven Order Consolidation with Vehicle Routing Optimization

This study compares time-based and quantity-based consolidation strategies within the Vehicle Routing Problem (VRP) framework to optimize supplier profitability and logistical efficiency. The time-based model consolidates deliveries at fixed intervals, offering predictable routes, reduced customer wait times, and cost efficiency in stable markets. Conversely, the quantity-based model dynamically adjusts delivery volumes to meet fluctuating demand, providing flexibility in dynamic environments but potentially increasing long-term costs due to logistical complexity. Using a mixed-integer linear programming (MILP) model, sensitivity analyses, and scenario-based experiments, the study demonstrates that the time-based model excels in stable conditions, while the quantity-based model performs better in highly variable demand scenarios. These findings provide actionable insights for selecting consolidation strategies that optimize delivery operations and enhance supply chain performance based on market dynamics.

Implementation Of Convolutional Neural Network (Cnn) Based On Mobile Application For Rice Quality Determination

The purpose of this study is to design and build a CNN deep learning program modeling for a mobile application for rice quality classification and analyze the performance of a mobile application-based classification program as a means of halal information in real time. The method applied is an experimental method that utilizes machine learning technology by using many rice images that are used as datasets. The data of these images is classified by their shape, color and background. This image is used as a reference for the training dataset. After the CNN training model is formed, it is then set up in a web editor p5.js then an interface is created to connect to a server such as Google Cloud using FastAPI, which can be accessed using a mobile application or a web server such as Chrome. In the mobile application, create an interface to connect with the camera system and data base on the cloud server. The results of the study were obtained that CNN deep learning modeling can be used in real time. In web browser usage, the data shown is also affected by lighting. The accuracy level of the built model reached above 99.8 percent with a validation accuracy rate of 99.7 percent in the data training process. When testing, the average accuracy of the data was around 99.9 percent. This clearly proves that CNNs can be used to classify objects properly and accurately.

A hybrid decision programming model utilizing DEMATEL–ANP–VIKOR for digital energy business model portfolio: an environmental, social, and governance (ESG), and financial perspective

A hybrid decision programming model utilizing DEMATEL–ANP–VIKOR for digital energy business model portfolio: an environmental, social, and governance (ESG), and financial perspective