Operation Process Research Articles

Critical Success Factors for the Widespread Adoption of Virtual Alternative Dispute Resolution (VADR) in the Construction Industry: A Structural Equation Modeling Analysis

This study explores the increasing adoption of virtual alternative dispute resolution (VADR) in the construction industry, enhancing efficiency and accessibility in dispute resolution. VADR is crucial for streamlining processes and reducing participation barriers. The study aims to investigate the critical success factors (CSFs) influencing the adoption of VADR in the construction sector. Given the rising importance of VADR technologies, understanding the key factors driving their acceptance is crucial. The background highlights the growing reliance on innovative technologies to boost operational efficiency and decision-making processes. The data for the study were collected using a cross-sectional design with online structured survey questionnaire (N = 97) from diversified construction industries. Using Smart PLS 4, structural equation modeling (SEM) was employed to test the validity, reliability, and proposed hypotheses of the study. The results showed that cost factors had the greatest impact on VADR acceptance, followed by user competence and training, procedural adaptability, and technological infrastructure. Logistical assistance and legal frameworks also had a considerable favorable impact. However, stakeholder buy-in had no significant influence on VADR implementation. The implications indicate that economic feasibility, logistical readiness, flexible procedures, supportive legal contexts, and user skills are significant factors for successful VADR integration. Governments and organizations should deploy VADR technologies to encourage innovation and operational improvement in the construction industry.

Energy features of CO2-cycles during oxygen combustion of methanol

The PURPOSE of the work is to determine the mass-flow characteristics of working substances and energy indicators of an installation based on a CO2 cycle with a two-stage pressure increase (Allam cycle) during oxygen combustion of methanol.METHODS. A research methodology is presented in which the mass-flow characteristics of working substances are determined based on the reactions of combustion and synthesis of methanol and electrolysis of water. These reactions are basic for ensuring operational processes in the installation. The method is based on the molar ratios of substances participating in reactions under stoichiometric conditions. Using the thermodynamic parameters of the cycle being implemented and at a given installation power, the consumption of the working fluid, the share of carbon dioxide renewal in the cycle, the amount of methanol produced in the synthesis unit, the amount of fresh methanol, the amount of hydrogen produced in the water electrolysis unit and the amount of oxygen are determined, necessary to ensure fuel combustion processes. At the same time, the amount of commercial hydrogen is determined. Electricity consumption for own needs is determined using regulatory methods and data from equipment manufacturers.RESULTS. The article shows that with similar values of thermal efficiency of CO2 cycles based on oxygen combustion of methane and methanol, the amount of carbon dioxide removed from the cycle for disposal is 11% less. It has been shown that a CO2 cycle operating on methanol is capable of producing commercial hydrogen simultaneously with electricity generation. The specific electricity consumption for hydrogen production is 22% less than for its production without combination with the CO2 cycle.

The Integral Management of the Wastewater Treatment Sector in Mexico Using a Circular Economy Approach

Wastewater treatment must be proactive and sustainable to facilitate an increase in the circularity of water. Therefore, the current approach, based on a linear cycle, must be replaced with a circular economy concept that implements strategies to address the different byproducts in the wastewater treatment sector. In recent years, Nuevo León, Mexico, has encountered high water stress levels, with its main water bodies presenting their lowest levels ever recorded. This study was focused on the wastewater treatment plant Monterrey, which treats the largest volume at the state level. Throughout its operation process, it generates different potential byproducts that are yet to be harnessed to fully. This study developed three proposals using a circular economy approach: the treatment of water for the industrial sector, the use of residual sludge as an organic fertilizer, and the cogeneration of energy from biogas. These proposals can potentially generate benefits regarding the three pillars of sustainability, yielding a closed cycle in the wastewater treatment sector at the national level.

Enhancing International Trade Security: Real-Time Risk Assessment in Brazilian Customs with Blockchain Technology

This study introduces an innovative approach by integrating blockchain technology with risk analysis methods for international trade. The practical application and tangible improvements observed in a real case study highlight the originality and value of this approach, offering a robust framework for future research and practical implementation. The security of exports presents a challenge for customs authorities, requiring the analysis of vast volumes of data from diverse sources and stakeholders. Blockchain technology offers a viable solution by facilitating the sharing of reliable and secure data among participants in the international supply chain. This study proposes a distributed risk analysis method based on Blockchain to detect and prevent fraud and irregularities in Brazilian exports. The technique aims to enhance efficiency and protection in customs clearance, providing real-time risk analysis feedback at various supply chain stages. The method includes four stages: data collection, risk classification, validation, and feedback. It employs a Model Form for Insertion into the Blockchain, the DBSCAN algorithm to identify out-of-pattern transactions (outliers) at the manufacturing stage, and a Monte Carlo simulation to analyze the volume and specific products exported from Rio Grande do Sul. We applied this method in a case study of beef exports from Brazil. Using this method led to significant improvements in processing times, operational costs, transparency, and security in the supply chain. The blockchain-based approach identified fraudulent activities, such as value tampering and undeclared goods, enhancing compliance and trust among trade partners. Implementing this method can significantly reduce operational costs and processing times in international trade while increasing security and transparency. This approach benefits customs authorities and private operators, promoting a more reliable and efficient commercial environment. Adopting blockchain technology in international trade can reduce fraudulent and illicit practices, promoting greater fairness and equity in commercial transactions. Increased transparency strengthens trust among various actors in the supply chain, fostering a sense of social responsibility.

Standby and inspection policy optimization in systems exposed to common and operational shock processes

Motivated by practical applications like data storage, product defect detection, medical imagining, and sensing, this paper puts forward a new inspected standby system model where only one element can be online operating and only one element can stay in the standby mode at any time. Both operating and standby elements are exposed to random common shocks, causing their deterioration and even failures. The operating element may also be deteriorated by random operational shocks. The system undergoes periodic inspections to determine the refill of the operating or standby element. A new optimization problem is formulated and solved to determine the inspection and standby element addition policy with the objective to minimize the expected mission cost (EMC) attributed to factors including system downtime, number of inspections, element modes and failures, element activation and mode transitions. A new and efficient system state transition-based numerical algorithm is proposed to evaluate the EMC. A case study of a standby sensor system is provided to demonstrate the proposed model and impacts of several cost parameters as well as shock rates on the EMC and the optimal inspection and standby element addition policy, leading to insightful managerial guidelines for the system design and operation.

A Numerical Study on the Flow Field and Classification Performance of an Industrial-Scale Micron Air Classifier under Various Outlet Mass Airflow Rates

In this study, the gas−particle flow field in a real-size industrial-scale micron air classifier manufactured by Phenikaa Group using 3D transient simulations with the FWC-RSM–DPM (Four-Way Coupling-Reynold Stress Model-Discrete Phase Model) in ANSYS Fluent 2022 R2 and with the assistance of High-Performance Computing (HPC) systems is explored. A comparison among three coupling models is carried out, highlighting the significant influence of the interactions between solid and gas phases on the flow field. The complex two-phase flow, characterized by the formation of multiple vortices with different sizes, positions, and rotation directions, is successfully captured on the real-size model of the classifier. Additionally, analyzing the effects of the vortices on the flow field provides a comprehensive understanding of the gas–solid flow field and the classification mechanism. The effect of the outlet mass airflow rate is also investigated. The classifier’s Key Performance Indicators (KPIs: d50, K, η, ΔP) and the constrained condition of the particle size distribution curve of the final product are used to evaluate the classification efficiency. The contributions of this work are as follows: (i) a simulation analysis of a real-size industrial-scale classifier is conducted that highlights its advantages over a lab-scale one; (ii) a comparison is conducted among three coupling models, showing the advancement of four-way coupling in providing accurate results for simulations of interactions between the gas phase and particles; and (iii) the particle size distribution curve performances of a classified product under different simulation models and outlet airflow rates are addressed, from which optimal parameters can be selected in the design and operation processes to achieve the required efficiency of an air classifier.

Design challenges and response plans for intelligent decision support systems for Korean nuclear power plants under normal and abnormal conditions

In nuclear power plants (NPPs), an intelligent decision support system (IDSS) aids the decision-making process of main control room operators. It achieves this by monitoring and diagnosing conditions, predicting progress, and providing preventive advice during both normal and abnormal operations. Despite ongoing research in Korea, few IDSS for NPPs have been effectively applied and verified. Furthermore, established designs and validation guidelines for IDSSs for Korean NPPs remain lacking. To address this gap, this study systematically identifies problems and corresponding countermeasures for applying artificial intelligence-based design and validation technologies, while complying with licensing regulatory standards. The aim is to develop a practical and effective IDSS. This study identifies design challenges in the practical application of fundamental IDSS technology, outlining necessary design and validation technologies. Additionally, it suggests technology-specific response plans for the identified design challenges.

Assessment of water quality variation: tool for effective water treatment system operations

Water quality and treatment systems are dynamic because they constantly undergo seasonal variations in water chemistry, varying plant operating conditions, and new environmental laws, among others. Because of this, proper monitoring is essential to ensure that the water supplied by the treatment system safeguards public health from waterborne diseases. Selected surface water quality parameters as inflow were obtained before treatment against the treated water for different hydrological periods (2009 – 2019) from a water treatment system to determine the trend in water quality variation, water quality index, and effectiveness of the treatment process. Each hydrologic year had varying concentrations of selected parameters for raw and treated water quality. The concentration values of pH, electrical conductivity, total hardness, calcium and magnesium hardness, chloride, and total dissolved solids of the natural source water were within the recommended limit. Turbidity concentrations were above the recommended value for each hydrologic year, values ranging from 14.65 – 57.98 NTU, and iron concentration was above the permissible for 2010 and 2012. Selected parameters were all within the threshold limit after treatment with a water quality index (WQI) ranging between 1.09 – 39.39, rated as good/excellent water quality. The treatment system operations were effective throughout the observation period. However, turbidity, iron and hardness should be tested more frequently during the operational and verification monitoring process.

Process mining-enhanced quality management in food processing industries

PurposeThe purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. The aim is to derive roots and patterns of quality deviations and non-conforming finished products as well as best practice facilitating employee training in the food processing industry. Thereby, a key focus is on recognising tacit knowledge hidden in event logs to improve quality processes.Design/methodology/approachThis study applied process mining to detect root causes of quality deviations in operational process of food production. In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.FindingsThe findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.Originality/valueProcess mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.

A theoretical framework for linking hospitals longitudinally: demonstrated using German Hospital Quality Reports 2016–2020

BackgroundIn longitudinal health services research, hospital identification using an ID code, often supplemented with several additional variables, lacks clarity regarding representativeness and variable influence. This study presents an operational method for hospital identity delimitation and a novel longitudinal identification approach, demonstrated using a case study.MethodsThe conceptualisation considers hospitals as evolving entities, identifying “similar enough” pairs across two time points using an automated similarity matrix. This method comprises key variable selection, similarity scoring, and tolerance threshold definition, tailored to data source characteristics and clinical relevance. This linking method is tested by applying the identification of minimum caseload requirements-related German hospitals, utilizing German Hospital Quality Reports (GHQR) 2016–2020.ResultsThe method achieved a success rate (min: 97.9% - max: 100%, mean: 99.9%) surpassing traditional hospital ID-code linkage (min: 91.5% - max: 98.8%, mean: 96.6%), with a remarkable 99% reduction in manual work through automation.ConclusionsThis method, rooted in a comprehensive understanding of hospital identities, offers an operational, automated, and customisable process serving diverse clinical topics. This approach has the advantage of simultaneously considering multiple variables and systematically observing temporal changes in hospitals. It also enhances the precision and efficiency of longitudinal hospital identification in health services research.

The future of AI: hospitality and tourism perspectives

ABSTRACT The Hotel Management School Leeuwarden and the European Tourism Futures Institute organised a webinar to discuss and explore how artificial intelligence (AI) will impact the hospitality and tourism industry in the future. The webinar brought together a panel of academics, including Professor Iis Tussyadiah from the University of Surrey, Professor Stanislav Ivanov from the Varna University of Management, and Frederik Jan van der Meulen from Hotel Management School Leeuwarden, to discuss the multifaceted applications of AI in the industry. The speakers shared their insights through a series of presentations, underscoring AI integration’s strategic, economic and sustainability aspects. AI is presented as a critical element in enhancing customer experiences, optimising operational processes, and shaping the industry’s future landscape. The speakers discussed the role of generative AI in improving the industry’s resilience, the economic implications of AI’s integration and the challenges and strategies for sustainable AI adoption. This webinar goes beyond traditional AI debates by analysing AI’s strategic function in future-proofing hospitality and tourism businesses, its economic impact and its role in sustainability. The speakers’ crucial insights can help the hospitality and tourism industry embrace AI, highlighting the delicate balance between innovation, ethics and sustainability. This webinar’s extensive discussion of AI as a tool for efficiency and industry transformation makes it valuable for academics and industry professionals.

MATLAB Combined with APDL Simulation in Reference to Y-shaped Steel Turnout Pipe

This paper takes a symmetric Y-shaped steel bifurcation pipe as an example, uses ANSYS finite element analysis software to carry out parametric modeling, grid division and load application, combined with MATLAB software to carry out operation processing, and finally analyzes the results and draws a conclusion.

A smartphone-based multichannel magnetoelastic immunosensor for acute aortic dissection supplementary diagnosis

Some biomarkers of acute aortic dissection (AAD) can be used for the potential supplementary diagnosis of AAD, such as C-reactive protein (CRP), smooth muscle myosin heavy chain (SmMHC), and D-dimer (D-D). However, the current measurement methods for common markers primarily rely on sophisticated instruments. The operation process is complicated, and the reagents used are expensive. To provide chronic disease monitoring and home self-examination services for potential AAD patients in real time, we developed a smartphone-based multichannel magnetoelastic (ME) immunosensing device to detect protein levels. Our immunosensor reduced the aforementioned restrictions and demonstrated excellent performance for the supplementary diagnosis of AAD. In this paper, we successfully combined the intelligent terminal with the hardware system to sample the resonance frequency shift (RFS) on the multichannel ME immunosensor. According to the target detection objects with their respective antibodies in the immune binding response, multiple experiments were conducted to detect multiple groups of samples, and we found that a CRP concentration, a SmMHC concentration, and a D-D concentration in the range of 0.1−100μg/mL, 1−4ng/mL, and 0.25−5μg/mL were linearly proportional to the RFS of the ME immunosensor, respectively. For CRP, SmMHC, and D-D, the sensitivities were 13.37Hz/μg∙mL‐1, 155.19Hz/ng∙mL‐1, and 332.72Hz/μg∙mL‐1, respectively, and the detection limits were 2.634×10‐3μg/mL, 1.155×10‐2ng/mL, and 3.687×10‐3μg/mL, respectively. The experiments demonstrated that the accuracy and stability of our device were comparable to those of the vector network analyzer (VNA, Calibration instrument).

MantaRay-ProM: An efficient process model discovery algorithm

Discovering the business process model from an organisation’s records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models.

Standing on FURM Ground: A Framework for Evaluating Fair, Useful, and Reliable AI Models in Health Care Systems

The impact of using AI to guide patient care or operational processes depends on the interplay between the AI model’s output, the decision-making protocol based on that output, the capacity of the stakeholders involved to take the necessary subsequent action, and the benefits and harms of the action taken. Estimating the effects of this interplay before deployment and studying it in real time after deployment are essential for bridging the chasm between AI model development and achievable benefits. To accomplish this, the Data Science Team at Stanford Health Care has developed a testing and evaluation mechanism to identify Fair, Useful, and Reliable AI Models (FURMs) by conducting an ethical review to identify potential value mismatches, running simulations to estimate usefulness, making financial projections to assess sustainability, conducting analyses to determine IT feasibility, designing a deployment strategy, and recommending a prospective monitoring and evaluation plan. The authors report on FURM assessments carried out to evaluate six AI model–guided solutions for potential adoption, spanning both clinical and operational settings, each with the potential to impact up to tens of thousands of patients each year. The authors describe the assessment process, summarize the six assessments, and share their framework, to enable others to conduct similar assessments. Of the six solutions assessed, two have moved into an implementation phase. The novel contributions of this effort — usefulness estimates determined by simulation, financial projections to quantify sustainability, and a process for carrying out ethical assessments — as well as the underlying methods and open-source tools, are available for other health care systems to use to conduct actionable evaluations of candidate AI solutions.

Analisis Pelaksanaan Tugas Ramp Handling dalam Menjaga On Time Performance (OTP) Pada Maskapai Garuda Indonesia di Bandara Soekarno Hatta Cengkareng

Soekarno-Hatta Airport is the main gateway for international and domestic air travel in Indonesia. As one of the busiest airports in Southeast Asia, Soekarno-Hatta Airport handles more than 60 million passengers per year (Soekarno-Hatta Airport Authority, 2023). According to the operational management theory by Suryanto (2020), efficiency in managing all operational processes, including ramp handling, is crucial to ensure smooth and punctual flights. With high traffic volume, the main challenge is to maintain On Time Performance (OTP) to minimize flight delays. This study aims to determine the implementation of ramp handling tasks during pre-flight activities, as well as to determine the obstacles experienced by Ramp Handling officers in pre-flight activities at Soekarno Hatta Cengkareng International Airport and to determine how efforts are made by PT Gapura Angkasa in improving the quality of On Time Performance (OTP) at Soekarno Hatta International Airport, Cengkareng. This study uses a qualitative method, namely through interviews, observations, and documentation of ramp handling personnel units, this study was conducted for 1 month at Soekarno Hatta International Airport The results of this study are that ramp handling officers at Soekarno-Hatta Cengkareng International Airport are responsible for checking the readiness of ground equipment, flight documents, and coordinating with the ground crew team. The use of checklists ensures that all pre-flight steps are carried out correctly and efficiently, which is important for smooth boarding and loading.

Risk Assessment of ZAP-X→ Radiosurgery System in a Quaternary Care Hospital Using FMEA Approach

Background and Aims: The overarching goal of this study is to conduct a robust risk assessment using the Failure Modes and Effects Analysis (FMEA) approach. By identifying potential failure modes and their potential impacts on patient care and hospital operations, this research seeks to provide actionable insights and mitigation strategies for the safe and effective implementation of the ZAP-X Radiosurgery System. The swift integration of advanced medical technologies, exemplified by the ZAP-X→ Gyroscopic Stereotactic Radiosurgery System, a computer-controlled machine that delivers radiation to an intended target in the brain, necessitates a comprehensive proactive risk assessment before the launch of the system to ensure the safety of patients, staff and operational processes. Method: Applying the FMEA methodology, we meticulously mapped all processes and sub-processes associated with the ZAP-X Radiosurgery System implementation. Our multidisciplinary team, through a discussion on ‘what can go wrong’, identified failure modes. Risks were prioritised based on severity, likelihood and detectability giving them a Risk Priority Number (RPN). The team proposed mitigation strategies, deploying controls to enhance safety and reliability.

Ethical AI in Healthcare: Balancing Innovation with Privacy and Compliance

Within the dynamic and ever-changing healthcare industry, Artificial Intelligence (AI) emerges as a powerful and revolutionary concept capable of transforming patient care, optimising operational processes, and improving clinical decision-making. Nevertheless, the incorporation of artificial intelligence (AI) into healthcare systems gives rise to substantial ethical considerations, especially pertaining to privacy and adherence to regulations. This work examines the ethical aspects of artificial intelligence (AI) in the healthcare sector, highlighting the importance of maintaining a harmonious equilibrium between technical advancement and strict compliance with privacy regulations and legal obligations. Moreover, the use of artificial intelligence in healthcare must adhere to legislative frameworks specifically created to safeguard the rights of humans. This encompasses not just compliance with current rules but also the proactive identification and resolution of developing legal norms as AI technology advances. Healthcare practitioners and artificial intelligence (AI) developers should cooperate to create explicit standards and procedures that conform to existing legislation and expected future rules.

Economic development through sustainable energy transition: the impact of machine learning-driven energy efficiency in industrial sectors

The transition to sustainable energy systems is a critical component in addressing global environmental challenges and fostering economic development. Industrial sectors, which are major consumers of energy and contributors to greenhouse gas emissions, play a pivotal role in this transition. Machine learning (ML) technologies have emerged as transformative tools for enhancing energy efficiency in these sectors, offering significant potential for economic and environmental benefits. This review explores the impact of ML-driven energy efficiency on economic development, focusing on its application within various industrial contexts. The integration of machine learning into energy management systems allows for advanced predictive analytics, real-time monitoring, and process optimization. ML algorithms can analyze large datasets to forecast energy demand, detect inefficiencies, and optimize operational processes, leading to substantial cost savings and energy reductions. For example, in manufacturing industries, ML-driven solutions have been employed to enhance predictive maintenance, optimize production processes, and reduce energy consumption, resulting in lower operational costs and increased productivity. Similarly, in industrial facilities such as chemical plants and data centers, ML has been used to streamline energy management and minimize waste, demonstrating its versatility and effectiveness across diverse industrial applications. The economic impacts of ML-driven energy efficiency are profound. By reducing energy costs and improving operational performance, industries can achieve significant financial savings, which in turn can be reinvested into further innovations and growth. Additionally, the adoption of ML technologies fosters job creation and skill development in the tech and energy sectors, contributing to broader economic growth. Industries that leverage ML for energy efficiency gain a competitive advantage, positioning themselves favorably in the market and tapping into new opportunities for innovation and expansion. However, the transition to ML-driven energy efficiency is not without challenges. Issues related to data availability and quality, high implementation costs, and industry-specific constraints must be addressed to fully realize the benefits. Policymakers play a crucial role in facilitating this transition by developing supportive policies and regulations that encourage the adoption of ML technologies in energy management. Machine learning offers a transformative approach to achieving energy efficiency in industrial sectors, with significant implications for economic development. By addressing the challenges and leveraging emerging opportunities, industries and policymakers can drive sustainable energy transitions that benefit both the environment and the economy. This review provides a comprehensive analysis of these impacts and offers strategic recommendations for advancing ML-driven energy efficiency in the industrial sector. Keywords: Economic Development, Sustainable Energy, Machine Learning, Industrial Sectors.

Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%.