Machine Selection Research Articles

An improved genetic algorithm with modified critical path-based searching for integrated process planning and scheduling problem considering automated guided vehicle transportation task

Integrated process planning and scheduling (IPPS) can take advantage of the complementary attributes of process planning and shop scheduling to obtain better production schemes and process routes improving the whole performance of the manufacturing system. Additional consideration of the shop logistics system including task assignment of automated guided vehicles (AGVs) can improve shop productivity while ensuring the smooth running of the whole manufacturing system. This paper investigates an IPPS problem considering AGV transportation task (IPPS_T). Compared with the original IPPS, IPPS_T addresses not only the process selection, operation sequencing, and machine selection but also the transportation task assignment of the AGVs. Therefore, it is much more difficult than the IPPS problem which has already been proven to be NP-hard. The paper proposes an integrated encoding method to improve the integration of the manufacturing system by representing the process route, shop scheduling scheme, and transportation task assignment plan simultaneously in one individual. This paper designs an improved genetic algorithm (IGA) combining a critical path-based neighborhood searching strategy which can ensure the effectiveness of local search on both AGVs and machines. The numerical experiments with different numbers of AGVs are conducted on the open instances which are extended from the well-known Kim benchmark. The results obtained by the IGA show significant advantages proving the effectiveness of the proposed encoding method and critical path-searching strategy.

Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study

PurposeProstate cancer (PCa) with high Ki-67 expression and high Gleason Scores (GS) tends to have aggressive clinicopathological characteristics and a dismal prognosis. In order to predict the Ki-67 expression status and the GS in PCa, we sought to construct and verify MRI-based radiomics signatures.Methods and materialsWe collected T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images from 170 PCa patients at three institutions and extracted 321 original radiomic features from each image modality. We used support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression to select the most informative radiomic features and built predictive models using up sampling and feature selection techniques. Using receiver operating characteristic (ROC) analysis, the discriminating power of this feature was determined. Subsequent decision curve analysis (DCA) assessed the clinical utility of the radiomic features. The Kaplan–Meier (KM) test revealed that the radiomics-predicted Ki-67 expression status and GS were prognostic factors for PCa survival.ResultThe hypothesized radiomics signature, which included 15 and 9 selected radiomics features, respectively, was significantly correlated with pathological Ki-67 and GS outcomes in both the training and validation datasets. Areas under the curve (AUC) for the developed model were 0.813 (95% CI 0.681,0.930) and 0.793 (95% CI 0.621, 0.929) for the training and validation datasets, respectively, demonstrating discrimination and calibration performance. The model's clinical usefulness was verified using DCA. In both the training and validation sets, high Ki-67 expression and high GS predicted by radiomics using SVM models were substantially linked with poor overall survival (OS).ConclusionsBoth Ki-67 expression status and high GS correlate with PCa patient survival outcomes; therefore, the ability of the SVM classifier-based model to estimate Ki-67 expression status and the Lasso classifier-based model to assess high GS may enhance clinical decision-making.

Selection of peanut butter machine by the integrated PSI-SV-MARCOS method

Production enterprises are enterprises that produce goods or services that aim to meet human needs such as machinery-equipment materials and labour. In order for a manufacturing enterprise to carry out its activities successfully, it must make the right choice when choosing its inputs. The correct execution of production activities and the selection of machinery, which requires high capital investments, also affect the efficiency of the enterprises, the correct use of materials and their costs. Therefore, it is an important decision for business managers to choose the right machine. At this stage, multi-criteria decision making (MCDM) methods are used for choosing the right machine. MCDM methods are methods used in the evaluation of alternatives using more than one criterion. In addition, the MCDM method is used in machine selection as well as in many areas. In this study, PSI, SV and MARCOS methods, which are among the MCDM methods, were used for peanut butter machine selection. First, the criteria and alternatives to be used for the peanut butter machine selection were determined by interviewing a peanut butter factory manager. In the study, while the criteria weights were determined, PSI and SV methods were used, while the machines were ranked with the MARCOS method. In addition, the MARCOS method was compared with other MCDM methods such as PIV, CODAS and WEDBA methods. After the rankings were found according to the methods, the relations between the rankings were examined using the Spearman Correlation method. The main purpose of the study is to determine the suitable butter machine for a peanut paste production factory. Contribution of this study to the literature PSI, SV and MARCOS methods were used together for the first time. In addition, no study has been found in the literature related to peanut butter machine. Therefore, this study is original and contributes to the literature.

The Physical, Mechanical, and Nutritional Properties of Freshly Harvested Paddy Rice Varieties Dried at Different Temperatures

Rice has become a major food in Nigeria, but the acceptance of the local varieties are low due to the presence of impurities traceable, poor processing, and low quality. This study investigates the effect of drying temperature on selected properties of Ofada white and Boromo paddy rice. Using standard methods, selected physical, mechanical, and nutritional properties of the rice varieties were estimated when freshly harvested and after drying at 50, 60, and 80oC to 14% moisture content (wet basis). The drying temperature had a significant effect on the length, width, thickness, arithmetic diameter, geometric diameter, equivalent diameter, sphericity, grain volume, surface area, bulk density, true density, porosity, angle of repose, and thousand-grain weight, bending strength, compressive strength, fracture energy, carbohydrate content, crude protein, and amylose content. This study concludes that drying temperature significantly affects rice properties and should be considered in the design and selection of processing machines.

A novel multi-criteria decision making procedure for saw machine selection in the mechanical machining

A novel multi-criteria decision making procedure for saw machine selection in the mechanical machining

E-ReInForMIF Routing Algorithm Based on Energy Selection and Erasure Code Tolerance Machine

Aiming at the problems of data loss and uneven energy consumption in wireless sensor networks during data transmission, this paper proposes a ReInForM transmission fault-tolerant routing algorithm based on energy selection and erasure code fault-tolerant machines (E-ReInForMIF). The E-ReInForMIF algorithm improves the multi-path routing algorithm by combining an erasure coding fault-tolerant machine and node residual energy sorting selection. First, the erasure coding fault-tolerant machine is used to encode the signal, determine the number of transmission paths through multi-path routing, and then select the specific node of the next hop by sorting the residual energy of the node. The E-ReInForMIF routing algorithm effectively solves the problems of uneven energy consumption and data loss in data transmission, improving network lifespan and transmission reliability. Finally, the signal is decoded. The simulation results show that the E-ReInForMIF routing algorithm is superior to the ReInForM routing algorithm in improving the reliability of data transmission.

The m-polar fuzzy set ELECTRE-I with revised Simos’ and AHP weight calculation methods for selection of non-traditional machining processes

Using improvements to the recently published m-polar fuzzy set (mFS) elimination and choice translating reality-I (ELECTRE-I) approach for calculating criteria weights, the selection of a Non-Traditional Machining (NTM) process problem from the industry is solved in this research. The criteria weights for the m-polar fuzzy ELECTRE-I method are evaluated using the Analytical Hierarchy Process (AHP) approach and the Revised Simos' method. For the ELECTRE family's criteria weight calculations, the Simos’ approach has been revised. Many researchers calculated the weight of the criteria in the selection of the NTM process using the AHP approach. Problems with both physical and intangible properties can be solved using the m-polar fuzzy ELECTRE-I approach. Additionally, it has the ability to solve MCDM issues with more variables. The improved Simos' technique is used in this work because it incorporates user choices for the criteria, or user voting for the criterion. Using expert assistance, the AHP technique prioritizes the criterion based on pair-by-pair comparisons of the criteria. The AHP approach makes compromises between the criteria. The ultimate selection of the process based on the needed aim is affected by both tangible and intangible features in the NTM selection dilemma. The impact of criteria weight techniques on the choice of the NTM process is examined using a single dimensional sensitivity analysis. AHP approach is proven to be less stable for criteria weight variation than the improved Simos' weight calculation method. The updated Simos' method, which takes into account user preferences, performs better for the m-polar fuzzy ELECTRE-I algorithm than the AHP weight calculation method.

Machine learning model of imipenem-resistant Klebsiella pneumoniae based on MALDI-TOF-MS platform: An observational study.

Machine learning is an important branch and supporting technology of artificial intelligence, we established four machine learning model for the drug sensitivity of Klebsiella pneumoniae to imipenem based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and compared their diagnostic effect. The data of MALDI-TOF-MS and imipenem sensitivity of 174 cases of K. pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity of 70 cases of imipenem-sensitive and 70 resistant cases were randomly selected to establish the training set model, 17 cases of sensitive and 17 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA), the training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, logistic regression (LR) algorithm, support vector machines (SVM) algorithm, neural network (NN) algorithm, the area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 3-fold Cross-validation respectively, the accuracy of the prediction model was verified by test set confusion matrix. The R²Y and Q² of OPLS-DA were 0.546 and 0.0178. The AUC of the best training set and test set models were 0.9726 and 0.9100, 1.0000 and 0.8581, 0.8462 and 0.6263, 1.0000 and 0.7180 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the LASSO, LR, SVM and NN model were 87%, 79%, 62%, and 68% in test set, respectively. The LASSO prediction model of K. pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.

Tool–workpiece separation characteristic and surface generation in ultrasonic assisted milling

Ultrasonic assisted machining is an eco-friendly surface texture fabrication method with low machining cost, high-accuracy precision, and efficiency. This study analyzes the surface texture formation in feed direction ultrasonic vibration assisted milling (FUVAM) and elliptical ultrasonic vibration assisted milling (EUVAM). The trajectories of the single and adjacent tool tips in FUVAM are analyzed. Furthermore, the tool–workpiece separation mechanism in EUVAM is discussed. Moreover, a 3D surface topography model of EUVAM is applied to study the influences of machining parameters on the ultrasonic machining topography. The experiments of conventional milling (CM) and EUVAM are carried out and different types of surface textures are successfully fabricated on Ti-6Al-4V using EUVAM. The results of the response surface indicate that the surface roughness shows an increasing trend with higher spindle speed, but decreases with improved feed speed and cutting depth. Compared with CM, the chips generated in EUVAM have the characteristics of small width and low bending degree. This work is expected to provide some guidance for the selection of machining and vibration parameters during ultrasonic assisted milling.

Choosing the best machine tool in mechanical manufacturing

Machine tools are indispensable components and play an important role in mechanical manufacturing. The equipment of machine tools has a huge effect on the operational efficiency of businesses. Each machine tool type is described by many different criteria, such as cost, technological capabilities, accuracy, energy consumption, convenience in operation, safety for workers, working noise, etc. If the selection of machine is only based on one or several criteria, it will be really easy to make mistakes, which means it is not possible to choose the real best machine. A machine is considered to be the best only when it is chosen based on all of its criteria. This work is called multi-criteria decision-making (MCDM). In this study, the selection of machine tools has been done using two different multi-criteria decision-making methods, including the FUCA method (Faire Un Choix Adéquat) and the CURLI method (Collaborative Unbiased Rank List Intergration). These are two methods with very different characteristics. When using the FUCA method, it is necessary to normalize the data and determine the weights for the criteria. Meanwhile, if using the CURLI method, these two things are not necessary. The selection of these two distinct methods is intended to produce the most generalizable conclusions. Three types of machine tool, which are considered in this study, include grinding machine, drilling machine and milling machine. The number of grinders that were offered for selection was twelve, the number of drills that were surveyed in this study was thirteen, while nine were the number of milling machines that were given for selection. The objective of this study is to determine the best solution in each type of machine. The results of ranking the machines are very similar when using the two mentioned methods. Specially, in all the surveyed cases, the two methods FUCA and CURLI always find the same best alternative. Accordingly, it is possible to firmly come to a conclusion that the FUCA method and the CURLI method are equally effective in machine tool selection. In addition, this study has determined the best three machines corresponding to the three different machine types

Combination of symmetry point of criterion, compromise ranking of alternatives from distance to ideal solution and collaborative unbiased rank list integration methods for woodworking machinery selection for small business in Vietnam

Woodworking industry plays an important role in the development of Vietnam’s economy. The efficiency of woodworking process depends a lot on the machinery used in the woodworking process. Selecting the best option among a variety of machines is tedious and complex work. However, if the choice of machine is based only on the subjective opinion of the customer, it will lead to mistakes. That mistake is understood that the customer will choose the option that is not the best among the machines proposed by the supplier. Instead, machine selection must be based on all machine parameters. This is called multi-criteria decision making (MCDM). There are MCDM methods, when used it is necessary to know the weights of the criteria. However, there are also methods that do not need to know the weights of the criteria. CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) is a method that, when used, is required to weight the criteria. In contrast, this problem is unnecessary when using the CURLI (Collaborative Unbiased Rank List Integration) method. In this study, three kinds of machinery commonly used for small business in woodworking field were selected. The three kinds of machinery mentioned in this study include wood milling machine, wood saw machine, wood planer. The SPC (Symmetry Point of Criterion) method was used to calculate the weights of the criteria for each kind of machinery. This is the youngest method among the methods of determining the weights for the criteria, it was only found in 2023. The two methods include CRADIS and CURLI were used to rank the machinery kinds. The result showed that in all the surveyed situation, the best alternative is always determined consistently when using CRADIS and CURLI methods. Accordingly, three best alternatives with three different machinery kinds (milling machine, saw machine and planer) were found in this study

Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data

Agencies that regulate electricity providers often apply nonparametric data envelopment analysis (DEA) to assess the relative efficiency of each firm. The reliability and validity of DEA are contingent upon selecting relevant input variables. In the era of big (wide) data, the assumptions of traditional variable selection techniques are often violated due to challenges related to high-dimensional data and their standard empirical properties. Currently, regulators have access to a large number of potential input variables. Therefore, our aim is to introduce new machine learning methods for regulators of the energy market. We also propose a new two-step analytical approach where, in the first step, the machine learning-based adaptive least absolute shrinkage and selection operator (ALASSO) is used to select variables and, in the second step, selected variables are used in a DEA model. In contrast to previous research, we find, by using a more realistic data-generating process common for production functions (i.e., Cobb–Douglas and Translog), that the performance of different machine learning techniques differs substantially in different empirically relevant situations. Simulations also reveal that the ALASSO is superior to other machine learning and regression-based methods when the collinearity is low or moderate. However, in situations of multicollinearity, the LASSO approach exhibits the best performance. We also use real data from the Swedish electricity distribution market to illustrate the empirical relevance of selecting the most appropriate variable selection method.

Selection of Additive Manufacturing Machines via Ontology-Supported Multi-Attribute Three-Way Decisions

Selection of a suitable additive manufacturing (AM) machine to manufacture a specific product is one of the important tasks in design for AM. So far, many selection approaches based on multi-attribute decision making have been proposed within academia. Each of these approaches works well in its specific context. However, the approaches are not flexible enough and could produce undesirable results as they are all based on multi-attribute two-way decisions. In this paper, a selection approach based on ontology-supported multi-attribute three-way decisions is presented. Firstly, an ontology for AM machine selection is constructed according to vendor documents, benchmark data, expert experience, and the Senvol database. Supported by this ontology, a selection approach based on multi-attribute three-way decisions is then developed. After that, four AM machine selection examples are introduced to illustrate the application of the developed approach. Finally, the effectiveness and advantages of the approach are demonstrated via a set of comparison experiments. The demonstration results suggest that the presented approach is as effective as the existing approaches and more flexible than them when the information for decision making is insufficient or the cost for undesirable decision results is high.

Conceptual Design of a Semi-Automatic Process Line for Recycling Photovoltaic Panels as a Way to Ecological Sustainable Production

The article presents the developed technology for the comprehensive recycling of depleted, used or damaged photovoltaic (PV) cells made of crystalline silicon. The developed concepts of technology and the results of research on recycling were presented on silicon photovoltaic cells and modules. The sequence of steps and the type of procedures used are proposed. A thermal delamination method for used commercial photovoltaic modules has been developed to separate the materials. In addition, a recycling line was proposed along with the selection of machines and a holistic approach to project profitability based on a SWOT analysis. The presented semi-automatic installation enables recycling on a laboratory scale. The line was designed for the assumed capacity of 30 t/h. The total energy demand for the designed line was calculated, which showed that 16.49 kWh is needed to recycle 1 ton of photovoltaic laminates. Implementation of developed solutions on an industrial scale will allow to reduce production costs, mainly thanks to energy savings, which translates into less devastation of the natural environment and reduced material consumption. In addition, the implementation of the PV module recycling system will reduce and, consequently, eliminate a significant amount of used PV devices deposited in landfills. The content of the article gives a fresh and innovative look at the essence of photovoltaic panel recycling processes in terms of production benefits as well as financial and environmental benefits.

Multi-Criteria Decision-Making for Machine Selection in Manufacturing and Construction: Recent Trends

As the number of alternative machines has increased and their technology has been continuously developed, the machine selection problem has attracted many researchers. This article reviews recent developments in applying multi-criteria decision-making (MCDM) methods for selecting machines in the manufacturing and construction industries. Selected articles are classified according to the application area and the applied MCDM method. By focusing on the last five years, this paper identifies recent trends in developing and using these methods. Results suggest that there has been a noticeable growth in the utilization of MCDM techniques for machine selection problems in both sectors. It is also noted that several decision-support tools and methods have been developed and successfully applied during this period. Accordingly, needs and directions for future research are discussed.

Multi-Task Multi-Agent Reinforcement Learning for Real-Time Scheduling of a Dual-Resource Flexible Job Shop with Robots

In this paper, a real-time scheduling problem of a dual-resource flexible job shop with robots is studied. Multiple independent robots and their supervised machine sets form their own work cells. First, a mixed integer programming model is established, which considers the scheduling problems of jobs and machines in the work cells, and of jobs between work cells, based on the process plan flexibility. Second, in order to make real-time scheduling decisions, a framework of multi-task multi-agent reinforcement learning based on centralized training and decentralized execution is proposed. Each agent interacts with the environment and completes three decision-making tasks: job sequencing, machine selection, and process planning. In the process of centralized training, the value network is used to evaluate and optimize the policy network to achieve multi-agent cooperation, and the attention mechanism is introduced into the policy network to realize information sharing among multiple tasks. In the process of decentralized execution, each agent performs multiple task decisions through local observations according to the trained policy network. Then, observation, action, and reward are designed. Rewards include global and local rewards, which are decomposed into sub-rewards corresponding to tasks. The reinforcement learning training algorithm is designed based on a double-deep Q-network. Finally, the scheduling simulation environment is derived from benchmarks, and the experimental results show the effectiveness of the proposed method.

CODAS, TOPSIS, and AHP Methods Application for Machine Selection

Currently, companies have the need to make a robust analysis about the performance of their processes. In this regard, this article proposes the use of tools focused on the selection of machines, methods such as CODAS, TOPSIS, and AHP. In this study, it is sought to optimize the decision for equipment selection through multicriteria decision-making methods, maximizing the process reliability, since it is something that is demanded by the market, thus improving the quality of the finished product offered by companies. Thereby, this work addresses the comparison of these methods using real data from a case of study involving a machine selection for a pastry company, specifically it is looking for a certain kind of mixer machine that will help to improve the times of process and the quality of the final product. It shows how easy and relevant these methods are for small businesses and huge companies to motivate them to improve their selection processes explaining each method and then the application for each one comparing the results and finally doing a projection for this company’s best choice.

An Interactive Two-Stage Framework for Simultaneous Machine Selection and Buffer Allocation

In manufacturing, machine selection and buffer allocation are extremely important and widely studied problems, as both can significantly affect the system performance. Solving either problem alone in a deterministic setting is already challenging. In this study, however, we simultaneously solve a high dimensional machine selection and buffer allocation problem in a highly stochastic and complex production line environment. The objective is to simultaneously select the optimal combination of machine type and buffer size allocation to maximize production throughput under limited cost and total buffer size constraints. We develop an efficient, interactive two-stage optimization method, called Machine Selection and Buffer Allocation algorithm (MSBA), that consists of a rapid machine selection (RMS) procedure and an adaptive global particle and local hyperbox search (AGPLHS) algorithm to solve the problem via simulation optimization. MSBA achieves seamless integration of RMS and AGPLHS so as to enable the simultaneous machine selection and buffer allocation to be solved efficiently. Furthermore, we compare our overall framework with three common existing methods (Genetic Algorithm, Nelder-Mead Algorithm and Adaptive Tabu Search). It is shown that our interactive two-stage framework outperforms the competing algorithms both in terms of effectiveness and efficiency. Note to Practitioners—Two important problems to solve in many production processes such as semiconductor manufacturing are machine selection and buffer allocation. Various methodologies have been utilized to solve each of the two problems individually. This work proposes an interactive two-stage simulation optimization algorithm which simultaneously solves both problems in complex, high dimensional and profoundly stochastic environments. While the most basic problem setting is a serial multi-workstation environment, complexities such as re-work stations, merging, splitting, parallel machine, and multi-product processor can be incorporated into the simulation model and solved using our proposed methodology. The methodology is user-friendly and intuitive yet adaptable enough to handle a wide range of production line environments or solve other stochastic optimization problems which require the simultaneous optimization of a mixture of binary and integer-based solutions. The numerical study, consisting of two different job-shop environments and including three benchmark algorithms, provides evidence for the efficiency of the proposed algorithm. Sensitivity analysis on user-defined parameters is also provided for the benefit of practitioners.

Environmentally friendly strategy for hybrid flow shop scheduling with the unrelated parallel machines

With the increasing environmental pressure, reducing the environmental impact on hybrid flow shops (HFS) has attracted extensive attention because of its broad industrial applications. The selection of machines in the implementation of HFS is a complex decision-making process that hinders the optimization efficiency in shops. However, this issue has not been addressed thoroughly. In light of this, a multi-objective mathematical model is formulated for the minimization of makespan and energy consumption of the hybrid flow shop scheduling problem (HFSP). The environmental performance of machines is calculated by the proposed evaluation index and is ranked on the basis of the integrated entropy and fuzzy technique for order performance by similarity to ideal solution method. Moreover, to solve the multi-objective model of HFSP, an improved differential evolution algorithm with a heuristic active decoding rule that incorporates the ranking of environmental performance of machines into the iterative process of the algorithm is presented. Finally, a case study is presented to evaluate the effectiveness of the proposed method and to prove the feasibility of the ranking-based differential evolution algorithm (RBDE). The result shows that the proposed RBDE outperforms RBNSGA-II and RBPSO in searching for non-dominated solutions that can solve the environmental production of HFSP effectively.

S-56-4: ESTABLISHING A NEPHROLOGY-LED AMBULATORY BLOOD PRESSURE MONITORING SERVICE IN SINGAPORE

Objective: The prevalence of hypertension in Singapore is increasing, and 64.3% of patients with hypertension are not able to meet treatment targets. Management of hypertension in patients with chronic kidney disease (CKD) and end stage kidney disease (ESKD) is challenging due to higher risk of resistant and masked hypertension, autonomic dysfunction, and blood pressure fluctuations related to intermittent haemodialysis (HD) treatment. Ambulatory blood pressure monitoring (ABPM) is the gold standard for diagnosis and management of hypertension and can provide better insight into patients blood pressure patterns and response to treatment. ABPM for a 24-hour period is an established service with the Cardiology department at our hospital. However, access remains limited, and utilizing another departments resources increases costs and may be a burden for our patients. Our aim was to develop a nephrologist-led ABPM service to increase accessibility and utilization of ABPM in our department and provide the option of 48-hour ABPM for patients on haemodialysis to be able to track blood pressure trends on both dialysis and non-dialysis days. Design & methods: We procured a Schiller BR-102 ambulatory blood pressure monitor with battery compatibility ensured to be able to run for 48 hours. Other factors in machine selection were portability, and ease of use of machine interface and reporting software. Two cuff sizes were obtained to increase utility for patients of different body habitus. The device underwent a review by hospital operations management team for meeting standards for safety and patient data protection. Scheduling and nursing workflows were established Nurses were trained in pre and post procedure checks with the aid of a nursing checklist, and a patient log was drafted for listing timing of medications, haemodialysis, and sleep and wake times. A patient information sheet was also created with a phone hotline to call during office hours in the event of complications. The service was then integrated into the electronic health reporting system. Reporting guidelines were adapted from the 2019 HOPE Asia Networks expert panel consensus guidelines for ABPM in Asia (2019). Results: Pilot studies were run on two healthy subjects to ensure the process flowed smoothly and that the machine was in working order., Recruiting of patients for ABPM service is ongoing. Conclusions: Establishing an ABPM service in our nephrology practice is feasible and will facilitate improved management of hypertension in our complex patient population.