Efficient Operational Planning Research Articles

Development of an ARIMAX model for forecasting airport electricity consumption in Accra-Ghana: The role of weather and air passenger traffic

The rapid expansion of the aviation industry has significantly increased energy consumption within airport terminals. This study introduces an advanced ARIMAX model to forecast electricity consumption at Kotoka International Airport (KIA) in Ghana by incorporating weather conditions and air passenger numbers. By analysing data from 2019 to 2022, including lockdown periods, the model demonstrates superior predictive accuracy, evidenced by an RMSE of 55,914 kWh, a MAPE of 4.22 %, and an R-squared value of 0.899. Key determinants of energy consumption identified include air passengers and temperature, highlighting their substantial impact on energy use. The ARIMAX model's accuracy underscores its potential for enhancing energy management practices at airports. By integrating variables such as air passenger traffic and weather conditions, the model refines energy demand predictions, supporting more efficient energy use and operational planning. The ARIMAX model proves to be a valuable tool for forecasting energy consumption at KIA, aiding in energy management and sustainability efforts.

A novel approach to forecast surgery durations using machine learning techniques.

This study presents a methodology for predicting the duration of surgical procedures using Machine Learning (ML). The methodology incorporates a new set of predictors emphasizing the significance of surgical team dynamics and composition, including experience, familiarity, social behavior, and gender diversity. By applying ML techniques to a comprehensive dataset of over 77,000 surgeries, we achieved a 24% improvement in the mean absolute error (MAE) over a model that mimics the current approach of the decision maker. Our results also underscore the critical role of surgeon experience and team composition dynamics in enhancing prediction accuracy. These advancements can lead to more efficient operational planning and resource allocation in hospitals, potentially reducing downtime in operating rooms and improving healthcare delivery.

A Study to Improve the Stress Management Policy for Firefighters

The National Fire Agency in Korea has implemented various stress management programs for firefighters. However, few studies have focused on the actual effectiveness of these programs. This study thus aimed to explore problems in the psychological counseling system for firefighters, focusing on the case of Jeju Island, and to prepare an efficient operation plan for the system. Survey results indicate that counselors’ lack of understanding of firefighting results in low satisfaction with counseling services, and that firefighters with short service periods require counselors who understand the firefighting profession more than other groups do. In order to overcome these limitations, it is proposed that a counselor’s education program be introduced and that retired firefighters be hired for the psychological counseling program.

A Case Study on Simulation of Water Cycle in a Watershed Considering Artificial Elements such as Agricultural Reservoirs

The National Fire Agency in Korea has implemented various stress management programs for firefighters. However, few studies have focused on the actual effectiveness of these programs. This study thus aimed to explore problems in the psychological counseling system for firefighters, focusing on the case of Jeju Island, and to prepare an efficient operation plan for the system. Survey results indicate that counselors’ lack of understanding of firefighting results in low satisfaction with counseling services, and that firefighters with short service periods require counselors who understand the firefighting profession more than other groups do. In order to overcome these limitations, it is proposed that a counselor’s education program be introduced and that retired firefighters be hired for the psychological counseling program.

Algorithm for operational planning of transportation by machine shipments in long-distance traffic

Efficient operational planning of transportation is crucial in longdistance traffic, where the coordination of shipments plays a pivotal role in optimizing logistics operations. This article presents an algorithm designed to streamline the planning process for machine shipments in long-distance transportation. The algorithm leverages advanced optimization techniques and machine learning to address the complexities associated with route selection, scheduling, and resource allocation.

Design and Evaluation of Field Segmentation, Path Generation and Sequential Point Tracking Algorithms for Agricultural Mobile Robots

Highlights Field segmentation, path generation, and sequential point tracking algorithms were designed for agricultural mobile robots. An optimized field segmentation plan and operation path could be generated for the robot. The sequential point tracking algorithm could guide the robot to a series of target points. The algorithms were tested in simulation calculations and practical experiments. Abstract. As agricultural production becomes increasingly intelligent, operations by autonomous mobile robots become an inevitable trend. For some operations, such as in-field soil sampling, agricultural robots need to track a series of target points in the field. In this work, a field segmentation algorithm, a path generation algorithm, and a sequential point tracking algorithm were designed and evaluated. The field segmentation and operation path generation algorithms, which took, respectively, the total lost area of the field and the total turning angle of the robot as optimization objectives, aimed to provide an efficient operation plan for the robot to follow. The sequential point tracking algorithm was designed for the robot to realize automatic tracking of the planned target points following a desired sequence. The algorithms were tested by simulation calculations using the boundary coordinates of two test fields and field experiments based on a small four-wheeled mobile robot. Field results of the straight-path, multi-target point tracking experiment showed that the designed steering angle and velocity control laws could accurately guide the robot from a start point to a series of target points when the robot was running on a concrete surface. The absolute distance errors of the robot with respect to the targets were smaller than 0.03 m. To test the robot’s tracking performance on different surface conditions, two whole-field, continuous point tracking experiments were performed. The general effectiveness of the sequential point tracking algorithm was validated. When the robot was running on hard surface conditions (concrete and grass), the tracking errors were between 0.014 m and 0.074 m. When the robot was running on a soft and bumpy soil surface, the tracking accuracy degraded to submeter level due to the limitation of small-size wheels. The average, RMS, and maximum errors were 0.774 m, 0.775 m, and 0.798 m, respectively. However, the feasibility of the sequential control algorithm was not influenced. The designed algorithms could be potentially utilized in the development of robotic systems for in-field site-specific operations. Keywords: Agricultural mobile robot, Control sequence, Field segmentation, Operation path generation, Point tracking.

A multi-appointment patient scheduling system with machine learning and optimization

Appointment scheduling is critical to increasing resource utilization and operational performance in various industry domains, especially healthcare. Costs to care for several serious diseases are projected to grow due to the aging population and rising drug prices. Thus, there is an urgent need for efficient operational planning and scheduling to reduce expenses. This research explores ways to effectively schedule outpatient chemotherapy visits with multiple appointments requiring different resources. The study aims to assess the impact of patient no-shows and individual stochastic appointment durations in scheduling performance and determine if overbooking is viable to mitigate the adverse effects of patient no-shows. The study first applies artificial neural networks (ANN) to calculate patient no-show probabilities and individualized appointment durations. Then, it builds several optimization models that use the ANN models’ outcomes to schedule outpatient chemotherapy visits. The performance of patient schedules obtained from these optimization models is assessed using simulation analysis to identify the effectiveness of overbooking to combat patient no-shows and determine if individual stochastic appointment durations produce better key performance indicators.

Parallel optimization method of train scheduling and shunting at complex high‐speed railway stations

AbstractThe train operations of large stations are critical in determining the efficiency of the railway network. Large high‐speed railway stations often have more than two adjacent stations running in multiple directions and must address highly complex train operation patterns. To develop a more efficient operation plan than the existing route‐based representations for modeling train conflicts, a more systematic track‐based resource network (TRN) representation for a railway station that simulates fixed equipment is used in this paper. Based on this high‐fidelity TRN, an improved operation scheduling model (OSM) is introduced for the integrated scheduling of train operations, including shunting operations. Besides, two approaches based on Lagrangian relaxation (LR) and the Alternating Direction Method of Multipliers (ADMM) are described in detail to address the operation scheduling problem (OSP) for stations, and an efficient heuristic rule‐based algorithm is also designed to generate lower bound solutions. To improve the algorithm efficiency, we designed a parallelization strategy for the proposed approaches. Based on real‐world case studies, the computing results demonstrate that the parallelized ADMM‐based approach can always find high‐quality feasible solutions for complex stations and generate conflict‐free station operation plans with significantly improved efficiency.

베트남 메콩델타 지역 농산물 물류센터 추진전략에 관한 연구

Can Tho City, the fifth largest city in Vietnam and located at the hub of the Mekong Delta region, is located 160km west of Ho Chi Minh City, the largest city in Vietnam, and the southern part of the Hasu River, the first tributary of the Mekong River. The area is 1,439 ㎢, and the climate is hot and humid and the topography is flat, making it an environment suitable for agricultural and marine product production. Agricultural products, a major item of Vietnam's primary industry, are a major handling item in the Mekong Delta region, including Can Tho City. In order to determine the direction for the development of logistics development in the Mekong Delta region and Can Tho City, field surveys and detailed data surveys, consultations with related organizations such as Can Tho City, and development experiences and opinions of Korean companies that have entered Vietnam were listened to. As a result, building an Agricultural Products Processing Center (APC) was identified as a priority. As a result of the AHP analysis, in the major categories, ‘securing infrastructure’ was rated as 0.642, which was evaluated as overwhelmingly more important than other items. Next, ‘preparation of government support policy’ was 0.188, and ‘establishment of smart logistics operating system’ was 0.170. In the sub-category of securing major category infrastructure, 'transport network connected to logistics center' was evaluated as 0.383, the most important item, followed by 'storage facility' (0.241), 'gathering and delivery facility' (0.233), and 'support facility' (0.143). Subsequently, based on the results of the AHP survey, this study presented the development strategies for the agricultural logistics center in Can Tho City by dividing it into short-term, mid-term, and long-term. In the short term, it was proposed to focus on building logistics center infrastructure and linked transportation infrastructure, and in the mid term, an efficient system operation plan for agricultural logistics center was suggested. In the long term, support policies for companies using agricultural logistics center and the establishment of food industry clusters in the hinterland were proposed. This study presents a strategy for promoting an agricultural logistics center in Can Tho City, the central city of the Mekong Delta region. It is expected that it will be used as a reference material.

Optimal Rebalancing Strategy for Shared e-Scooter Using Genetic Algorithm

Shared e-scooters are provided as a free-floating service that can be freely rented and returned within the service area. Although this has a positive effect in terms of convenience for users of shared e-scooters, it is creating new urban problems, such as undermining the aesthetics of the city and obstructing the passage of pedestrians. Therefore, this study developed an optimal rebalancing algorithm to mitigate these problems and proposed an efficient operation plan. Complete relocation was performed to match the demand and supply for an efficient operation by reducing the unnecessary oversupply of shared e-scooters. The optimal rebalancing algorithm that reflects the attributes of e-scooters was developed through genetic algorithms and subsequently applied to actually used cases. The results indicate that when 20% of the potential demand was considered, an optimal solution could be derived with two relocation vehicles; however, when the potential demand was not considered, three relocation vehicles were required. Therefore, it is anticipated that the results of this study can serve as basic data for solving various urban problems caused by the recent rapid increase in the use of shared e-scooters.

Efficient operational planning of active distribution network by embedding uncertainties and network reconfiguration

Efficient operational planning of active distribution network by embedding uncertainties and network reconfiguration

A Real-Time Spatiotemporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions

Abstract The safe and successful operation of offshore infrastructure relies on a detailed awareness of ocean wave conditions. Ongoing growth in offshore wind energy is focused on very large-scale projects, deployed in ever more challenging environments. This inherently increases both cost and complexity and therefore the requirement for efficient operational planning. To support this, we propose a new machine learning framework for the short-term forecasting of ocean wave conditions to support critical decision-making associated with marine operations. Here, an attention-based long short-term memory (LSTM) neural network approach is used to learn the short-term temporal patterns from in situ observations. This is then integrated with an existing, low computational cost spatial nowcasting model to develop a complete framework for spatiotemporal forecasting. The framework addresses the challenge of filling gaps in the in situ observations and undertakes feature selection, with seasonal training datasets embedded. The full spatiotemporal forecasting system is demonstrated using a case study based on independent observation locations near the southwest coast of the United Kingdom. Results are validated against in situ data from two wave buoy locations within the domain and compared to operational physics-based wave forecasts from the Met Office (the United Kingdom’s national weather service). For these two example locations, the spatiotemporal forecast is found to have an accuracy of R2 = 0.9083 and 0.7409 in forecasting 1-h-ahead significant wave height and R2 = 0.8581 and 0.6978 in 12-h-ahead forecasts, respectively. Importantly, this represents respectable levels of accuracy, comparable to traditional physics-based forecast products, but requires only a fraction of the computational resources. Significance Statement Spectral wave models, based on modeling the underlying physics and physical processes, are traditionally used to generate wave forecasts but require significant computational cost. In this study, we propose a machine learning forecasting framework developed using both in situ buoy observations and a surrogate regional numerical wave model. The proposed framework is validated against in situ measurements at two renewable energy sites and found to have very similar 12-h forecasting errors when benchmarked against the Met Office’s physics-based forecasting model but requires far less computational power. The proposed framework is highly flexible and has the potential for offering a low-cost, low computational resource approach for the provision of short-term forecasts and can operate with other types of observations and other machine learning algorithms to improve the availability and accuracy of the prediction.

축류펌프의 입구 안내깃 모사에 따른 성능 및 내부 유동장에 관한 수치해석적 연구

In this study, an absolute flow angle was numerically assigned to the inlet of an axial pump in order to predict its performance and internal flow field prior to the design process of an inlet guide vane. The finite volume method, which is an approximate analysis method based on the three-dimensional Reynolds-averaged Navier-Stokes (RANS) equation, was applied for discretization of governing equations. The shear stress transport (SST) model was used as the turbulence model and the hexahedral grid system was selected as the appropriate number of nodes through the grid dependency test. The change in the performance curve and internal flow field was found with respect to the difference in the absolute flow angle at the impeller inlet. When installing a variable inlet guide vane of an axial flow pump, an efficient operation plan by adjusting the angle of an inlet guide vane might be established with the results of this study.

수소철도차량 기지 내 수소충전설비 최적 운영전략 연구

With the development of hydrogen fuel cell train, it is essential to develop an efficient operation plan of hydrogen charging Facilities at Train Depots for stable and economical operation of routes. Accordingly, this paper calculates the hydrogen demand for each time period of the hydrogen charging facilities based on the hydrogen fuel cell train operation schedule, and presents an optimization method that can respond to this hydrogen demand at the minimum cost for each season. It also includes case studies based on urban railway data using these optimization methods.

LoTOx 시스템 적용 폐기물 소각로의 경제성 분석 및 효율적 운전 방안

The purpose of this study was to investigate how to improve the combustion gas treatment method of domestic waste incinerator comprising SNCR and SCR by adopting the LoTOx (ozone low-temperature oxidation) system. NOx reduction performance, efficient operation plan, and economic feasibility of the LoTOx system not yet commercialized in domestic field were studied. First, by applying the LoTOx system to actual waste incineration facilities, it was possible to compare and evaluate the NOx reduction performance with conventional SCR facilities currently widely used. The NOx reduction rate was close to 90% based on combustion gas of 110℃. If the temperature of the combustion gas was lowered further, the NOx reduction rate was much closer to the theoretical expected value, and the reduction rate of 90% or more can be shown. Second, economic benefits in terms of initial investment and operating cost were analyzed for an existing combined SNCR and SCR system, by replacing SCR with the LoTOx ozone oxidation system. The initial investment cost was 73.6% compared to the SCR facility, and the longer the operating period, the greater the cumulative operating cost is reduced. The LoTOx system could be predicted to fall to 61.2% of the SCR facility in the fifth year and 55.3% in the tenth year. Third, based on the experiment and existing papers, several factors impacting the efficiency of the operation of the LoTOx system were identified. It was possible to present the most efficient operation plan such as the temperature of the processing gas, the installation method of the cooling water pipe of the ozone generator, input voltage, and gas flow rate.

An Operation Strategy of ESS for Enhancing the Frequency Stability of the Inverter-Based Jeju Grid

As environmental pollution deteriorates, the acceleration of decarbonization worldwide is underway. Several countries, including Korea, are incorporating new and renewable energy into existing systems, and various methods of decarbonization are being researched. With changes in the control environment, the expansion of the system linkage of new and renewable energy can cause system instability problems. This paper introduces an operation plan that uses the fast response characteristics of energy storage system(s) (ESS) to improve stability and review system stability as a result of the expansion of new and renewable energy on Jeju Island. Frequency stability was ensured through an analysis of frequency response characteristics in the event of a system accident, and an efficient ESS operation plan was simulated by changing the ESS parameters. Through this simulation, stable ESS operation and hunting in the frequency recovery process were minimized through cooperation between the existing and new ESS via a wide dead-band range and speed adjustment rate correction, and the lowest frequency was improved.

지방자치단체 남북교류협력 네트워크의 효율적 운영을 위한 제언

Inter-Korean exchanges and cooperation have been suspended due to the prolonged economic hardship, but the present point can be used as a preparation period for the resumption of inter-Korean exchanges and cooperation by local governments. In the past, local governments sought to promote the project only when inter-Korean relations improved. Therefore, in order to achieve inter-Korean exchanges and cooperation, which are vulnerable to various external variables, within a short period of time, problems were revealed in various aspects such as poor planning and systemicity of the project, lack of competency, and lack of professional manpower. This study tried to propose an efficient operation plan through the application of the network concept, rather than the existing local government approach for inter-Korean exchange and cooperation governance. In the past, governance of inter-Korean exchanges and cooperation was not established by local governments, but since the local government-level governance called the “Inter-Korean Peace and Cooperation Local Government Council” has recently been established, it is necessary to focus on establishing efficient operation plans through networking. To this end, the current status and problems of inter-Korean exchange and cooperation networks of local governments were examined. In addition, it was proposed to set clear goals and directions for the inter-Korean exchange and cooperation network of local governments for efficient operation, to propose an operation plan, to reflect North Korean demand, and to promote a new endogenous regional development strategy in parallel.

장애학생의 생존수영 교육에 관한 탐색적 연구

This study is an exploratory study on survival swimming education for 73 students with disabilities, and aims to derive the problems and needs of how survival swimming education for students with disabilities is progressing through the focus group interview method. A focus group interview was conducted based on pre-surveyed questionnaire, and data were collected through video and voice recording using a laptop and camera with the consent of the participants. By analyzing the collected data, 3 themes, 5 categories, and 14 subcategories were derived, and the results of the research are as follows. First, in the case of a lack of survival swimming education facilities for the disabled and the difficulties of their participation, efforts should be made to secure swimming pool facilities for the disabled and expand the education time for their learning in public facilities swimming pools.<BR> Second, there is a lack of facilities for the operation of survival swimming education program for students with disabilities; and thus, the need for mandatory survival education, and continuous education on survival swimming for students with disabilities are required. In addition, variouvs continuous and long-term programs suitable for students with disabilitie should be adopted and practiced. Third, the findings of the research revealed that there was a lack of awareness and leadership for students with disabilities in the operation of training personnel for survival swimming for students with disabilities; and thus, the need for education accompanied by guardians, and the need to nurture experts in survival swimming for students with disabilities to meet national qualifications. Fourth, the results also implied that for an efficient operation plan, it is required to increase the number of new facilities, survival swimming education programs for students with disabilities, and a professional manpower nurturing system. required. Fifth, as a way to improve survival swimming for students with disabilities, it was found that promotion and improvement of the program, national cooperation for the use of facilities, and a change in awareness of general students about survival swimming education for students with disabilities would be required.

Experimental study and operation optimization of a parallel-loop heat pump for exhaust air recovery in residential buildings

The indoor space of residential building is limited, especially in building retrofit, to improve the indoor space utilization and the performance of exhaust air heat recovery system, a novel dual-cylinder rotary compressor with independent suction and discharge ports was designed to drive an inverter parallel-loop exhaust air heat pump. Through the experiments and analyzing the parallel-loop exhaust air heat pump system working characteristics under variable compressor frequency and different fresh air volume flow rate, the optimized method is proposed and verified experimentally. Finally, the annual energy efficient operation plan was proposed, and the seasonal performance factor and seasonal average input power under this operation plan in three typical countries/organization were calculated. Results show that the system COP can be greatly improved by applying a mixed air supply method during winter. When the fresh-air-to-return-air ratio is 1:1 and the outdoor temperature is −5 °C, the system COP reached 10.18, which is the highest comparing with the related studies. In summer, the system COP is increased by increasing the ventilation volume flow rate. When the operation of the parallel-loop exhaust air heat pump system follows the energy efficient operation plan to ensure the temperature effectiveness over 100% and the outdoor working condition complies with GB 21455–2013, the heating season performance factor, heating season average input power, cooling season performance factor, and cooling season average input power are 7.22, 0.6909 kW, 2.83, and 0.8167 kW, respectively. The work would provide guidance for the development of exhaust air heat recovery system in residential building.

Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads

In Korea apartment buildings, most energy is consumed as heating energy. In order to reduce heating energy in apartment buildings, it is required to reduce the amount of energy used in heating systems. Energy saving in heating systems can be achieved through operation and control based on efficient operation plans. The efficient operation plan of the heating system should be based on the predicted heating load. Thus, various methods have been developed for predicting heating loads. Recently, artificial intelligence techniques (e.g., ANN: artificial neural network) have been used to predict heating loads. The process for determination of input data variables is necessary to obtain the accuracy of predicted results using an ANN model. However, there is a lack of studies to evaluate the accuracy level of the predicted results caused by the selection and combination of input variables. There is a need to evaluate the performance of an ANN model for prediction of residential heating loads. Therefore, the purpose of this study is, for a residential building, to evaluate the accuracy levels of predicted heating loads using an ANN model with various combinations of input variables. To achieve the study purpose, each case was classified according to the combination of the input variables and the prediction results were analyzed. Through this, the worst, mean, and best were selected according to the predicted performance. In addition, an actual case was selected consisting of variables that can be measured in an actual building. The derived cv(RMSE) of each case resulted in a percentage value of 38.2% for the worst, 7.3% for the mean, 3.0% for the best, and 5.4% for the actual. The largest difference between the best and worst resulted in 33.2%, and thus the precision of the predicted heating loads was highly affected by the selection and combination of the input variables used for the ANN model.