Planning Scheduling Research Articles

Clinical experiences with online adaptive radiotherapy of vulvar carcinoma

Background and purpose Radiotherapy for vulvar carcinoma is challenging due to relatively high risk of locoregional disease recurrence, a technically challenging target, and postoperative lymphocele, and a high risk radiation sequelae. We aim to explore, if it is possible to reduce dose to normal tissue, while maintaining CTV coverage for this patient group with online adaptive radiotherapy. Materials and methods 20 patients with vulvar carcinoma (527 fractions) were treated with online adaptation on a Varian Ethos accelerator. Setup CBCTs were acquired daily for adaptive planning. Verification CBCTs were acquired immediately prior to dose delivery. CTV dose coverage and dose to bladder and rectum were extracted from the scheduled and adapted plans as well as from adapted plans recalculated based on verification CBCTs. In addition, analysis of the decision of the adaptive procedure was performed for 17 patients (465 fractions). Results Mean CTV D95% and standard deviation was 98% ± 5% for the scheduled plan compared to 100.0 ± 0.3% and 100.0 ± 0.8% for the adapted plan on the setup and verification CBCT respectively. Dose to OARs varied substantially and did not show any benefit from adaption itself, however a margin reduction was implemented after the first patients treated. The adapted plan was chosen for 63.5% of the fractions and dominant reasons for not adapting were ‘no significant dosimetric gain’ (75 fractions, 14%) and ‘Medical doctor (MD) not available for treatment’ (50 fractions, 9.5%). The median adaption time was 15 min and the 25th and 75th percentile was 12 and 17 min, respectively. Conclusion CTVs and PTVs dose coverage were significantly improved with adaptation compared to image-guided RT. This gain was robust during the treatment time.

Well-being as a performance pillar: a holistic approach for monitoring tennis players.

This perspective article aims to discuss the usefulness of tools that can assist tennis professionals effectively manage the well-being of their players. This includes identifying and monitoring meaningful metrics (i.e., training load, training intensity, heart rate variability), as well as careful planning of training and competition schedules with appropriate recovery periods. The use of innovative training methods (i.e., repeated-sprint training in hypoxia and heat training), and proper dietary practices, along with biometric assessment for young players, represents should be considered. Adopting a holistic approach to decision-making about training and competition, balancing both health and performance considerations, is crucial for tennis players and their support teams. More research is needed to refine best practices for enhancing tennis performance while prioritizing the well-being of players.

Research on dynamic scheduling and perception method of assembly resources based on digital twin

ABSTRACT The uncertainty and dynamic changes in assembly resources can seriously affect the normal operation of the assembly plant. In response to the problems of incomprehensive resource control mechanism, poor timeliness of monitoring data, and low level of scheduling intelligence in the assembly plant, a dynamic scheduling and perception method of assembly resources based on digital twin is proposed so that the uncertainties in the assembly process can be monitored and dealt with in time. In this paper, a dynamic scheduling model of assembly resources based on a digital twin is constructed, and the operation mechanism of assembly resources in the constructed digital twin model is expounded. And the dynamic perception method of assembly resources based on the Petri network is studied in detail, and the perception and interaction models of four assembly resources in the product assembly process are constructed: workpiece, handling equipment, assembly center, and storage area. Finally, combined with the assembly workshop of enterprise A’s frame factory, the Petri network model is simulated with the help of the CPN Tools simulation tool to obtain real-time and simulation data such as assembly resources and workstation operation time are obtained, which provides a scientific basis for the smooth implementation of enterprise assembly plan and dynamic scheduling of assembly resources.

Efficacy of Ayurvedic Formulations in Dyslipidemia (Medo Dusti)

Obesity (Overweight) is the condition arises as consequence of various metabolic disorder in which dyslipidemia is one of them. Dyslipidemia is described by an increase in Total Cholesterol, low density lipoprotein (LDL), triglycerides (TGA’s) concentration in the blood. In Ayurvedic classics it had been described as Medoroga in which excessive deposition of Meda Dhatu in body produces various symptoms. In the above case report patient comes with symptoms of Meda dusti and diagnosed on the basis of deranged lipid profile. The combination of Arogyavardhini vati, Navak guggul with Anupaan of Darvyadi Kwath was prescribed. After 34 days of drug intervention with scheduled diet plan there is a significant reduction in Triglycerides, Cholesterol and other parameters lies in the normal range, weight reduces (from 71kg to 68kg) within 30 days with BMI. The management of the disease results in formation of vitiated Kapha and meda dhatu through Dravya's of Lekhaniya Gana with Pathya sevan.

Joint Optimal Dispatch of Complex Urban Raw Water Supply: A Case Study of Lanxi City, Zhejiang Province, China

Water resources play a vital role in supporting urban economic and social development and ecological and environmental protection. Water shortage is a key factor that restricts the high-quality development of cities, while the coordinated and optimized allocation of urban raw water is an important measure to alleviate the water supply–demand imbalance. The current research on urban water supply issues and their underlying causes still needs to be further strengthened. Similarly, the formulation of rules for multi-source and cross-district water supply should pay more attention. This paper proposes a general analytical process consisting of four main stages: problem identification, system generalization, rule formulation, and model construction and solution for the joint optimal scheduling of raw water in a complex urban water supply system. This study investigates the significant water resource wastage and structural water shortage in the reservoirs of Lanxi City. The optimal scheduling plan is proposed by formulating rational rules for inter-district water supply and establishing a multi-source and multi-objective joint optimization scheduling model. Compared to the current independent scheduling scheme and multi-water source joint dispatching scheme based on the current dispatch diagram, the optimal scheduling plan effectively reduced the cumulative water shortage by 68.04 million m3 and 29.72 million m3, respectively, and increased the urban water supply guarantee rate in all districts of the city to over 90%. This study offers valuable insights to urban water resource managers, empowering them to develop optimal multiple water source supply rules that align to the specific characteristics of other case studies.

Inter-Breath-Hold Geometric and Dosimetric Variations in Organs at Risk during Pancreatic Stereotactic Body Radiotherapy: Implications for Adaptive Radiation Therapy.

Pancreatic cancer is the fourth leading cause of cancer-related death, with nearly 60,000 cases each year and less than a 10% 5-year overall survival rate. Radiation therapy (RT) is highly beneficial as a local-regional anticancer treatment. As anatomical variation is of great concern, motion management techniques, such as DIBH, are commonly used to minimize OARs toxicities; however, the variability between DIBHs has not been well studied. Here, we present an unprecedented systematic analysis of patients' anatomical reproducibility over multiple DIBH motion-management technique uses for pancreatic cancer RT. We used data from 20 patients; four DIBH scans were available for each patient to design 80 SBRT plans. Our results demonstrated that (i) there is considerable variation in OAR geometry and dose between same-subject DIBH scans; (ii) the RT plan designed for one scan may not be directly applicable to another scan; (iii) the RT treatment designed using a DIBH simulation CT results in different dosimetry in the DIBH treatment delivery; and (iv) this confirms the importance of adaptive radiation therapy (ART), such as MR-Linacs, for pancreatic RT delivery. The ART treatment delivery technique can account for anatomical variation between referenced and scheduled plans, and thus avoid toxicities of OARs because of anatomical variations between DIBH patient setups.

Review Kapasitas Daya Dukung Dan Tinjauan Waktu Pelaksanaan Bore Pile Pada Pembangunan Jembatan Jalan Bebas Hambatan Binjai Pangkalan Brandan

In the Sei Wampu Bridge work project, Binjai Pangkalan Brandan carried out bore pile foundation installation work at the A2 foundation point with a total of 21 piles. Review of the bearing capacity of bore pile foundations using Analysis from Meyerhof (1956) and the Converse-Labarre Formula method for the calculation of pile group efficiency. The time of completion of work based on the time schedule plan is 125 calendar days. This research conducted a review using the Critical Chain Project Management (CCPM) method to obtain time efficiency, so that the project can be carried out on time.
 From the results of analysis and discussion, it was obtained that the largest foundation carrying capacity was the result of laboratory data using method C. Significant differences in results were obtained, especially laboratory data due to the correlation value used in each method. The result of calculating the bearing capacity of the foundation based on the method of Luciano Decourt (1987) using N-SPT data is 287,600.76 kN; Meyerhof's (1956) method using N-SPT data is 265,062.68 kN; a combination of Meyerhof's method (1976), Vesic's method (1977); the combination of the Vesic method (1977), the Coyle and Castello method (1981) and the results of foundation carrying capacity analysis using the All Pile program is 41,159.85 kN.
 The Critical Chain Project Management method is defined as the longest chain of interrelated events, where the linkage lies in the work or resources that are interconnected with each other, by cutting 50% of the duration of the work and replacing it as a buffer. The time comparison obtained from the calculation results using the CCPM method obtained a time efficiency of 116 days with a work time difference of 7.2% or about 9 days from the existing duration of the previous 125 days, while to compare the results of the CCPM method, calculations were carried out based on realization for 97 days, where the realization results were smaller than using the CCPM method and time schedule plan.
 Keywords : Bore pile, Carrying capacity, Time review

Transportation scheduling for modules used in modular integrated construction

ABSTRACT Modular integrated construction (MiC) is an innovative construction method that is widely used in practice, but high transportation cost is an obstacle to its wider implementation. This paper is motivated by the real-world need for a scientific approach for MiC transport scheduling, and addresses the practical problem of how to achieve on-time door-to-door MiC delivery in an economical manner. An innovative integer programming model is formulated with the objective of minimising the total transportation costs, including trailer rental, fuel costs, and worker salaries. A case study based on Hong Kong data validates that the developed model can be efficiently solved for problems of practical size. The results also demonstrate that the derived MiC schedule plan can reduce transportation costs by 58.7% compared with a just-in-time (JIT) strategy, thus bringing great economic benefits to off-site construction practitioners. Useful managerial implications related to on-site MiC storage areas are derived through sensitivity analysis. Overall, this paper sets the ground for the application of mathematical programming methodology to the off-site construction field. Future research may be devoted to MiC transport scheduling under uncertainty.

Electric Load Forecasting Based on Deep Ensemble Learning

Short-to-medium-term electric load forecasting is crucial for grid planning, transformation, and load scheduling for power supply departments. Various complex and ever-changing factors such as weather, seasons, regional economic structures, and enterprise production cycles exert uncontrollable effects on the electric grid load. While the causal convolutional neural network can significantly enhance long-term sequence prediction, it may suffer from problems such as vanishing gradients and overfitting due to extended time series. To address this issue, this paper introduces a new power load data anomaly detection method, which leverages a convolutional neural network (CNN) to extract temporal and spatial information from the load data. The features extracted are then processed using a bidirectional long short-term memory network (BiLSTM) to capture the temporal dependencies in the data more adeptly. An enhanced random forest (RF) classifier is employed for anomaly detection in electric load data. Furthermore, the paper proposes a new model framework for electricity load forecasting that combines a dilated causal convolutional neural network with ensemble learning. This combination addresses issues such as vanishing gradients encountered in causal convolutional neural networks with long time series. Extreme gradient boosting (XGBoost), category boosting (CATBoost), and light gradient boosting machine (LightGBM) models act as the base learners for ensemble modeling to comprehend deep cross-features, and the prediction results generated by ensemble learning serve as a new feature set for secondary ensemble modeling. The dilated convolutional neural network broadens the receptive field of the convolutional kernel. All acquired feature values are concatenated and input into the dilated causal convolutional neural network for training, achieving short-to-medium-term electric load forecasting. Experimental results indicate that compared to existing models, its root mean squared error (RMSE) and mean squared error (MSE) in short-term and mid-term electricity load forecasting are reduced by 4.96% and 12.31%, respectively, underscoring the efficacy of the proposed framework.

Optimizing the battery charging and swapping infrastructure for electric short-haul aircraft—The case of electric flight in Norway

Recent advances in battery technology have opened the possibility for short-haul electric flight. This is particularly attractive for commuter airlines that operate in remote regions such as archipelagos or Nordic fjords where the geography impedes other means of transportation. In this paper we address the question of how to optimize the charging infrastructure (charging power, spare batteries) for an airline when considering a battery swapping system. Our analysis considers the expenditures needed for (i) the significant charging power requirements, (ii) spare aircraft batteries, (iii) the used electricity, and (iv) delay costs, should the infrastructure not be sufficient to accommodate the flight schedule. The main result of this paper is the formulation of this problem as a two-phase recourse model. This is required to account for the variation of the flight schedule throughout a year of operations. With this, both the strategic (infrastructure sizing) and tactical (battery recharge scheduling) planning are addressed The model is applied for Widerøe Airlines, with a network of 7 hub airports and 36 regional airports in Norway. The results show that a total investment of 4412 kW in electricity power supply and 25 spare batteries is needed for the considered network, resulting in a daily investment of €11700. We also quantify the benefits of considering an entire year of operations for our analysis, instead of just one congested day (7% cost reduction) or one average day of operations (31% reduction) at the most congested airport.

Integrated optimization of rolling stock allocation and train timetables for urban rail transit networks: A benders decomposition approach

We investigate the integrated optimization of rolling stock allocation and train timetables (RATT) in an urban rail transit network with multiple connected lines and rolling stock depots. Different from most existing research on single-line cases, we consider that the rail manager plans to allocate a certain fleet of rolling stock to depots to implement the operational timetables of multiple lines, where each depot can serve more than one line but has limited capacity. By means of the construction of a space–time network, we formulate RATT into a bi-objective mixed-integer linear programming (MILP) model. The model can simultaneously generate the rolling stock allocation plan and train schedules for the considered lines, with the aim of optimizing both the investment cost of rolling stock and the service quality of passengers. Due to the computational complexity of large-scale real-world instances, we develop a Benders decomposition-based solution algorithm, which decomposes the MILP into a rolling stock assignment problem and a set of independent subproblems, i.e., a train scheduling subproblem for each line. As the subproblems also contain integral variables, we further prove that a large portion of the subproblems, after proper reformulation, has the integrality property and can be solved rapidly via linear relaxation. We test our integrated approach and solution algorithms on real-world instances from the Beijing rail transit network. The results show that our solution approach can obtain near optimal solutions, while the commercial solvers cannot even return feasible solutions on real-world instances. Compared to the existing plan in the Beijing metro, our RATT approach can improve the service quality by 17.6%, on average, using the same fleet size of rolling stock.

Digital twin-driven real-time planning, monitoring, and controlling in food supply chains

There needs to be more clarity about when and how the digital twin approach could benefit the food supply chains. In this study, we develop and solve an integrated problem of procurement, production, and distribution strategies (PPDs) in a medium-scale food processing company. Using the digital twin approach, the model considers the industrial symbiosis opportunities between the supplier, manufacturer, and customer using interval and sequence variables operating in a constrained environment using mixed-integer linear programming (MILP) and agent-based simulation (ABS) methodology. The study optimizes the make-span and lead time, simultaneously achieving a higher level of digitalization. The analysis demonstrates how digital twin accelerates supply chain productivity by improving makespan time, data redundancy (DR), optimal scheduling plan (OSP), overall operations effectiveness (OOE), overall equipment effectiveness (OEE), and capacity utilization. Our findings provide compelling evidence that the seamless integration PPDs enormously enhance production flexibility, resulting in an excellent service level of 94 %. Managers leverage real-time simulation to accurately estimate the replenishment point with minimal lead time, ensuring optimized operations.Furthermore, our results demonstrate that implementing PPDs has yielded considerable benefits. Specifically, we observed a remarkable 65 % utilization of the pasteurizer and aging vessel and an impressive 97 % utilization of the freezer. Moreover, by applying the DT model, the present model found a notable 6 % reduction in backlog, further streamlining operations and enhancing efficiency.

A new approach to circularity assessment for a sustainable water sector: Accounting for environmental functional flows and losses

Resource recovery solutions can reduce the water sector's resource use intensity. With many such solutions being proposed, an assessment method for effective decision-making is needed. The water sector predominantly deals with biogeochemical resources (e.g., nitrogen) that are different from technical resources (e.g., industrial coagulants) in three ways: (1) they move through the environment in natural cycles; (2) they fulfil different human and environmental functions; and (3) they are subject to substantial environmental losses. Whilst several circularity assessment methods exist for technical resources, biogeochemical resources have received less attention. To address this, a well-established material circularity indicator (MCI) method is modified. This is done by redefining the terms: restoration, regeneration, and linear flows to create a new circularity assessment approach. The new approach is demonstrated in a real-life case study involving treated wastewater (TW) fertigation. The new approach reveals that using the original MCI method underestimates the circularity of resource recovery solutions involving biogeochemical resources. This is because, in the original MCI method, only the flows that are reused/recycled for human functions can be considered circular, whereas, in the new approach, one also considers flows such as N2 emission and groundwater infiltration as circular flows. Even though these may not be reuse/recycle type flows, they still contribute towards future resource availability and, thus, towards sustainability. The modified assessment method shows that TW fertigation can significantly improve nitrogen and water circularity. However, careful planning of the fertigation schedule is essential since increasing fertigation frequency leads to lower water but higher nitrogen circularity. Additionally, collecting drainage water for reuse can improve nitrogen circularity. In conclusion, using the modified MCI approach, circularity can be assessed in a manner that is better aligned with sustainability.

Optimal Collaborative Scheduling of Multi-Aircraft Types for Forest Fires General Aviation Rescue

The scheduling of rescue aircraft needs to be studied in depth because of its criticality for the general aviation rescue of forest fires. This paper constructs a collaborative schedule optimization model for general aviation rescue under the condition of multiple aircraft, from multiple rally points to multiple fire points, targeting the shortest rescue time and the lowest rescue cost in the context of forest fires based on the simulation verification of a forest fire that broke out simultaneously in multiple locations in Liangshan Prefecture, Sichuan Province, China. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm was used to find the optimal set of solutions satisfying the objective function: four feasible solutions. Then, the optimal solution was solved based on the weighted TOPSIS method, which was the optimal solution for this rescue task. The simulation results show that unnecessary flight times can be reduced by optimizing the schedule plan. Under the premise of ensuring rescue timeliness, the utilization rate of rescue aircraft was improved, and rescue costs were further reduced. The presented work provides a theoretical reference for the efficient development of general aviation rescue.

An intelligent system for constructing metro train driver working schedules

Aim. The paper aims to tests and analyse the results of application of an intelligent system for constructing metro train driver working schedules that is intended for automatically improving the efficiency of utilisation of the working time of metro train drivers. In this case, depending on the task at hand and the chosen criterion, efficiency is understood as a reduction of the number of drivers involved in the implementation of the specified standard train schedule, improved uniformity of alternation of driver work and rest periods, as well as reduction of the duration of down time within work shifts.Methods. The study presented in the paper uses the graph theory, recursive and heuristic algorithms.Results. The authors have developed recursive algorithms for preparing work schedules for main metro train drivers and substitute drivers that operate during main drivers’ lunch breaks. The developed algorithms are used in the intelligent system for metro train driver work schedule planning. The algorithm for generating main driver work schedule includes driver allocation to a number of work lines preliminarily defined based on the rolling stock operation schedule for the purpose of ensuring traffic according to the planned metro train schedule. The algorithm for generating substitute driver work schedule involves substitute driver work time planning based on the possibility of arranging lunch breaks for main drivers.Conclusion. The paper presents the results of a trial of the developed intelligent system for train driver work planning for the Moscow Metro using the example of the Zamoskvoretskoye Depot of the Zamoskvoretskaya Line, the Vykhino Depot of the Tagansko-Krasnopresnenskaya Line, as well as the results of the system’s adaptation to the Moscow Central Circle. It also presents a comparative analysis of driver work schedules, i.e., the actual one and one obtained using the developed intelligent system. The application of the developed system may enable as much as a 28% improvement of the efficiency of metro train drivers’ work hour use.

Surgical cases assignment problem using a multi-objective squirrel search algorithm

Surgical cases assignment problem (SCAP) is among the most investigated interests in operating room planning. The studies related to the SCAP are mainly focused on the single objective optimization. To concern multiple criteria in the management, this paper reformulates a multi-objective SCAP with minimizing total operating cost and maximizing scheduled surgeries number, simultaneously. Although dozens of multi-objective methods have been introduced in recent years, these cannot be applied into the multi-objective SCAP directly considering the characteristics of the problem. To tackle the multi-objective SCAP, a problem-specified multi-objective squirrel search algorithm (MOSSA) is presented. First, a targeted single-list scheme is devised to encode individuals and an efficient decoding scheme integrating a repair strategy is proposed to construct feasible scheduling plan. Second, two simple heuristics are incorporated into the algorithm to imitate the behaviors of squirrels and improve the search ability. Third, an addition archive is employed to store the non-dominated solutions. Moreover, a DOE method is performed to investigate the influence of parameter settings. The performance of the MOSSA is assessed by a representative benchmark dataset with three different evaluating indicators. Extensive experiments and analysis results demonstrate the effectiveness of the proposed algorithm and the superiority over compared approaches in addressing the multi-objective SCAP.

Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction.

A Blockchain-Based Shared Bus Service Scheduling and Management System

With the continuous development of urbanization, it has become an important issue to effectively alleviate urban road traffic congestion and improve traffic efficiency. By combining blockchain technology and shared buses, this paper builds an intelligent traffic-service scheduling management system based on blockchain. The system effectively solves the core problems of shared buses, improves data security and privacy protection, realizes intelligent scheduling and route planning, and simplifies cross-organization cooperation and settlement processes. The research shows that the system can reduce the distance and number of buses, and improve the service quality and operation efficiency while ensuring the waiting time of passengers. The results of this paper verify the feasibility and advantages of the system, bring innovation and improvement to the field of traffic management, and promote the sustainable development of urban intelligent traffic management system. Future research could further explore the application of blockchain technology in traffic management to achieve more intelligent and sustainable urban traffic management.

Urban virtual power plant operation optimization with incentive-based demand response

Urban virtual power plant (VPP) shows great potential in alleviating urban power shortage or power supply-demand imbalance. This study proposes a bi-layer optimization model considering incentive-based demand response (IDR) to optimize the operation scheduling of urban VPP. The bi-layer optimization model includes the lower-layer IDR model and the upper-layer urban VPP optimal operation scheduling model. In the lower-layer IDR model, user satisfaction, comfort and preference are considered. Users mainly participate in the scheduling of urban VPP by IDR. The demand response resources obtained by urban VPP through providing incentives for users can effectively alleviate the energy supply pressure. In the upper-layer urban VPP optimal operation scheduling model, the scheduling plans for generating units and trading plans for the electricity wholesale market can be determined. Through upper-layer scheduling, it can not only meet the energy demand of users, but also minimize the urban VPP operation cost. The experimental results show that the proposed bi-layer optimization model for urban VPP operation considering IDR can achieve urban energy resources optimal allocation and support urban energy supply-demand balance, on the premise of ensuring economic benefits.

Tracking Photovoltaic Power Output Schedule of the Energy Storage System Based on Reinforcement Learning

The inherent randomness, fluctuation, and intermittence of photovoltaic power generation make it difficult to track the scheduling plan. To improve the ability to track the photovoltaic plan to a greater extent, a real-time charge and discharge power control method based on deep reinforcement learning is proposed. Firstly, the photovoltaic and energy storage hybrid system and the mathematical model of the hybrid system are briefly introduced, and the tracking control problem is defined. Then, power generation plans on different days are clustered into four scenarios by the K-means clustering algorithm. The mean, standard deviation, and kurtosis of the power generation plant are used as the features. Based on the clustered results, the state, action, and reward required for reinforcement learning are set. In the constraint conditions of various variables, to increase the accuracy of the hybrid system for tracking the new generation schedule, the proximal policy optimization (PPO) algorithm is used to optimize the charging/discharging power of the energy storage system (ESS). Finally, the proposed control method is applied to a photovoltaic power station. The results of several valid experiments indicate that the average errors of tracking using the Proportion Integral Differential (PID), model predictive control (MPC) method, and the PPO algorithm in the same condition are 0.374 MW, 0.609 MW, and 0.104 MW, respectively, and the computing time is 1.134 s, 2.760 s, and 0.053 s, respectively. The consequence of these indicates that the proposed deep reinforcement learning-based control strategy is more competitive than the traditional methods in terms of generalization and computation time.