Load Planning Research Articles

Optimization of cargo distribution for high-speed freight trains to overcome strong wind conditions

The high-speed freight train (HSFT) is an emerging freight transportation mode that can play a significant role in the development of remote cities. In this study, we propose an optimization method for cargo distribution to prevent HSFT from overturning in strong winds. The multibody dynamics (MBD) HSFT model is built and imposed with aerodynamic loads derived through the computational fluid dynamics method. Based on the MBD simulation results, the prediction model of overturning safety for HSFT is established using gradient-boosting decision tree to make the value of cargo height continuous. Consequently, a nonlinear programming model for the cargo distribution problem is built and solved. The MBD simulation results reveal that the outer side crosswind is good for large vehicle velocity and the vehicle in the rear position of HSFT is safer. The case study demonstrates that the proposed optimization method can decrease the overturning risk for HSFT by more than 10% compared with the even distribution scheme. This research will contribute to making the loading plan for the rail company, ensuring the operational safety of HSFT in the windy area.

Design of Comfortable Structure of Junior High School Building By Using ETABS Software

Ibnu Abbas Foundation intends to build a four-story school building for junior high school in Talun District, Cirebon Regency. This study aims to design a school building structure that is comfortable, safe, and meets earthquake-resistant standards using ETABS software. The design process includes structural planning, analysis of building response to static and dynamic loads such as dead load, live load, wind load, and earthquake load, as well as detailed design of structural elements such as floors, columns, beams, and foundations. This research method uses a calculation approach that refers to the applicable Indonesian National Standards (SNI), namely SNI 2847: 2019 for structural planning, SNI 1727: 2020 for load planning, and SNI 1726: 2019 for earthquake resistant planning. The planning process uses the Response Spectrum SRPMK method and is assisted by ETABS software for structural analysis and design. The planning results show that the four-story school building structure can be built safely using a pile foundation with a depth of 4.4 meters and diameters of 50 and 60 cm. The foundation is able to withstand the load of the planned structure. The structural elements of the school building are planned using reinforced concrete with a material quality of K-300 for the frame and K-350 for the columns. The main reinforcement used is BJTS 420A, and the transverse reinforcement is BJTP 280.

A model for container stowage planning considering stability constraints

The growth in global trade has led to the deployment of mega-container ships to meet increasing transportation demands. However, the larger vessel sizes pose significant challenges for planners in arranging container loading. This study addresses the container stowage planning problem for near-sea routes, incorporating stability constraints, container types, and technical limitations related to stack weight and stress. We propose a mixed integer linear programming model to describe this problem and generate load plans that minimize total costs associated with re-stowing and bending moments (BM). To solve the model efficiently, we design a dynamic weight and time-varying particle swarm optimization (PSO) algorithm, which combines particle classification and time-varying acceleration constants. The effectiveness of the model and the efficiency of the algorithm are validated through extensive numerical experiments. The results demonstrate that our proposed algorithm outperforms the traditional PSO algorithm in both solution quality and computation time. Additionally, sensitivity analyses are conducted to derive potentially useful managerial insights for supporting shipping liners’ decision-making. This study highlights the benefits of particle classification and time-varying acceleration constants, and reveals the influence of container bay capacity and permitted frame BM.

A new low carbon scheduling method for source load multi time scale in power systems

Abstract The impact of global climate change caused by carbon dioxide emissions on human production and life is becoming increasingly severe. New energy sources, led by wind and solar power, have become the backbone of reducing carbon emissions. The main focus of this article is to develop advantages such as multi-energy complementarity, efficient integration, flexibility and stability, and low-carbon environmental protection. While meeting the diverse energy needs of the energy consumption side, it is necessary to reasonably allocate resources on the energy supply side and effectively absorb renewable energy. To begin with, this paper develops a multi-time-scale economic dispatch model for a holistic energy system, focusing on collaborative carbon reduction between energy sources and loads. The upper layer serves as a long-term energy scheduling mechanism, dynamically updating electricity prices and conveying them to the lower layer model. The lower layer functions as a short-term energy scheduling component, directing load management according to electricity prices and providing feedback on the load plan to the upper layer model. Both the upper and lower-layer models strive to minimize the overall operational costs as their primary objective. Secondly, the probability distribution of wind and photovoltaic power is discretized using sequence operation theory. The CPLEX is used to solve the above model. Finally, taking a comprehensive energy system as an example, the impact of source load side carbon reduction measures on the system scheduling results under multiple time scales is analyzed.

Design of cold storage as a storage space for animal vaccines at PT. XYZ

A refrigeration system is a system used to cool a room or an industrial product in the food, pharmaceutical, beverage, vegetable, fruit, and other industries to maintain product quality. One of its uses is as storage for various kinds of pharmaceutical needs, including vaccines for both animals and humans. The problem with cold storage is that existing vaccine storage has been in operation for a long time so it is necessary to study whether it is still able to maintain the vaccine storage temperature, namely -80C. The aim of designing cold storage is to maintain a stable vaccine storage temperature. Because vaccines consist of various kinds of microorganisms that are easy to activate. To maintain the quality of the vaccine, cold storage is needed. The research methodology for writing this thesis is to collect design data from one of the vaccine storage industry companies, and literature studies, then analysis of cooling load planning studies, vaccine storage capacity in cold storage measuring 10 x 10 x m 3 meters and determine the power of the cooling machine using a system. standard vapor compression cooler. The results of the analysis of the refrigeration system planning study showed that the indoor vaccine capacity was 30,600 bottles or 2056,701 lbs (822,680 kg), storage temperature -80C, cooling load 308,133 Btu/hr, or 90,283 W, this system uses R 134 A refrigeration. Using refrigerant, power compressor 23.32 kW, or 32 HP, COP 3.9.

Maximizing profitability in integrated energy markets: A novel framework for multi-faceted energy trading

The emergence of Integrated Energy Systems (IESs) has shifted energy management from traditional electricity paradigms to a more diverse approach involving electricity, thermal energy, and natural gas. This study introduces a framework for an Integrated Energy Network (IEN) that enables simultaneous trading across these interconnected markets. The objective is to maximize the profitability of integrated energy companies by optimizing energy trading strategies. The proposed business strategy incorporates integrated load response, addressing constraints within electric distribution systems, heating networks, and natural gas pipelines. A three-stage process is used: information sharing in the unified energy market, energy conversion in hubs, and flexible load planning in the retail market. Flexible loads are categorized as displaceable, reducible, or transformable. A Conditional Value-at-Risk (CVaR) mean-variance model addresses market price volatility. Simulations on a test system validate the model, demonstrating its effectiveness in improving profitability and operational efficiency for integrated energy companies.

Developing a Container Ship Loading-Planning Program Using Reinforcement Learning

This study presents an optimized container-stowage plan using reinforcement learning to tackle the complex logistical challenges in maritime shipping. Traditional stowage-planning methods often rely on manual processes that account for factors like container weight, unloading order, and balance, which results in significant time and resource consumption. To address these inefficiencies, we developed a two-phase stowage plan: Phase 1 involves bay selection using a Proximal Policy Optimization (PPO) algorithm, while Phase 2 focuses on row and tier placement. The proposed model was evaluated against traditional methods, demonstrating that the PPO algorithm provides more efficient loading plans with faster convergence compared to Deep Q-Learning (DQN). Additionally, the model successfully minimized rehandling and maintained an even distribution of weight across the vessel, ensuring operational safety and stability. This approach shows great potential for enhancing stowage efficiency and can be applied to real-world shipping scenarios, improving productivity. Future work will aim to incorporate additional factors, such as container size, type, and cargo fragility, to further improve the robustness and adaptability of the stowage-planning system. By integrating these additional considerations, the system will become even more capable of handling the complexities of modern maritime logistics.

REVENUE FORECASTING SCENARIOS FOR INTERNATIONAL HOTEL CHAINS

The purpose of the study is to develop a system of balanced scorecards for forecasting the income of hotels belonging to international chains. The subject of the study is the system processes of evaluating the effectiveness of the management analysis system, which are determined on the basis of calculating key performance indicators, and as a result of the integral indicator through the analysis and aggregation of individual functional criteria based on comparison with the reference values of the revenue management model for pricing, load planning and its redistribution between revenue centres. Methodology. The study uses the methods of theoretical and logical generalisation. The article describes a set of strategies and tactics used by international hotel chains to manage the demand for hotel services. The results of the article are to form a model for conducting a detailed operational and financial analysis of the hotel enterprise by revenue centres, which contributes to the development of a strategy. The authors have selected a set of indicators that are used to ensure a balanced approach to measuring performance through the indicator method and visual representation in a graphical representation. The paper analyses the performance of international hotel chains over twelve years in order to assess the impact of key factors on their revenues and develop forecasts; the estimated indicators were classified into seven groups: assessment of the average daily revenue per room, room cost, occupancy rate, market share, staff productivity, resource intensity and digitalisation costs. The publication uses the example of international hotel chains to clarify the content and importance of revenue forecasting in the revenue management system.

Stochastic weather simulation based on gate recurrent unit and generative adversarial networks

AbstractThe weather has a significant impact on power load and power system planning. Stochastic weather simulation is important in the field of power systems. However, due to factors such as long recording years, observation technology, and so on, the historical weather data often have the problem of missing or insufficient. Meteorological data are characterized by changeable, rapid change, and high dimensions. Therefore, it is a challenging task to accurately grasp the law of weather data. This article presents a random weather simulation model based on gate recurrent unit (GRU) and generative adversarial networks (GAN). GRU selectively learns or forgets what was in the previous moment during training; it can learn the previous and current data of the time series data. When combined with the GAN, it will produce data with the same distribution as the original weather data. The proposed method was evaluated on a real weather dataset, and the results show that the proposed method outperforms the other contrast algorithms.

The Influence of the Playing Surface on Workload Response in Spanish Professional Male Soccer Players.

This study aimed to quantify the influence of the playing surface on workload-related variables (i.e., external load, Rate of perceived exertion (RPE), and mental load) in training sessions with a Spanish professional soccer team. Twenty professional male players from the same soccer team were involved. A total of thirty training sessions related to the preseason period were included. All the players completed training sessions on three playing surfaces: natural turf of poor quality, natural turf of high quality, and third-generation artificial turf. Monitoring during sessions involved assessing internal load (i.e., RPE and mental load) via self-reported questionnaires, and external load using Global Positioning System devices. Linear mixed models showed that RPE was significantly higher on natural turf of high quality than on natural turf of poor quality (p < 0.001). Total distance, relative total distance, the number of accelerations, decelerations, and high metabolic load distance were significantly lower on third-generation artificial turf compared to natural turf of poor quality (p < 0.001) and high quality (p < 0.001). In addition, high-speed running, sprint running distances, and the number of sprints reached higher values on third-generation artificial turf compared to the other two playing surfaces. These findings highlight the need for coaches to consider the type of training surface in soccer to optimize training load planning and prevent injuries.

Mining Enterprise Production Line Optimal Load Modelling

The article is devoted to the issues of the mining enterprise production line optimal load model. A large number of issues to related to alternative production plan implementation transition are considered in various works. A mathematical model to form an optimal plan mining enterprise raw material mixture from deposits is proposed in the study. A method of multi-criteria alternative ranking in terms of integral efficiency indicator with the following problem solution of optimal plan determination for a raw material mixture from deposits was chosen for that purpose. The article proposes an approach to determine the production line optimal loading plan of a mining enterprise based on the creation of an ore mixture with the highest utility. The value of a particular deposit is determined by a variety of factors, including the distance from the production line, the ore content in the rock, and the volume of the deposit. The paper shows that the development of a specified field management can be implemented based on solving linear programming problems. The hierarchy contains three levels: the system purpose; the factors influencing the deposit appeal (the metal content, the deposit volume, and transport accessibility); alternatives (deposits of raw materials directly). Let's assume that it is necessary to find the maximum efficiency by means of an ore mixture from the given deposits. Resource restrictions are imposed on the task: the work and machine time fund, the amount of first type reagent, and the amount of second type reagent. The composition of the constraint vector can be set depending on the value of the resources. The study found that the optimal plan is implemented under the complete resource utilization both of the first and the second types and a fifty-three percent level of the third resource utilization. Despite the low integral assessment of the raw material value of the fourth deposit, which is mainly determined by the low ore content in the rock, with respect to other deposits, its maximum utilization is also recommended, which in turn, is determined by the low resource intensity of ore extraction from this rock.

World Modeling for Autonomous Wheel Loaders

This paper presents a method for learning world models for wheel loaders performing automatic loading actions on a pile of soil. Data-driven models were learned to output the resulting pile state, loaded mass, time, and work for a single loading cycle given inputs that include a heightmap of the initial pile shape and action parameters for an automatic bucket-filling controller. Long-horizon planning of sequential loading in a dynamically changing environment is thus enabled as repeated model inference. The models, consisting of deep neural networks, were trained on data from a 3D multibody dynamics simulation of over 10,000 random loading actions in gravel piles of different shapes. The accuracy and inference time for predicting the loading performance and the resulting pile state were, on average, 95% in 1.2 ms and 97% in 4.5 ms, respectively. Long-horizon predictions were found feasible over 40 sequential loading actions.

Pengaruh Angin Terhadap Tata Letak dan Jumlah Sekrup Konstruksi Baja Ringan Berdasarkan SNI-7971- 2013

Light weight steel material has experienced rapid development in the construction. However, there are still many costruction failures on light weight steel, resulting in cost loss. The failure of light steel structures often occurs at the connections. The connection tool on milld steel is usually using a self-drilling screw. The research location is at Aceh One Stop Integrated Service and Investment Service Building (DPMPTSP) Aceh. The problem in this study is the placement and number of screws on the lightweight steel roof structure in the field which is carried out for each member according to the code, and the effect of wind load on the placement and number of screws on the lightweight steel roof structure. This study aims to determine the effect of the placement and number of screws on the lightweight steel roof structure. The scope of this research of problems including the placement and number of screws on the lightweight steel roof structure. The standars used in the design of light steel structures in this study is SNI 7971 2013 and the loading planning standars using SNI 03 1727 1989. Calculation steps using the LRFD method for steel structure design regulated in SNI 03 1729 2002. Based on the results, it is obtained that the number and placement of the screws in the field are different from those obtained in the calculation. From the calculations, it is obtained from the recapitulation, dead loads with a maximum value of 123.07 kg, live loads with a maximum load of 30.1 6 kg, and for compressed air and suction loads with a maximum load of 180 kg. the maximum design force value is 1241.33 kg and the screws bearing resistance value is 256.752 kg. the number of screws is obtained where the the value of the design force is divided by the value of the bearing resistance, on each member obtained 1screw, 2 screws, 3 screws, 4 screws, and 5 screws. Whilst it is found for each member in the field using 3 screws.

Efficient Model for Waste Load and RouteOptimization

Urbanization frequently gives rise to substantial environmental issues, namely in waste management and water quality maintenance. Gross Pollutant Traps (GPTs) are essential in urban stormwater management as they effectively capture substantial pollutants before they enterthe centralwater bodies. Nevertheless, the irregular buildup of trash caused by fluctuating rainfall intensity hinders the effective transfer of garbage from GPTs to their ultimate disposal locations. This research presents a holistic approach toenhancing the efficiency of waste transportation by improving route and load planning. The model utilizes machine learning techniques to forecast the quantity of waste collected by GPTs. We have created an optimization algorithm that usesthe forecast outcome from a prior research dataset. This algorithm is designed to efficiently plan the routes and loads for trucks responsible for transporting waste to its final disposal location. The optimization process consideredthe estimated amounts of garbage, the capacities of the vehicles, and the locations of the disposal sitesto reduce transportation expenses and save time. The system adaptively optimized routes using real-time data on the vehicle'sorigin and destination, ensuring effective allocation of resources and prompt garbage removal. Installingthis approach resulted in a substantial decrease in transportation expenses and enhanced compliance with waste pickup timetables. The integration of predictive modelingand route optimization is enhancing urban trash management. Accurate garbage quantity forecasts and optimized transportation logistics can enable municipalities to deploy resources more effectively, decrease operational costs, and improve environmental protection. We chose a subset of 7 days, equivalent to one week, from the projected dataset for our experiment.Subsequently, we conductednumerous trials involving various waste disposalfrequencies. The findings suggest that waste disposalevery four(4) days is the most advantageous approach. Still, itperforms similarlyto waste disposalevery three (3)days and has negligible environmental consequences. Hence, we select to execute the optimal solution for three(3) days, as it provides exceptional performancewhen consideringthe influence of natural pollution.

Analysis of the Application of Static Load Test in Bridge Bearing Capacity Testing

This article uses real engineering projects as examples to analyze how static load test technology is applied in testing the bridge-bearing capacity. The analysis covers aspects such as testing section layout, test load and efficiency coefficient, loading plan, evaluation optimization, test result modification, and result evaluation. The aim is to support the accurate detection and evaluation of bridge-bearing capacity.

Pengaruh Angin Terhadap Tata Letak dan Jumlah Sekrup Konstruksi Baja Ringan Berdasarkan SNI-7971- 2013

Light weight steel material has experienced rapid development in the construction. However, there are still many costruction failures on light weight steel, resulting in cost loss. The failure of light steel structures often occurs at the connections. The connection tool on milld steel is usually using a self-drilling screw. The research location is at Aceh One Stop Integrated Service and Investment Service Building (DPMPTSP) Aceh. The problem in this study is the placement and number of screws on the lightweight steel roof structure in the field which is carried out for each member according to the code, and the effect of wind load on the placement and number of screws on the lightweight steel roof structure. This study aims to determine the effect of the placement and number of screws on the lightweight steel roof structure. The scope of this research of problems including the placement and number of screws on the lightweight steel roof structure. The standars used in the design of light steel structures in this study is SNI 7971 2013 and the loading planning standars using SNI 03 1727 1989. Calculation steps using the LRFD method for steel structure design regulated in SNI 03 1729 2002. Based on the results, it is obtained that the number and placement of the screws in the field are different from those obtained in the calculation. From the calculations, it is obtained from the recapitulation, dead loads with a maximum value of 123.07 kg, live loads with a maximum load of 30.1 6 kg, and for compressed air and suction loads with a maximum load of 180 kg. the maximum design force value is 1241.33 kg and the screws bearing resistance value is 256.752 kg. the number of screws is obtained where the the value of the design force is divided by the value of the bearing resistance, on each member obtained 1screw, 2 screws, 3 screws, 4 screws, and 5 screws. Whilst it is found for each member in the field using 3 screws.

Study on the Relationship between Port Governance and Terminal Operation System for Smart Port: Japan Case

Background: To improve port productivity, safety, and sustainability, the use of information and communication technology is being promoted as a smart port. The utilization of a terminal operation system (TOS) is important for advanced port operations, and it is necessary to organize the issues and characteristics of the TOS. Methods: The characteristics of TOSs introduced in Japan and those widely introduced in Europe and Southeast Asia will be investigated and discussed according to the port management system in Japan. Results: Japanese TOSs are characterized by a lack of automated functions, such as ship loading plans, and by the fact that they are designed to allow the crane driver to select the order of operations, which may be attributed to a system wherein stakeholders are segmented and on-site decisions are emphasized. The promotion of smart ports in Japanese-style ports requires a system for information linkage between stakeholders. Conclusions: TOS capabilities for smart ports should be implemented according to the characteristics of port management in each region, and the studies conducted in this paper are useful in examining port system implementation strategies.

A multi-agent reinforcement learning approach for ART adaptive control in automated container terminals

Intelligent transportation equipment represented by Artificial Intelligence Robot of Transportation (ART) has been gradually applied to automated container terminals (ACT). ARTs have higher intelligence and distributed autonomous decision-making capability compared to AGVs. The production and operation of ACT require strict loading plans based on container types and destination ports, which puts strict requirements on the ART loading task sequence. ART travel time from the yard to the quay is highly uncertain due to special scenarios such as traffic flow convergence and turning in narrow areas, which will lead to long waits for ART due to out-of-sequence arrivals at the quay. Therefore, it is necessary to regulate the speed of ART to adjust the timing of ART arriving at the quay. However, existing scheduling systems tend to ignore the impact of ART speed and loading sequence constraints on terminal operations. To optimize the multi-ART control problem of ACT, the waiting time of ART at the quay is taken as the optimization goal, and the speed regulation of ART during each task is defined as a Markov decision process. Then, by incorporating reinforcement learning and a multi-agent approach, an adaptive decision-making method is developed to generate the optimal speed regulation strategy. This method interacts with the multi-agent simulation environment to obtain real-time status information and learns strategies online to improve the intelligence of individual agents and the collaborative capabilities of the agent group. Finally, a multi-agent simulation model embedded with a reinforcement learning agent is established on AnyLogic software. Simulation results show that this method can effectively reduce the waiting time of ART, improve the coherence and efficiency of loading operations, and exhibit good performance in online scenarios that consider emergency tasks. The results indicate that the speed of ART in ACTs with parallel layout of the yard can impact the operational efficiency of the system. Furthermore, by appropriately defining the reward function and training method, collaborative behaviour among equipment can be enhanced, ultimately optimizing the overall performance of the system.

Improving Cargo Transportation Planning Based on Digital Technologies

From the beginning of 2023, JSC Russian Railways has been gradually connecting logical controls to limit the acceptance of applications for the transportation of goods of the GU-12 form beyond the infrastructural capabilities of JSC Russian Railways. The presence of restrictions will be determined based on the results of calculations of the Dynamic Infrastructure Load Model (DILM) of JSC Russian Railways. This control limits the possibility of coordinating requests for transportation and the daily client loading plan (DCPP) beyond the calculated capabilities of the infrastructure. In addition, the automated system “Electronic Consignment Note” (AS ETRAN) implements automatic consideration of requests for transportation in all types of traffic with destination to limiting directions. The operation of the system is based on forecasting the occupancy of infrastructure elements for the entire period of transportation planning using data on operations with trains/cars in motion, loaded but not yet dispatched cars, as well as previously agreed upon requests for transportation. Data on the parameters of public infrastructure facilities are taken into account, including taking into account the implementation of planned repairs and construction and installation work, as well as non-public railway tracks. The system implements the determination of possible alternative schedules in the absence of the possibility of acceptance for transportation in accordance with the schedule declared by the shipper.

The puzzle of monitoring training load in winter sports - A hard nut to crack?

Elite winter sports have become increasingly professionalized and commercialized, often accompanied by congested competition schedules. This has brought the topic of load management, i.e., planning, monitoring, and adaptation, enormous attention in the scientific community. In this review, we summarize the general knowledge from various sports about training load and its effects on injuries and illnesses. After outlining the fundamentals of load monitoring, we present common monitoring tools, i.e., neuromuscular performance tests, heart rate measures, blood-based biomarkers, and questionnaires and discuss their potentials and pitfalls in application. Studies have identified training load-related variables, but also other factors such as travelling, previous injuries, or age contributing to the occurrence of illness and injury. Based on our findings, the use of monitoring tools must be context-specific and long-term, considering statistical aspects as well. Promising study designs and future perspectives are finally highlighted, with the general conclusion that there is still a clear need for research on this topic in general and in the field of winter sports in particular.