Intelligent Scheduling System Research Articles

The Impact of the Optimization Strategy of the Public Transportation System on Sustainable Urban Development

As urbanization accelerates and awareness of environmental conservation rises, the optimization and modernization of public transportation systems, crucial to urban infrastructure, encounter novel challenges and prospects. While public transportation has evolved in the past few years, the city still grapples with issues of inefficiency and poor passenger contentment, hindering its sustainable growth. In this context, there's an immediate need to investigate the ways in which creative optimization strategies can enhance the efficiency of public transit systems and aid in building cities that are both environmentally sustainable and conserve resources. Based on the above, this paper focus on the optimization strategy of the public transportation system and its impact on sustainable urban development. Firstly, the analysis focuses on the current status and challenges of public transportation systems, examining how these relate internally to objectives of sustainable development. Secondly, the leading strategies and technical frameworks for optimizing public transportation systems are presented from theoretical and technical aspects, especially the application value of big data analysis, intelligent scheduling systems, and energy-saving and emission-reduction technology. In addition, the researchers constructed a model to analyze the positive effects of optimization strategies on urban traffic flow, system efficiency, and environmental quality. They verified the actual effectiveness of these strategies through case analysis. To sum up, the research highlights the vital importance of effective and eco-friendly public transportation in lessening city traffic jams, enhancing the living standards of inhabitants, and curtailing carbon emissions. Furthermore, it offers practical experience and strategic advice for other cities.

Design and Application of Intelligent Scheduling System Based on Business and Labor Forecasting

Design and Application of Intelligent Scheduling System Based on Business and Labor Forecasting

Application of integrating reinforcement learning and intelligent scheduling in logistics distribution

Logistics distribution is an indispensable part of the modern economy and is crucial for ensuring the efficient operation of the supply chain. With the continuous progress of technology, the application of intelligent scheduling systems in the field of logistics distribution is becoming increasingly widespread. Reinforcement learning, as one of the hot technologies in the field of artificial intelligence, is gradually receiving attention in its application in intelligent scheduling. Reinforcement learning can continuously learn and predict the same thing to enhance memory, while intelligent scheduling requires continuous prediction and optimization of logistics distribution paths. In response to the current problems of slow logistics distribution efficiency and low customer satisfaction, this article analyzed the application of intelligent scheduling in logistics distribution from the aspects of basic data maintenance, basic data review, intelligent scheduling, scheduling result review, distribution information management, and vehicle tracking. By using reinforcement learning, the traffic network weight in logistics distribution was studied to improve logistics distribution efficiency and customer satisfaction. This article analyzed the efficiency of logistics distribution, vehicle tracking accuracy, vehicle scheduling ability, and logistics distribution costs under different logistics distributions. The results showed that the logistics distribution under the integration of reinforcement learning and intelligent scheduling reduced 12.047 km compared to traditional distribution paths, and its distribution cost decreased by 129.718 yuan compared to traditional logistics distribution costs. The efficiency of logistics distribution that integrates reinforcement learning and intelligent scheduling has significantly improved, with optimized distribution costs and paths. It also has a positive effect on improving the utilization rate of logistics distribution vehicles.

Multi-cloud resource scheduling intelligent system with endogenous security

<abstract> <p>A secure and reliable intelligent multi-cloud resource scheduling system in cyberspace is especially important in some industry applications. However, this task has become exceedingly challenging due to the intricate nature of information, the variety of knowledge representations, the compatibility of diverse knowledge reasoning engines, and the numerous security threats found in cloud networks. In this paper, we applied the endogenous security theory to the multi-cloud resource scheduling intelligent system and presented a novel model of the system. The proposed model incorporates various knowledge representations and inference engines, resulting in a multi-cloud resource scheduling intelligent system that ensures endogenous security. In addition, we have devised a scheme for an intelligent system that schedules multi-cloud resources using dual-channels and has an endogenous security mechanism, which we have named Dynamic, Heterogeneous, and Redundant (DHR). Finally, we have used the multi-cloud resource scheduling intelligent run log database to carry out numerous experiments to validate the efficiency of the dual-channel redundant reasoning system with the endogenous security mechanism's DHR property. The results of the experiment demonstrated that the multi-cloud resource intelligent scheduling system model with an endogenous security mechanism was superior to the current single-channel inference system scheme in regards to security and reliability.</p> </abstract>

Learning to schedule dynamic distributed reconfigurable workshops using expected deep Q-network

Distributed manufacturing has become common in the globalized production environment. Facing the tendency of mass customization, distributed workshops are required to have the reconfiguration capability to produce multiple products. Based on the scheduling demands of distributed workshops, this paper studied the dynamic distributed reconfigurable flowshop scheduling problem (DRFSP) with new job arrivals using deep reinforcement learning (DRL) for the first time. The DRL-based intelligent scheduling and reconfiguration systems are modeled by designing efficient rewards, actions, and state features for both scheduling and reconfiguration agents. A new state feature normalization approach is proposed to address the variability in production configurations and dynamics. A novel expected deep Q-network (EDQN) algorithm is proposed to provide more precise state-action values rather than using estimations. Moreover, the DRL-based training and execution procedures for the dynamic DRFSP are provided by combining decision-making, agent learning, and reconfigurable production. Training curves validate the reasonableness of system modeling and the good learning efficiency of the EDQN algorithm. Extensive comparison experiments show that our EDQN algorithm outperforms three popular DRL algorithms and four well-known priority dispatching rules (PDRs) by around 20 % and 89 % respectively, with good generalization ability and time efficiency. The average decision time under dynamics is only 0.65 ms, which enables real-time scheduling.

Efficient and privacy-preserving multi-agent systems for smart city carpooling with k-regret queries and differential privacy

Multi-Agent Systems are characterized by the presence of multiple independent agents and find diverse applications. In the context of smart cities, MAS is employed in traffic management to enhance operational efficiency, optimize resource utilization, and improve the quality of life for residents. This research paper focuses on the design of a multi-agent intelligent scheduling system, where passengers, vehicles, and carpooling platforms serve as intelligent agents. The primary objective of passengers is to identify suitable shared vehicles based on criteria such as waiting time, budget constraints, and willingness to carpool. Vehicles, on the other hand, organize their schedules based on passenger demands and designated routes. The carpooling platform takes into account resource allocation priority and optimization problems to ensure the efficient operation of the system. To address the issue of vehicle ordering, k-regret queries are utilized, while passenger preferences provide insight into determining loss factors. To safeguard privacy, differential privacy techniques and a random response mechanism are employed when dealing with multiple passenger queries. Furthermore, a direction-preserving insertion verification method is implemented to mitigate computational complexity. The effectiveness and efficiency of the proposed approach are validated through experimentation.

An Intelligent Scheduling System and Hybrid Optimization Algorithm for Ship Locks of the Three Gorges Hub on the Yangtze River

The ship lock system of the Three Gorges Hub(TGH) stands out as the most intricate navigation system for ships passing through dams, which is composed of the the world’s largest ship lock and ship lift. It operates with features like multi-dam configurations, multi-chambers, multi-operation modes and batch processing. This paper addresses the complex challenge of optimizing the lock schedule, ship entry timings, and ship placements within the lock chamber. By analyzing navigational rules, operational characteristics, and existing problems of TGH, this research introduces an intelligent scheduling system framework. With the incorporation of multiple objectives and constraints, a mixed-integer nonlinear programming(MINLP) model is formulated. Subsequently, a hybrid intelligent algorithm based on simulated annealing and heuristic search is constructed to achieve the optimization of the lock schedule. The algorithm efficacy is validated using the real-world data from TGH.

Precision Location-Aware and Intelligent Scheduling System for Monorail Transporters in Mountain Orchards

This study addressed the issue of the real-time monitoring and control of the transporter in a mountain orchard terrain characterized by varying topography, closed canopy, shade, and other environmental factors. This study involved independent research and the development of a series of electric monorail transporters. First, the application requirements of “Where is the monorail transporter?” were examined, and an accurate location-aware method based on high-frequency radio frequency identification (RFID) technology was proposed. In addition, a location-aware hardware system based on STM32 + RFID + LoRa was designed to determine the position of the monorail transporter on a rail. Second, regarding the application requirements of “Where is the monorail transporter going?”, a multimode control gateway system based on Raspberry Pi + LoRa + 5G was designed. An Android mobile terminal can obtain operational information about the transport plane in real time through the gateway system and remotely control its operation. The track-changing branch structure enables multimachine autonomous intelligent avoidance. Based on the experimental results of monorail transporter positioning in mountain orchards under various typical terrains, such as flat surfaces, turning paths, and uphill/downhill slopes, the road section average relative error of the 7ZDGS–250-type monorail transporter was 1.27% when the distance between benchmark positioning tags was set at 10 m on both flat and turning roads, and that of the 7ZDGS–300-type monorail transporter was 1.35% when the distance between benchmark positioning tags was set at 6 m uphill/downhill. The road section relative error of the 7ZDGS–250-type monorail transporter was 21.18%, and that of the 7ZDGS–300-type monorail transporter was 9.96%. In addition, the experimental results of monorail transporter communication control showed that the combination of the multimode control gateway control system and track-changing branch structure can achieve multimachine cooperation and autonomous avoidance function, ensuring that multiple monorail transporters can operate simultaneously without collision. The findings of this study establish the communication link of “monorail transporter-gateway system-control terminal” and form a precise positioning and real-time control scheme applicable to the operating environment of monorail transporters, thereby improving the intelligence and safety of mountain orchard monorail transporters.

Deep reinforcement learning for solving steelmaking-continuous casting scheduling problems under time-of-use tariffs

This paper proposes a novel intelligent scheduling method based on deep reinforcement learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) scheduling problem, under time-of-use (TOU) tariffs for the first time. The intelligent scheduling system architecture is designed, and a mathematical model is established to minimise the total sojourn time and electricity cost. To effectively reduce production costs by avoiding peak periods of electricity consumption, the ‘start time’ of the system is generated based on the Markov Decision Process (MDP), and heuristic scheduling rules related to power cost are used as the action space, with corresponding reward functions designed according to the characteristics of these two objectives. To satisfy the continuous casting which is a particular SCC constraint, a backward strategy is developed. Additionally, a branching duelling double deep Q-network (BD3QN) is adapted to guide action selection and avoid blind search in the iteration process, and then applied to real-time scheduling. Numerical experiments demonstrate that the proposed method outperforms comparison algorithms in terms of solution quality and CPU times by a large margin.

Research on smart mobility in public transportation and solutions for mobility

As an important part of modern urban transportation system, smart mobility and public transportation are directly related to people's travel convenience, transportation efficiency and sustainable urban development. With the continuous progress of science and technology, smart mobility solutions play an increasingly important role in the field of smart travel and public transportation. As an important support of urban transportation system, public transportation is of great significance in reducing traffic congestion, improving air quality and saving energy. However, traditional public transportation systems often face problems such as irrational route design, improper scheduling, and untimely information transfer. Therefore, the introduction of smart mobility solutions, such as intelligent scheduling systems and passenger information services, will bring new improvement and development opportunities for public transportation. The research significance of this thesis is to conduct an in-depth study on smart mobility solutions in the field of smart mobility and public transportation, so as to explore how to promote the upgrading and improvement of the transportation system at the technical, managerial and policy levels. This paper will focus on the integration of smart mobility and public transportation, exploring the synergies between the two to achieve a more efficient, environmentally friendly and convenient way of traveling.

Research on intelligent dispatching of micro‐grid according to optimized particle swarm algorithm

AbstractIn the process of grid connection of irregular output micro‐grid systems, it is of great research significance to make micro‐grid scheduling operate more effectively and economically. To minimize the environmental and total operating costs of the micro‐grid intelligent scheduling system during grid connection, this study proposes a micro‐grid intelligent scheduling model based on an optimized particle swarm optimization algorithm. The optimized particle swarm algorithm mainly improves the problem of the particle swarm algorithm being prone to local optima, making it reduce the possibility of local convergence. Comparative tests were conducted on the improved IPSO algorithm, and the results showed that the non‐dominated solution concentration pollution emissions obtained by the improved IPSO algorithm were at least 18326 Ib, which was lower than the comparison algorithm. In the empirical analysis of the intelligent scheduling model based on the improved IPSO algorithm, the convergence and satisfaction of the model were 0.0054 and 0.922, respectively, which were superior to the comparison algorithm. The above results indicate that the intelligent scheduling model for micro‐grid based on the improved IPSO algorithm has good economy and stability. Compared with traditional energy dispatch methods, this energy dispatch mode has higher economic benefits and is of great significance for promoting the development of micro‐grid.

Cloud–Edge Hybrid Computing Architecture for Large-Scale Scientific Facilities Augmented with an Intelligent Scheduling System

Synchrotron radiation sources are widely used in interdisciplinary research, generating an enormous amount of data while posing serious challenges to the storage, processing, and analysis capabilities of the large-scale scientific facilities worldwide. A flexible and scalable computing architecture, suitable for complex application scenarios, combined with efficient and intelligent scheduling strategies, plays a key role in addressing these issues. In this work, we present a novel cloud–edge hybrid intelligent system (CEHIS), which was architected, developed, and deployed by the Big Data Science Center (BDSC) at the Shanghai Synchrotron Radiation Facility (SSRF) and meets the computational needs of the large-scale scientific facilities. Our methodical simulations demonstrate that the CEHIS is more efficient and performs better than the cloud-based model. Here, we have applied a deep reinforcement learning approach to the task scheduling system, finding that it effectively reduces the total time required for the task completion. Our findings prove that the cloud–edge hybrid intelligent architectures are a viable solution to address the requirements and conditions of the modern synchrotron radiation facilities, further enhancing their data processing and analysis capabilities.

Design and Application of Vague Set Theory and Adaptive Grid Particle Swarm Optimization Algorithm in Resource Scheduling Optimization.

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

Selecting an Optimal Scenario for Addressing Supplier Selection Problem by Considering Sustainable Scheduling: A Hybrid Approach

The iron and steel industry is a pillar industry of the national economy in many countries and is also a source of high energy consumption and pollution gas emissions. In addition to the economic aspect, there have been increasing concerns over how to minimise the negative environmental impact and enhance the awareness of social responsibility for iron and steel enterprises. Therefore, this study proposes an intelligent scheduling system for addressing the supplier selection problem by considering sustainable scheduling (SS) (ISS-AFLCSS) to achieve maximised benefits of logistics costs, carbon emission and fatigued driving for the Chengsteel Company. In the ISS-AFLCSS, first, a multiobjective mathematical optimisation model is formulated. Second, this study proposed a hybrid approach using an improved genetic algorithm (GA) to optimise multiple objectives of scenarios and adopting the technique for order preference by similarity to an ideal solution (TOPSIS) method with the analytic hierarchy process (AHP) to precisely optimise and select a best-ideal scenario. The results confirm that the proposed ISS-AFLCSS can provide accurate guidance in practicing SS for managers of enterprises.

Research on Dynamic Scheduling Model of Plant Protection UAV Based on Levy Simulated Annealing Algorithm

The plant protection unmanned aerial vehicle (UAV) scheduling model is of great significance to improve the operation income of UAV plant protection teams and ensure the quality of the operation. The simulated annealing algorithm (SA) is often used in the optimization solution of scheduling models, but the SA algorithm has the disadvantages of easily falling into local optimum and slow convergence speed. In addition, the current research on the UAV scheduling model for plant protection is mainly oriented to static scenarios. In the actual operation process, the UAV plant protection team often faces unexpected situations, such as new orders and changes in transfer path costs. The static model cannot adapt to such emergencies. In order to solve the above problems, this paper proposes to use the Levi distribution method to improve the simulated annealing algorithm, and it proposes a dynamic scheduling model driven by unexpected events, such as new orders and transfer path changes. Order sorting takes into account such factors as the UAV plant protection team’s operating income, order time window, and job urgency, and prioritizes job orders. In the aspect of order allocation and solution, this paper proposes a Levy annealing algorithm (Levy-SA) to solve the scheduling strategy of plant protection UAVs in order to solve the problem that the traditional SA is easy to fall into local optimum and the convergence speed is slow. This paper takes the plant protection operation scenario of “one spray and three defenses” for wheat in Nanjing City, Jiangsu Province, as an example, to test the plant protection UAV scheduling model under the dynamic conditions of new orders and changes in transfer costs. The results show that the plant protection UAV dynamic scheduling model proposed in this paper can meet the needs of plant protection UAV scheduling operations in static and dynamic scenarios. Compared with SA and greedy best first search algorithm (GBFS), the proposed Levy-SA has better performance in static and dynamic programming scenarios. It has more advantages in terms of man-machine adjustment distance and total operation time. This research can provide a scientific basis for the dynamic scheduling and decision analysis of plant protection UAVs, and provide a reference for the development of an agricultural machinery intelligent scheduling system.

An intelligent green scheduling system for sustainable cold chain logistics

This study proposes an intelligent green scheduling system for cold chain logistics (IGSS-CCL) to support the integration and coordination of resources. Post-COVID-19, the traditional cold product market is rapidly converting to retail stores and e-commerce portals owing to social distancing restrictions, which creates a requirement and opportunities for the development of cold chain logistics. However, urban governance requirements, such as pandemic prevention, traffic restriction, energy conservation, and emissions reduction, have added challenges to this development. Therefore, it is vital to design a cold chain logistics scheduling system that considers the economic, safety, and environmental factors. The proposed system includes three parts: (1) the framework structure of the cold chain logistics intelligent scheduling system; (2) a multi-objective scheduling optimization model to allow for efficient and dynamic coordination between the distribution, demand, and external environment; and (3) a two-stage optimization algorithm based on Dijkstra's algorithm and a non-dominated sorting genetic algorithm to support intelligent scheduling operations. Numerical experiments were conducted to analyze the performance of the proposed system and demonstrate its application. The results highlight that multi-objective tactical optimization in the IGSS-CCL is conducive to saving resources, protecting the environment, and promoting the sustainable development of cold chain logistics, which remains ahead of the traditional single-objective optimization method. Managers can use the suggested IGSS-CCL as a decision-support tool to control and supervise the scheduling operations of cold chain logistics.

Resource Operation Intelligent Scheduling Algorithm and System Based on Integrated 5G Public Private Network Communication Network

With the development of the economy, users’ demands for communication systems continue to increase. At the same time, due to the development of technologies such as the Internet of Things, big data, and cloud computing, Internet users’ requirements for communication systems are also increasing. Network application scenarios such as in-vehicle networking, transmission of ultra-high-definition video, and real-time data transmission are emerging one after another. The breakthrough development of 5G communication technology has opened a chapter in the 5G era and has become the key to solving the problem of cellular communication technology. However, in the architecture of advanced networks, it is still a big problem to solve the problem of load balancing and realize the reasonable scheduling of network resources. At the same time, it has also become a hot spot of current research. This paper studies the resource allocation problem in the 5G communication network. Based on the analysis of its communication principle, the communication simulation system in the corresponding scenario is built and improved. At the same time, in order to solve the resource allocation under the new SDN architecture in the core network To solve the problem, a slice resource allocation scheme based on reinforcement learning is designed, and a slice resource allocation algorithm based on DQN (Deep Q-Network) is proposed. By testing the system functions, the superiority of the proposed algorithm in relevant scenarios is verified.

Research on the Education and Design policy of bio economic and management facts of the intelligent test based on fuzzy particle swarm optimization algorithm

with the passage of time, the era of technology is coming, and the huge demand of this modern world is the best education of the people in order to survive in the fast-developing era. Education system of any country holds great importance in the development of the country economy. Therefore, many developing and under developing countries has been focusing on the development of the education system in their country to improve their country’s economy as well as meet the requirements of the modern world. Apart from this, this allows organizations exceeds several universal standards, particularly in terms of educational fulfilment and higher education system exposition. moreover, there seems significant problems that policies makers must address, such as a low retention rate in blog academic achievement, universities and school improvement, a significant achievement gap between poor class, education funding, social classes, etc., this research focus on the management of the educational policies and intelligent test scheduling system based on fuzzy swarm particle optimized algorithm involving the business sector and educational program changes. Moreover, for the progress of the education system there is essential need to reduce errors rate in the system. this study explains the research on the design and management of the intelligent test scheduling system. Aiming at the problem of high error rate in the traditional intelligent test scheduling system of higher education institutions, an intelligent test scheduling system of higher education institutions based on fuzzy particle swarm optimization algorithm is designed. In terms of hardware, x86 server is designed. In terms of software, standardize the business process of intelligent arrangement in colleges. The fitness function is established based on fuzzy particle swarm optimization algorithm, and the intelligent test scheduling time selection operator is dynamically planned. This paper analyzes the data flow of intelligent test scheduling in colleges, constructs a database, and comprehensively considers the conflict factors of test scheduling. In the case of no missing candidates, the intelligent test scheduling of higher education institutions is realized, and the system design is completed. For this purpose, we collected sample data from various educational institutions of China and investigated collected data by utilizing optimized particle swarm algorithm. The experimental results show that the test scheduling error rate of the designed system is significantly lower than that of the traditional test scheduling systems 1, 2 and 3, which can solve the problem of high-test scheduling error rate of the traditional intelligent test scheduling system in colleges.

Research and implementation of high priority scheduling algorithm based on intelligent storage of power materials

The Smart Supply Chain technology has been gradually established and improved as a popular power system over different application fields, which raises the development of informatization and intelligence in intelligent warehouse management. Intelligent scheduling systems are eagerly required to standardize warehouse operation management and intelligently schedule inbound and outbound tasks, maximizing the rational use of intelligent devices and realizing the automation unmanned operations in actual scheduling situations. Since the scheduling algorithm is the key component of the intelligent scheduling system that acts as the most powerful tool adding automation and intelligence to electric power material storage, this paper studies the principle of the high-priority based scheduling algorithm carefully and proposes a novel data model based on the algorithm to improve the execution efficiency and performance of intelligent scheduling system. The superiority of the proposed algorithm is verified by its principle analysis and comparing with other state-of-the-art methods, ensuring that our algorithm is perfectly applicable in actual scheduling systems. Additionally, we design an efficiency scheduling system satisfying the needs of intelligent warehouse management, and further demonstrate our algorithm works well in realizing the automatic loading and unloading operation in the storage of electric power materials under unmanned condition with simulation experiments.

Operating Room Time Prediction: An Application of Latent Class Analysis and Machine Learning

Objective: The objective of this work is to build a prediction model for Operating Room Time (ORT) to be used in an intelligent scheduling system. This prediction is a complex exercise due to its high variability and multiple influential variables. Materials and methods: We assessed a new strategy using Latent Class Analysis (LCA) and clustering methods to identify subgroups of procedures and surgeries that are combined with prediction models to improve ORT estimates. Three tree-based models are assessed, Classification and Regression Trees (CART), Conditional Random Forest (CFOREST) and Gradient Boosting Machine (GBM), under two scenarios: (i) basic dataset of predictors and (ii) complete dataset with binary procedures. To evaluate the model, we use a test dataset and a training dataset to tune parameters. Results and discussion: The best results are obtained with GBM model using the complete dataset and the grouping variables, with an operational accuracy of 57.3% in the test set. Conclusion: The results indicate the GBM model outperforms other models and it improves with the inclusion of the procedures as binary variables and the addition of the grouping variables obtained with LCA and hierarchical clustering that perform the identification of homogeneous groups of procedures and surgeries.